An application test of the crop calendar portion of a corn (maize) stress indicator model developed by the early warning, crop condition assessment component of AgRISTARS was performed over the corn for grain producing regions of the U.S.S.R. during the 1980 crop year using real data. Performance of the crop calendar submodel was favorable; efficiency gains in meteorological data analysis time were on a magnitude of 85 to 90 percent

Ashburn, P.

Taylor, T. W.

English

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EW-U 1-04074
JSC-17130
APR <; 2 1981
A Joint Program for
Agriculture and
Resources Inventory
Surveys Through
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March 1981
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LARGE AREA APPLICATION OF A CORN HAZARD MODELI
3 .Pat Ashburn and T. W. Taylor
U.S. Department of Agriculture
FAS, CP, CCAD and SEA-AR
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Lyndon B. Johnson Space Center
Houston Texas 77058
EW-U1-04074
JSC-17130
LARGE AREA APPLICATION OF A CORN HAZARD MODEL
PRINCIPAL INVESTIGATORS
Pat Ashburn and T. W. Taylor
APPROVED BY
Jai6es R. Hickman, Director
	 Glennis 0. Boatwright, Ma
	 er
,.6r p Condition Assessment Div.
	 Early Warning/CCA Project
AgRISTARS Program
Earth Observation Division
Space and Life Science Directorate
NATIONAL AERONAUTICS AND SPACE ADMINISTRATION
LYNDON B. JOHNSON SPACE CENTER
r
	 Houston, Texas
March 1981
CORN HAZARD MODEL
	
FIRST ISSUE
A large area operational application of the Corn Hazard Model using
real time data and evaluation of results.
1. REASON FOR ISSUANCE
Documents the first large area application of a Corn Hazard Model
developed by T. W. Taylor and Francis W. Ravet, Early Warning and
Crop Condition Assessment component of AgRISTARS. Large area
application was conducted by CCAD with the support of EW/CCA
AgRISTARS.
2. COVERAGE
This Technical Memorandum evaluates the operational application of
the Corn Hazard Model, by CLAD, over the major corn for grain
producing regions of the USSR during the 1980 growing season.
3. ACKNOWLEDGMENTS
Dr. T. W. Taylor and Francis W. Ravet, EW/CCA AgRISTARS for
developing and documenting the model.
4. PREPARED BY-
.1_
March 1981
Pat Ashburn, FAS, CP, CCAD
	
Date
March 1981
Dr. T. W. faylor, SE
	
EW/CCA AgRISTARS
	
Date
PART	 TM-	 PAR.
TABLE OF CONTENTS
Page No.
PART 1.0	 INTRODUCTION
	
1.1	 Summary and Conclusions
	
1.1	 Background
	
1.3	 Purpose
	
1.4	 Data Set
FIGURE 1-1 - Corn for Grain Producing
Areas in the Soviet Union
PART 2.0 PERFORMANCE EVALUATION
	
2.1	 Efficiency
TABLE 2-1	 Estimated and Reported
Crop Calendar Dates for
the Soviet Union
	
2.2	 Accuracy
	
2.3	 Summary
EXHIBITS
	
No. of Pages
1	 References
APPENDIX
	
No. of Pages
*	 A Description of Corn Hazard Model
PART	 TM-	 PAR.
PART 1.0
INTRODUCTION
1.1 SUMMARY AND CONCLUSIONS
In an effort to improve crop condition analysis capabilities used by the
Crop Condition Assessment Division (CLAD) of the Foreign Agricultural
Service (FAS) ; new and/or improved agricultural models are continuously
assessed and tested over large operational areas for possible future
integration into the operational system of CCAD. The Corn Hazard Model
(CHM) developed and tested by the Early Warning/Crop Condition
Assessment (EW/CCA) component of AgRISTARS is such a model. This hazard
model which also estimates a Corr crop calendar was run using opera-
tional data over the corn- fo­g-ain producing regions of the Soviet
Union. The model uses primary World Meteorological Organization (WMO)
meteorological data elements, such as temperature and precipitation.
Soils data interacts with the model through the use of soil moisture
calculations. The model provides reports on geographic areas that are
analyzed by the country analyst; thereby reducing the comprehensive
analysis of many primary data elements.
An application test of the crop calendar portior of this hazard model
was performed over the corn-for-grain producing regions of the Soviet
Union during the 1980 crop year using real data. Success of the model
was measured in terms of efficiency, accuracy, objectivity, repeatabil-
ity, and continuity, with efficiency and accuracy receiving the highest
considerations. Only the crop calendar portion was evaluated. Add-
itional analysis of the hazard indicators will follow as time permits.
The general performance of the model as operated over the test area was
very favorable. Efficiency gains in meteorological data analysis time
were 85 t, 90 percent.
Model results combined with Landsat data analysis were used to estimate
the area and condition of the corn-for-grain in the Soviet Union for
1980. The results were in line with the recent area estimates and
conditions provided by the U.S. Agricultural Counselor in the Soviet
Union and with Soviet newspaper and radio accounts.
The Crop Condition Assessment Division (CCAD) will continue to use the
Corn Hazard Model in the USSR and further evaluate its performance. We
will also provide feedback to the Early Warning component of AgRISTARS
on the results of our use of the model.
1.2 BACKGROUND
The Crop Condition Assessment Division (CCAD) of the Foreign
Agricultural Service (FAS) is responsible for verifying and assessing
1-1
 
 
 
 
 
 
"Page missing from available version"
PART	 TM-
	
PAR.
1.4' DATA SET
The primary corn-for-grain producing area of the USSR was selected as
the test area for evaluation of the CHM. Temperature and precipitation
data were extracted from World Meteorolgogical Organization (WMO)
station data. Planting dates were extracted from radio and newspaper
reports from the USSR. Soil moisture was derived from a soil moisture
model that was run using WMO data and soil type for each WMO station for
each day of data. In total 15 stations were used to evaluate the model.
Verification data were limited to FAS Agricultural Attache reports,
official USSR reports, Soviet radio and newspaper accounts, and Landsat
data.	 -
1-3
PART 2.0	 TM-	 PAR.
PART 2.0
PERFORMANCE EVALUATION
2.1	 EFFICIENCY
The general performance of the CHM operated over the USSR during 1980
was favorable. Efficiency gains in meteorological data analysis time
were significant. Estimates of savings in time were on a magnitude of
85 to 90 percent.
The model evaluated a larger number of stations; thus, it significantly
improved the analysis of the corn condition.
2.2 ACCURACY
Evaluating the accuracy of the CHM results were difficult due to a lack
of confirmed ground observations. Accuracy evaluation was assessed
using U.S. Agricultural Attache reports sent from the Soviet Union,
Soviet newspaper and radio reports, and analysis of Landsat data.
Nine separate WMO stations (Figure 1) were selected across the grain
producing areas. These stations are located to provide a full range of
weather conditions experienced during 1980. They are also located to
provide the full range of geographic conditions experienced in the major
grain producing regions in the USSR. The CHM was given planting dates
provided by CCAD analyst. These dates were provided by reports from the
USSR. Where no planting dates were available, they were spread from
other reporting stations.
A significant number of stations reported growth stages in a 10 day
range. This is due to the type of reports from which the information
was taken. These are decade reports published for each 10 day period.
The CHM was considered successful when the growth stage was no more than
3 days outside of this range.
Grain is harvested in the USSR sometime after the beginning of the dent
stage. The harvest is related more to weather conditions, lateness of
the season, or a need to clear the field. Consequently, accurate growth
stage reporting is clouded during the end of the year.
The hazard output of the CHM is difficult to evaluate due to a lack of
good ground truth data. Without this, the results must be checked over
a long period of time - probably more than one year. The hazard portion
of the model is presently considered successful if it alarms an area
when other information sources are indicating an alarm should be
announced. For the 1980 data, the model seems to be setting flags where
they should be set. However, additional information and time are
required to adequately evaluate the hazard portion of the model.
2-1
PART 2.0
	
TM-
	
PAR.
The crop calendar portion of the CHM was highly successful in the USSR
for 1980. The results shown in Table 1 were very encouraging. These
indicated the model accurately operated over a very wide geographic area
and over a wide range of very different weather conditions..
2.3 SUMMARY
The crop calendar component of a corn stress indicator model was
evaluated using 1980 real time data for the corn-for-grain producing
regions of the Soviet Union. Model success was measured in terms of
efficiency, accuracy, repeatability and continuity. Verification data
were FAS Agricultural Attache reports, official USSR reports, Soviet
radio and newspaper accounts, and Landsat imagery. Use of the model
resulted in a time saving for meteorological data analysis of 85 to 90
percent. A description of the model logic and components is included.
2-2
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TM-	 PAR.
REFERENCES
1. Aaronson, A.C. and Ashburn, P., The Large Area Operational Application of
the Winterkill Model Using Real Time Data and Evaluation Results.
USDA-FAS Technical Memorandum No. 13, Houston, Texas, 1980.
2. Brown, D.M., Heat Units for Corn in Southern Ontario, No. 75-007 Ministry
of Agriculture and Food, Ontario, Canada.
3. Hanway, J. J., How a Corn Plant Devwtops. Agron. J. 55:487-492, 1971.
4. Ravet, F. W. and J. R. Hickman, A Meteorologically Driven Wheat Stress
Indicator Model. USDA-FAS Technical Memorandum No. 8, Houston, Texas,
1919.
2-3
APPENDIX A
DESCRIPTION OF CORN STRESS INDICATOR MODEL
PURPOSF.
The purpose of this section is to document a corn hazard model that detects
plant stress due to moisture deficiency and adverse temperatures. A brief
synopsis of the climatic stress thresholds for corn tmaize) at different
growth stages is also given.
MAJOR VARIABLES
The degree of stress is dependent on three variables - phonological growth
stage, available soil moisture and temperature.
PHENOLOGICAL GROWTH STAGE
The Hanway Growth Stage (HGS) system (Hanway, 1963) was used in the model as
defined below:
HGS	 PHENOLOGICAL STAGE
0.0 Emergence
1.0 4 Leaves
2.0 8 Leaves
3.0 12 Leaves
4.0 16 Leaves
5.0 Silk-tassel
6.0 Blister Kernel
7.0 Dough
8.0 Begin Dent
9.0 Full Dent
10.0 Physiological Maturity
During each stage optimum and stress conditions exist. Most of these
conditions are directly related to meteorological factors. Stress was
defined in this model version as those factors considered to most affect the
maize growth cycle and for which input data are presently available to CCAD.
Problem and optimal conditions that form the model logic are presented by
growth stags in Table 1.
STRESS MODEL COMPONENTS
The stress model has 3 central components - a hazard model, a crop calendar
model and a soil water budget model. Thee latter 2 models collectively
require daily meteorological data - maxirunu and minimum temperature and
precipitation. The phonology-based hazard routine contains.the stress
definitions and thresholds- The crop calendar is a fixed-increment
degree-day model developed by EW/CCAD that requires an actual or estimated
planting date. Degree-day summations are variety specific. A two-layer
soil moisture model as modified by Ravet and Hickman (1979) is employed to
track the amount of plant-available soil water.
011
A-1
DEGREE-DAY CALCULATION
Temperature is a regulator of maize growth and development. Most efforts to
predict the timetable of maize development have used a heat-unit approach;
the most common is the degree-day. The Brown Aethod (1975) of determining
degree days is based on the physiological response of plants to temperature
and is determined as follows:
DD a ;Ymax + Ymin) /2
where Ymax • (Tmax - 10 0C)*(3.33-0.084*(Tmax - 10 001 and Ymin a 1.8 *(Turin
- 4.44°C). When Tmax is less than or equal to 1$°C the value of Ymax is
zero and when Tmin is I-es than or Squal to 4.44 C the value gf Ymin is
zero. When 'Tmax is greater than 32 C the value of Tmax is 32 C.
The values of the accumulated degree-days for the most common Soviet Union
variety arse: 110 to emergence, 420 to HGS 1.U ► 775 to 1.5, 120 to 4.75,
1730 to 6.5, 2080 to 8.0 and 1565 to 10.0.
MODEL PARAMETERS AND OUTPUTS
The model identifies three environmental conditions - optimum, adequate and
hazardous. Hazardous conditions include:
(a) Insufficient pre-season stored sail moisture
(b) Planting/harvest delay (tractability problems)
(c) Poor germination
(d) Poor emergence
(e) Adverse growing season soil moisture and temperature
(excessive/deficient, phenology-bated)
The stress indicator model determines the possibility of maize stress
based on temperature and moisture conditions (see Table i). The stress and
optimal growth conditions are recorded for sch growth stage as well as the
time the plant remained in these stages. From this information the analyst
can judge the degree of damage or stress occurring at a growth stage and
then determine the overall effect on crop development. The model does not
predict events nor does it attempt to assess the impact of stress; it pro-
vides information that indicates conditions occurring within a predescribed
geographic area. The output from the model is a record of each day that a
stress condition has occurred, the reason for the stress and the crop growth
stage. At the completion of processing data for a given meteorological
station, the data are summarized giving the total days for development, and
the number of optimum and hazardous growth days.
A-2
V uJ a a aJ
EW/CCA DISTRIBUTION	 ^,^,,; 1
	
a
AQRISTARS Level 1
W. Kibler (USDA)
P. Thome (NASA)
T. Potter (USDC)
F. Doyle (USDI)
AQRISTARS Level 2
C. Caudill (USDA)
W. Rice (NASA)
L. Hyatt (USDC)
A. Watkins (USDI)
A¢RIST:k'tS Proiect Managers
G. Boatwright (30
R. Ambroziak (3)'
J. Erickson (3)
R. Gilbert (3)
R. Allen (3)
P. Weber (3)
R. MacDonald (3)
J. Ritchie (3)
PSS, AgRISTARS
F. Barrett	 (2)
M. Helfert
W. Stephenson
D. Conte
R. Hatch
USDA
R. Reginato, Tempe, AZ (4)
H. Gausman, Weslaco, TX (5)
T. Engman, Beltsville, MD (2)
B. Stewart, Bushland, TX (5)
J. Quisenberry, Lubbock,TX(2)
D. Smika, Akron, CO (2)
A. Black, Mandan, ND (4)
K. Aase, Sidney, MT (5)
R. Johnston, College
Station, TX (4)
USDC /NO..A
D. Wiesnet
	
(3)
D. Tarpley
	
(3)
USDA
G. Hart
J. Hickman
	
(5)
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Large area application of a corn hazard model

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