A model was developed for predicting the day 50 percent of the wheat crop is planted in North Dakota. This model incorporates location as an independent variable. The Julian date when 50 percent of the crop was planted for the nine divisions of North Dakota for seven years was regressed on the 49 variables through the step-down multiple regression procedure. This procedure begins with all of the independent variables and sequentially removes variables that are below a predetermined level of significance after each step. The prediction equation was tested on daily data. The accuracy of the model is considered satisfactory for finding the historic dates on which to initiate yield prediction model. Growth prediction models were also developed for spring wheat

Haun, J. R.

English

NASA Technical Reports Server

MONTHLY PROGRESS REPORT
January 6, 1975
Contract NAS 9-14006
Period covered by report - December 1, 1974 to January 1, 19
CROP STATUS EVALUATIONS AND YIELD PREDICTI S
Principal Investigator: J. R. Haun .
Department of Horticulture ,
Clemson University, Clemson, S. C. ,2_'
A. Significant activities and results -
1. A model for predicting the day 50% of the wheat crop is planted in North
Dakota has been developed. This model incorporates location (LOC) as another
independent variable. The addition of this variable with its transformations brought
the total number of independent variables to 49. The Julian date when 50% of the crop
was planted for the 9 divisions of North Dakota for 7 years was then regressed on the 49
variables through the step-down multiple regression procedure. This procedure begins
with all of the independent variables and sequentially removes variables that are below
a predetermined level of significance after each step. The Julian date of 50% planted
for the years 1967-72, 74 was obtained as explained in the July 8 and August 5 reports.
The validity of this procedure was confirmed by a recent USDA report, N. D. Wheat
Historic Estimates 1955-1970, Ag. Statistics No. 33. 1973 data was omitted for use in
testing the model. 1974 data were used in the analysis since the year was very atypical
and therefore broadened the scope of data for the analysis. The following basic
independent variables were used: running three, six, and nine day sums of average
minimum and maximum temperature values (CO) (N3S, N6S, N9S, X3S, X6S, X95),
estimated soil moisture (EO in %), preseason precipitation (PP in cm), and location(LOC).
The basic variables were used as additional independent variables in the form of the
followingtransformations: square -and cube; -and cross-products of all basic variables.
Table 1 shows the statistics for the final step of the anlaysis, with Fig. 1 showing the
relationship of the predicted values to the actual values.
The prediction equation (Predicted planting date - 136.7 +0.0055 (EO X N9S) - etc.)
was tested on daily data not used in the analysis for 1973. An example of the predicted
values for one division (represented by dots) is presented in Fig. 2. The curved line is
the line of best fit (considering only 1st and 2nd order equations) through the points.
The straight line (450) passes through points where actual and predicted values are equal.
The objective of the procedure is to find the date where the value predicted by the equation
is equal to the actual Julian date. Thus, the point where the regression line crosses the
450 line is chosen as the predicted planting date. For the Northwest division of North
Dakota, the actual date was missed by 8 days using this method. Results for all divisions
are as follows:
(NASA-CR-141277) CROP STATUS EVALUATIONS N75-15130
AND YIELD PREDICTIONS Monthly Progress
Report, 1 Dec. 1974 - 1 Jan. 1975 (Clemson
Univ.) 10 p HC $3.25 CSCL 02B UnclasG3/43 06963
https://ntrs.nasa.gov/search.jsp?R=19750007058 2020-03-23T01:28:29+00:00Z
2Predicted Actual
Division Julian Date Julian Date
Northwest 123 131
North Central 138 133
Northeast 124 127
West Central 116 124
Central 138 118
East Central 124 111
Southwest 126 126
South Central 100 116
Southeast 126 112
Avg. 124 122
The accuracy of this model is considered satisfactory for the purpose of finding
the historic dates (i.e. years when no USDA reports were available) on which to initiate
our yield prediction model.
2. Growth rate prediction models have been developed for spring wheat for the
first two weeks of the 1974 season. These were developed from analysis of 1974 data
from Dickinson, Williston (2 locations), Minot, North Dakota and Clemson, South
Carolina. Since observations were not begun on the exact date of emergence at each
location, it was necessary to establish a stage of development in common for all locations
on which to base the subsequent weekly increments of data for analysis. The stage 2.4
was used as the point in common to represent the end of the first week of development.
Successive 7-day increments will be used for the subsequent weekly analyses. Each
analysis will combine the current week's data with all previous weeks.
Growth rates were regressed on the following independent variables: maximum-
air temperature (Co), minimum air temperature (CO), precipitation (cm.), estimated
soil moisture (100% by Thornthwaite method), solar radiation (Langleys); at 0, 1, 2,
and 3 day lag periods; squared, cubed; and 45 selected cross products of the basic lagged
variables; for a total of 93, by the step-up multiple regression procedure. This procedure
selects the most highly correlated individual independent variable in the first step, and
in successive steps variables are added or removed to maintain significance at a designated
level (considering all possible combinations). Tables 2, 3, 4, and 5 present statistics
of the steps selected for use as growth prediction models.
B. Overall status and problem areas -
Development of prediction models for spring wheat should be completed shortly
and then the models will be subjected to tests on domestic data. Several inquiries have
been made without success to obtain weather and yield data on the foreign countries for
which there is interest in trial predictions. We have on hand a limited amount of Canadian
and USSR data which may be used. Unless a source is found soon for data from China,
Argentina, Australia and India it is doubtful that tests will be accomplished on these
countries before April 1, 1975.
3C. Expected accomplishments during January 1975 -
Analyses will continue on North Dakota spring wheat data and the resulting
prediction equations will be applied to test data.
D. Recommendations and summary outlook for future work -
No new recommendations at this time.
E. Travel summary and plans -
A work conference with NASA personnel and other collaborating research parties
is scheduled at JSC the week of January 23.
4Table 1. Analysis of variance, regression coefficients, and statistics of fit for the
dependent variable, day of 50% planted in North Dakota, 1967-72, 1974.
Source df MS F Prob> F R2
Regression 4 1918.5994 59.7372 0.0001 0.8129
Error 55 32.1173
Corrected total 59
Partial regression Student's t Prob.>Itl
coefficients for HO:B = 0
I ntercept 136.6887 54.99 0.0001
Product of EO and N9S 0.0055 12.84 0.0001
Product of LOC and X9S -0.1177 -7.54 0.0001
Product of LOC and X3S 0.2777 6.24 0.0001
Product of PP and X3S -0.0138 -4.71 0.0001
N9S - Running nine day sum of average minimum temperature values (CO)
X3S - Running three day sum of average maximum temperature values (CO)
X9S - Running nine day sum of average maximum temperature values (CO)
LOC - Location (north, central, south)
PP - Preseason precipitation
EO -Estimated soil moisture
5160
I I-
I IIA I
0
a
150
Z
z-0
140
-o0
a 120
110
10A0
110 120 130 140 150 160
Day (of year) 50% of Wheat Planted in North Dakota
Fig. 1. Relationship of predicted date on which 50% of wheat was planted to actual
date for the period 1967-72, 1974, based on equation from Table 1.
160
3 140
W
UU
420
90 100 110 120 13 130 14150160
April May June
- I
Fig 2. Predicted planting date (PPLDATE) model applied to all dates from April 1, to June 15 In 1973 for
the Northwest'Division of North Dakota.
7Table 2. Analysis of variance, regression coefficients, and statistics of fit for the
dependent variable, growth rate of spring wheat 1974, one week (with Langleys)
Source df MS F Prob.> F R2
Regression 5 '0.005458 18.252 0.0001 0.843
Error 17 0.000299
Corrected total 22
Partial regression Student's t Prob.> t
coefficients for HO:B = 0
Intercept 6.971 X 10-2 3.784 0.0015
TN0 5.575 X 10-3 5.108 0.0001
P1 7.630 X 10-2 3.537 0.0025
PSE02 
-8.460 X 10-6 -3.102 0.0065
LSE02/1000 3. 262 X 10-5 7.601 0.0001
L3E02/1000 
-6.099 X 10-5 -6.389 0.0001
The first number following each basic variable indicates the lag period. The second
number indicates that the variable is squared (2) or cubed(3). Two variables combined
indicate a cross product.
PS - sum of PO, P1, P2, and P3
LS - sum of L1, L2, and L3
8Table 3. Analysis of variance, regression coefficients, and statistics of fit for the
dependent variable, growth rate of spring wheat 1974, one week (without Langleys)
Source . df MS F Prob.> F R2
Regress'on 8 - 0.003593 13.862 0.0001 0.888
Error 14 0.000259
Corrected total 22
Partial regression Student's t Prob.>jIt
coefficients for HO:B = 0
Intercept 
-2.252 X 10-1 -2.44 0.0285
TX2 1.719 X 10-2 6.01 0.0001
TX3 1. 172 X 10- 2  -7.02 0.0001
TX12 
-3.526 X 10- 4  -5.44 0.0001
TX22 1.345 X 10- 4  3.69 0.0024
TNO 9.307 X 10- 3  7.17 0.0001
P02 
-5.701 X 10-1 
-4.46 0.0005
E3. 5.340 X 10-3  3.23 0.0060
E13/1000 2.332 X 10- 4  -2.49 0.0259
The first number following each basic variable indicates the lag period. The second
number indicates that the variable is squared (2) or cubed(3) . Two variables combined
indicate a cross product.
9Table 4. Analysis of variance, regression coefficients, and statistics of fit f6r, the
dependent variable, growth rate of spring wheat 1974, two weeks (with Langleys)
Source df MS F Prob.> F R2
Regression 9 0.01587 25.983 0.0001 0.839
Error 45 0.00061
Corrected total 54
Partial regression Student's t Prob.>ltl
coefficients for HO:B = 0
Intercept 6.017 X 10-2 2.61 0.0123
TX33/1000 
-2.573 X 10- 3  -4.62 0.0001
TXOPO 1.638 X 10-3 4.61 0.0001
TXOLI 4.660 X 10-6 4.48 0.0001
TX2EO 8.180 X 10- 5  6.31 0.0001
TNOP2 2.280 X 10-2 9.21 0.0001
TN1P2 4.464 X 10-2 -12.28 0.0001
P33/1000 3.971 X 100 -3.39 0.0015
P1LI 
-6.300 X 10- 5  
-3.43 0.0013
P2L1 5.605 X 10-4 10.66 0.0001
The first number following each basic variable indicates the lag period. The second number
indicates that the variable is squared (2) or cubed (3). Two variables combined indicate
a cross product.
10
Table 5. Analysis of variance, regression coefficients, and statistics of fit for the
dependent variable, growth rate of spring wheat 1974, two weeks (without Langleys)
Source df MS F Prob.> F R
Regression 10 0.01354 23.013 0.0001 0.839
Error 44 0.00059
Corrected total 54
Partial regression Student's t Prob.> tIJ
coefficients for HO:B = 0
intercept 2.334 X 10- 1  4.42 0.0001
El 
-2.394 X 10- 3  -2.53 0.0150
E03/1000 2.223 X 10- 4  3.93 0.0003
P22 
-8.396 X 10-2 -8.92 0.0001
'P33,'1000 
-5.747 X 100 
-4.37 0.0001
PIEO 
-3.145 X 10- 4  -3.29 0.0020
TX33/1000 
-2.270 X 10- 3  -3.91 0.0003
TXOP2 2.089 X 10- 2 10.22 0.0001
TXOTX2 1.708 X 10- 4  6.47 0.0001
TNIPO 
-5.762 X 10- 3  -5. 13 0.0001
TN1P2 
-3.392 X 10- 2  -9.52 0.0001
The first number following each basic variable indicates the lag period. The second
number indicates that the variable is squared (2) or cubed (3). Two variables combined
indicate a cross product.


Crop status evaluations and yield predictions

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