-------
Table'B-S. Change in U.S. crop acreages, scenario 1,
O>
Crop
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Thousand acres
Corn
Grain sorghum
Barley
Oata
Wheat
Soybeans
Cotton
All hay
Fallow
Total
Commodity
Corn
Grain sorghum
Barley
Oats
Wheat
Soybeans
Cotton
All hay
Cottonseed
Cottonseed meal
Cottonseed oil
Soybean meal
Soybean oil
Beef
Pork
Chicken
Milk
Veal
21.92
2.54
.31
-.07
-.70
-26.83
-1.21
-.67
-.04
-4.82
1987
-.000
-.000
.OOO
.000
.000
.012
.000
.004
.225
.112
.000
.080
.001
.000
.000
.OOO
.000
.000
-5.86
.12
-10.04
-16.87
-3.97
-23.25
-2.71
9.52
-3.19
-59.42
Table B-6
1988
.000
.000
.002
.009
.OOO
.015
.000
-.049
.263
. 117
.000
.081
.002
.OOO
.000
.000
. 000
.000
-23.73
-2.28
-16.27
-17.16
-4.0V
-8.24
-5.35
8.48
-3.84
-76.28 -
Change
1989
.002
.000
.004
.015
.000
.009
.001
-.062
.209
. 104
.OOO
.033
.000
.000
.000
.000
.000
.000
-43.82
-3.48
-22.46
-16.28
-6.07
4.23
-9.20
-1.13
-6.29
110.79
-45.72
-2.75
-2S.41
-21.19
-7.49
3.87
-12. 13
-2.73
-6.38
-126.32
-45.12
-2.29
-29.02
-25.53
-11.35
-.11
-13.20
-4.48
-8.78
-148.68
-47.36
-2.54
-20.11
-28.51
-23.26
-.94
-12.22
-5.10
-9.09
-158.25
-51.27
-3.48
-12*. 85
-33.11
-33.87
-1 .54
-9.89
-7.19
-10.18
-173.58
-51.68
-3.70
-10.49
-36. 18
-39.75
-3.05
-7.42
-8. 10
-1 1 .08
-182.51
-42.83
-4.09
-6.89
-38.58
-47.00
-12.90
-5.61
-9.31
-11.99
-191.20
In commodity prices, scenario 1.
1990
.004
.002
.008
.013
.000
.004
.001
.004
.163
.098
.000
- .000
.000
.000
.000
.000
.000
.000
1991
.005
.003
.013
.014
.000
.003
.002
.043
.138
.087
.000
-.017
.OOO
.000
.000
.000
.000
.000
1992
.004
.003
.018
.016
.000
.003
.002
.068
.128
.080
.000
-.015
.000
-.000
.000
.OOO
.000
-.001
1993
.004
.003
.019
.017
.002
.003
.001
. OB4
.111
.070
.OOO
-.009
.000
-.001
.000
.OOO
.000*
-.002
1994
.005
.003
.016
.019
. OO3
.003
.001
. 103
.087
.060
.000
.OOO
.OOO
-.000
.000
.OOO
.000
-.003
1995
.005
.003
.013
.020
.003
.OO3
.000
. 1 18
.079
.056
.OOO
.008
.OOO
- .000
.001
.000
.000
- .003
1996
.004
.003
.010
.022
.004
.006
.OOO
. 13O
.098
.064
.000
.030
.OOO
.000
.001
.000
.000
-.002
-------
Table B-7. Change In crop income over variable coata. scenario 1. I/
OD
I
CJl
Crop
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Hillion dollara
Crop
Grain sorghum
Barley
Data
Wheat
Soybeans
Cotton
All hay
Total, net of fixed
and variable costs
-3.14
-.12
-.07
.00
-.16
-3.81
-2.62
.38
-9.32
-76.39
-4.19
-7.48
-5.12
-26.17
-33.09
-10. OS
-5.01
-338.62
-53.10
-3.69
-5.86
-3.43
-25.75
-33.92
-7.81
-6.35
-309.65
-50.88
-2.84
-4.08
-4.64
-25.56
-36.36
-S.79
-.20
-297.93
-47.
-2.
-2.
-4.
-25.
-36.
-3.
3.
-291.
83
43
00
13
26
50
94
62
17-
-48.50
-2.54
.23
-3.42
-25.13
-33.43
-2.36
6.15
-271.49
-46.62
-2.62
.35
-3.17
-23.98
-30.34
-1.57
7.72
-260.55
-43.51
-2.52
-.99
-2.49
-22.99
-27.49
-1.02
9.62
-257.84
-40.83
-2.34
-2.18
-2.01
-21.81
-27.68
-1.03
11 .15
-244.76
-42.89
-2.40
-3.62
-1.57
-21.13
-24.02
-1.97
12.42
-243.35
I/ Excluding changes In commodity program payments.
Table B-8. Change in crop income by region, scenario 1. !_/
Region
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Hillion dollara
Corn Belt
Lake States
Northern Plains
Southern Plains
Delta States
Mountain
Pacific States
Northeast
Appalachian
Southeast
1O
1
1
-19
-
-6
.63
.88
.96
.57
.09
.27
.88
.22
.07
. 17
-75.90
-43.70
-si .09
-21.73
-34.58
-36.08
-17.49
-18.22
-20.64
-19.18
-65.95
-39.97
-45.77
-21.04
-31.91
-34.90
-17.03
-17.90
-18.58
-16.61
-67.95
-39.56
-44.37.
-19.78
-28.41
-33.13
-15.55
-17.76
-17.95
-13.47
-69 .45.
-41.96
-43.12
-20.07
-25.58
-19.55
-16.69
-20.27
-19.66
-14.62 •
-64.84
-37.50
-38.61
-18.08
-2O.34
-30.50
-13.37
-17.85
-17.54
-12.86
-64.42
-36.68
-36.30
-17.92
-16.22
-30.42
-12. .82
-17.93
-17.24
-12.60
. -62.92
-39.27
'-36.54
-19.00
-13.17
-18.84
-15.18
-2O.34
-18.67
-13.90
-57.07
-34.98
-32.83
-17.34
-11 .90
-30.82
-12.96
-17.93
-16.48
-12.46
-55.38
-35.11
-32.17
-17.52
- 1 1 . 67
-31.38
-13.32
-18. 11
-16.14
-12.55
I./ Excluding changes in commodity program payments.
-------
Table B-9. Important welfare effects, scenario 1,
1987
198B
1989
1990
1991
1992
1993
1994
1995
1996
Crop consumer effect
Livestock income
change
Livestock consumer
effect
-22.72 -32.44 -36.65 -56.26
.00 -.45 1.21 -5.21
.00 .00 -24.01 -35.01
Million dollars
-69.11 -78.34 -82.43 -88.57
-25.90 -39.62 -44.45 '-41.91
-14.27 5.43 10.81 6.03
-91.85 -95.44
-37.18 -29.86'
-8.57 -25.99
Table B-10. Change in U.S. crop acreages, scenario 2.
Crop
1987
1988
1989 1990
1991
1992
1993
1994
1995
TO
I
1996
Thousand acres
Corn
Grain sorghum
Barley
Oats
Wheat
Soybeans
Cotton
All hay
Fallow
Total
1S7
4
-
-
-204
27
-3
-
-19
.22
.19
.08
.43
.47
.99
.85
.69
.02
.52
42.39
-3.96
-9.51
-15.66
-17.58
-98.40
41.07
23.78
-2.01
-41.86
-84. 3O
-12.05
-15.34
-11.46
-27.06
50.78
36.13
22.89
-3.16
-46.71
-120.60
-12.36
-20.57
-9.00
-17.00
90.41
16.22
7.02
-5.67
-77.19
-39.80
-2.82
-23. 7O
-IS. 48
-3.27
-7.75
.37
-2.01
-6.78
-108.02
-800.95
31.99
-25.74
-20.70
-11-. 04
607.04
-41.71
-25.83
-8.97
-304.88
1115.13
138.12
-18.38
-51.36
18.35
-999.43
-152.68
11.46
-7.47
46.27
1023.33
197.58
-14.20
-54.04
-17.31
-933.60
-217.99
58.31
-7.40
27.28
841.48
183.52
-7.93
-33.75
-22.34
-601 .20
-232.57
99.06
-.08
226.13
706.48
146.83
-3.57
-15.32
-23.69
-445.06
-215.87
80.95
-2.17
226.42
-------
Table B-ll. Change In commodity prices, scenario 2.
CO
I
Comnodity
Corn
Grain sorghum
Barley
Oata
Wheat
Soybeans
Cotton
All hay
Cottonseed
Cottonseed meal
Cottonseed oil
Soybean meal
Soybean oil
Beef
Pork
Chicken
Milk
Veal
1987
-.OO2
-.000
.OOO
.000
.000
.156
-.000
.022
1 . 4O3
.822
.004
1.441
.012
.000
.000
.000
.000
.000
1988
.000.
.000
.002
.009
.001
.180
-.002
-.124
1.727
.759
.005
1 .393
.017
.OOO
.000
.000
.000
-.001
1989
.008
.003
.004
.012
.003
.076
-.001
-.172
.827
.344
.002
.617
.008
.002
.002
.000
.OOO
.000
1990
.012
.007
.008
.009
.003
-.012
-.OOO
-.079
- .000
-.028
.000
-.101
.000
.002
.002
.000
.000
.002
1991
.007
.006
.012
.011
.002
-.012
.000
.010
-.039
-.048
.000
-.159
-.000
-.002
.002
.000
.000
.000
1992
. SOB
.149
.017
.013
.001
-.153
.003
.198
-1.198 ,
-.660
-.003
-1.330
-.012
-.005
.000
-.000
.000
-.002
1993
.339
.163
.017
.027
.OO5
.216
.ooa
.146
2.713
2.048
.OO7
2.007
.016
.045
.048
.027
.000
-.047
1994
.286
.137
.016
.030
' .010
.367
.011
-.086
4.756
3.101
.015
3.141
.036
.033
.088
.025
.000
. OO3
1995
.227
.106
.012
.020
.010
.322
.013
-.479
4.491
2.552
.017
2.339
- .037
.009
.056
.016
.000
.002
1996
.161
.073
.007
.008
.008
.243
.013
-.581
3.547
1.699
.016
1.339
.032
-.013
.013
.007
.000
-.007
Table B-12. Change in crop income over variable coats, scenario 2. !_/
Crop
1987
1988
1989
1990
.1991
1992
1993
1994
199S
1996
Million dollars
Corn
Grain sorghum
Barley
Oata
Wheat
Soybeans
Cotton
All hay
-21
-
-
189
-11
2
.51
.41
.00
.05
.01
.94
.57
.14
-83.82
-4.28
-7.52
-5.25
-25.96
209.05
-23.22
-12.21
-21.77
-2.34
-5.94
-4.69
-23.21
68.66
-20.37
-17.26
1 .28
-.15
-3.88
-6.27
-22.19
-58.29
-14.16
-8.74
-35.16
-.71
-1.98
-5.44
-24.24
-56.26
-9.23
- .00
2244.83
1O0.50
.03
-4.52
-25.75
-292.86
-17.35
17.96
1010.27
113. 4O
-.03
1 .00
-15.26
333.95 .
9.46
17.80
973.92
100.51
-1.01
1.69
-6.43
591 .33
30. SO
-4.46
747.64
79.15
-2.68
-2.45
-8.06
S14.60
42.01
-44.74
472.29
54.98
-4.27
-6.74
-15.95
380.52
44.28
-57.97
Total, net of fixed
and variable coats
1S9.46 -111.34 -186.60 -272.97 -314.49 1864.8O 129H.OS 1469.1O 11UO. 1)7
611.63
\_l Excluding changes in conocidity program, payments.
-------
Table B-13. • Change in crop income by region, scenario 2. I/
Region
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Million dollars
Corn Belt
Lake States
Northern Plains
Southern Plains
Delta States
(fountain States
Pacific States
Northeast
Appalachian
Southeast
ISO
27
23
-
-4
-
-
3
2
-42
.16
.98
.03
.00
.15
.86
.83
.26
.87
.00
88.40
-12.54
-26.48
-24.37
-15.72
-24.33
-21.13
-14.69
-12.64
-47.83
21.17
-20.44
-30.59
-24.10
-28.91
-22.21
-20.56
-15.45
-14.89
-30.62
-57.75
-34.18
-36.32
-22.23'
-37.01
-19.13
-17.69
-16.81
-19.41
-12.46
-81.11
-44.42
-42.35
-21 .85
-31.56
-19.77
-18.21
-2O.57
-21.94
-12.71
832.22
486.61
246.97
84.36
-47.62
6O.30
21.85
54.20
88.96
36.95
549.58
288.87
130.53
86.67
52.42
32.44
17. 03
15.49
78.48
46.55
666.42
277 . 27
141.59
88.16
89.80
24.79
15.25
15.51
93.41
56.89
515.54
199.98
89.38
72.59
"74.91
11.42
1O.O5
7.71
72.91
45.58
306 . 47
107.03
28.80
48.74
51.85
-1.74
2.46
-4.O4
43.74
28.31
!_/ Excluding changes in commodity program payments.
03
I
»-»
00
Table B-14. Important welfare effects, scenario 2.
Crop
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Crop consumer effect -235.82 -272.95 -158.41 -6O.75
Livestock income
change .00 -6.53 -.63 -29.99
Livestock con«un«i
effect .00 -38.95 -87.96 -81.74
Million dollars
-44.76 -2881.31 -26O7.44 -2564.88 -2062.60 -1477.10
-78.33 -99.18 -1601.30 -621.24 -1067.O3 -1421.79
11.31 109.22 -2251.68 -2626.22 -13S0.11 16.92
-------
Table-B-IS. Change in U.S. crop acreages, scenario 3.
Crop
1987
1988
1989
199'0
1991
1992
1993
1994
199S
1996
Thousand acrea
Corn
Grain sorghum
Barley
Data
Wheat
Soybeans
Cotton
All hay
Fallow
Total
157
4
-
-
-204
27
-3
-
-19
.22
.19
.08
.43
.47
.99
.as
.69
.02
.52
38.23
-4.11
- 9 . 98
-16.27
-19.73
-101.00
40.87
20.26
-2.63
-56.97
-90.93
- 12 . 30
-16.01
-12.91
-30.45
48.62
35.84
19.17'
-3.86
-66.72
-139.25
-11.93
-27.02
-11.15 '
-14.20
82.16
19.73
.62
-6.84
-114.72
-1O6..42
-3.26
-33.04
"-17.83
-7.50
4S.79
3.92
-7.26
-7.71
-140.99.
-1639.77
43.02
-30.21
-23.73
-26.08
1358.06
-91.45
-72.90
-10.78
-504.65
1098.16
212.86
-4.92
-82.43
-9.36
-867.56
-287.44
-61.61
-7.61
-17.52
. 1003.95
308.10
8.08
-104.37
-85.58
-776.59
-400.34
-21.95
-4.59
-77.89
797. 6O
285.68
16.47
-85.23
-112.37
-327.85
-424.51
' 31.78
8.96
199.51
664.47
222.23
' 16.97
r62.61
-127.91
-161.86
-385.31
8.83
8.41
191.65
CO
I
Table B-16. Change in commodity prices, scenario 3.
Commodity
Corn
Grain sorghum
Barley
Oats
Wheat
Soybeans
Cotton
All hay
Cottonseed
Cottonseed meal
Cottonseed oil
Soybean meal
Soybean oil
Beef
Pork
rhickan
Milk
Veal
1987
-.002
-.000.
.000
.000
.000
.156
- .OOO
.022
1 .403
.822
.004
1.441
.012
.000
.OOO
.OOO
.000
.000
1988
.000
.000
.002
.009
.001
.101
-.002
-.101
1 .739
.764
.005
1.399
.017
.000
.000
.OOO
.000
-.001
1989
.008
.004
.004
.013
.003
.077
-.001
-.137
.841
.349
.002
.623
.008
.002
.002
.OOO
.000
.OOO
1990
.018
.009
.018
.011
. OO7
.031
-.000
-.022
.410
.173
.001
.242
.004
.002
.002
.OOO
.000
.OO1
1991
.016
.010
.030
.013
.008
.025
.OOO
.074
.352
. 162
.001
'. 167
.003
- .000
.003
.OOO
• .000
- .000
1992
.777
.237
.038
.017
.008
-.257
.010
.547
-1.450
-.855
- .003
-2.283
- .020
-.003 .
.003 \
.000
.000
-.003
1993
.513
.254
.033
.043
.013
.222
.018
.755
3.6O6
2.873
.011
2.06B
.016
.067.
.072
.039
.000
-.067
1994
.430
. 2O8
.022
.058
.021
.425
.025
.632
6.376
4.353
.022
3.596
.041
.048
.130
.037
.000
-.000
1995
.339
.159
.010
.051
.021
.354
.029
.140
5.852
3.537
.024
2.461
.042
.013
.082
.024
.000
- . OO6
1996
.240
.110
.002
.038
.017
.243
.027
-.020
4.283
2.263
.021
1 .145
.033
-.016
..02O
.012
.000
-.019
-------
Table B-17. Change in crop income above variable coats, ecenario 3. !_/
Crop
Corn
Grain aorghum
Barley
Oats
Wheat
Soybeans
Cotton
All hay
Total, net of fixed
and variable costs
1987
-21.51
-.41
.00
.OS
-.01
189.94
- 1 1 . 57
2.14
159.46
1988
-82.72
-4.27
-7.48
-5.14
-25.83
210.46
-23.23
-10.00
-199.53
1989
-19.55
-2.29
-5.86
-4.26
-22.85
70.35
-20.39
-13.85
-271.62
1990
Million
16. 07
.52
-1.35
-5.63
-20.84
-2.88
-14.72
-3.15
-285.89
1991-
dollars
3.74
1.63
3.63
-4.73
-18.26
-9.25
-10.11
6.22
-302.70
1992
3136.56
149.84
7.40
-3.41
-17.43
-480. 3O
-27.97
50.56
2561 .71
1993
1308.52
169.17
4.99
7.35
-.58
330.17
23.03
77.93
1645.41
1994
1283.34
147.73
.37
12.12
13.81
672.07
62.50
66.93
1930.67
1995
982
115
-4
8
12
553
83
16
1421
.40
. 10
.35
.64
.65
.46
.96
.76
.98
1996
620.88
80.59
-7.60
3.75
2.23
366.99
85.64
-3.31
763.67
I/ Excluding changes in commodity program payments.
DO
ro
o
Table B-1B. Change in crop income by region, scenario 3. !_/
Region
1987
1988 1989
199O
1991
1992
1993
1994
1995
1996
Million -dollar*
Corn Belt
Lake States
Northern Plains
Southern Plains
Delta States
Mountain States
Pacific States
Northeant
Appalachian
.Soul hi*ant
ISO
27
23
-
-4
-
-
3
2
-A?
.16
.98
.03
.00
.15
.86
.83
.26
.87
.00
66.23
-26.57
-39.76
-29.10
-17.16
-29.80
-24.67
-22.95
-25.90
-49.85
. 11
-33.96
-43.14
-28.59
-30.27
-27.41
-23.80
-23.80
-28.07
-32. S9
-15.60
•43. 95
-41.44
-23.76
-27.80
-20.05
-10.36
-23.02
-59.75
-12.09
-33.74
-47.97
-40.95
-22.23
-24.70
-1.8.46
-17.31
-26.01
-SO.B3
-12.49
1391 .40
641.81
483.86
151.51
-93.58
112.36
46.50
-137.04
-48.75
13.55
876.73
354.27
281.46
159.98
43.84
73.83
45.93
- 164.05
-51.86
25.28
1009. 11
353.69
284.99
164.13
99.35
66.26
49.56
-133.20
-7.09
43.86
76O.24
255.06
193.00
139.59
80.77
45.70
42.41
-115.05
-11.48
.11 .74
438.96
134.95
91 .53
100.3.2
50.84
26.98
29.49
-99.70
-23.57
13.88
.!/ Excluding chan<|pB in commodity protjrau payments.
-------
00
fvi
Table B-19. Important welfare effects, scenario 3.
Crop 1987 1986 1989 199O 1991 1992 1993 1994 1995 1996
Crop consumer effect -235.82 -280.22 -169.7O -195.30 -2O5.B3 -44O3.68 -3U93.57 -3805.28 -30S5.04 -2188.32
Livestock Income
change .00 -6.53 -3.46 -33.35 - -97.74 -121.54 -2501.32 -995.59 -1597.77 -2053.40
Livestock consumer
effect .00 -38.95 -85.67 -79.43 -35.48 19.78 -3331.59 -3822.66 -1974.73 -60.06
-------
APPENDIX C
National Price-Quantity Model and Results
By
Craig Simons
and
Roger Lloyd I/
I/ OPRA Incorporated
-------
Appendix C
National Price-Quantity Model and Results
1.0 Model Description
The model used to estimate national commodity price-quantity
impacts closely follows the model developed by Lichtenberg et
al., I/ with some modifications required to overcome data defic-
iencies. With estimates of national impacts on production for
each commodity—through both increased costs and decreased yields-
-changes in marginal costs were estimated. The resulting changes
in commodity production and price at the national level were then
assessed with consideration of supply and demand elasticities.
Specific algebraic equations used to define the model are as
follows:
(1) PQ = MCQ
P (dY/Y.J + (dC/Y,J
(2) dMC = -=-
1 - (dY/YQ)
(3) dP/PQ = [es/(egeD)](dMC/MC)
(4) dQ/Q = [eDes/(es - eD)](dMC/MC)
where:
P = commodity baseline price, farm level
MC = baseline commodity marginal cost of production
dY = change in yield per acre of crop production from
the regulatory scenario
dC = change in variable cost per acre from the regula-
tory scenario
ec = elasticity of supply
3
e_ = elasticity of demand
Q = total baseline quantity of commodity production
Changes in producer and consumer surplus were then approximated.
To estimate changes in producer surplus, it was assumed that all
planned reductions in output would be achieved by shifts in
marginal production inputs (where zero economic profits were
I/ Lichtenberg, Erik; Douglas Parker and David Zilberman.
Economic Impacts of Cancelling Parathion Registration -for
Almonds, Western Consortium for the Health Professions,
Inc., January 1987.
C-l
-------
being earned in the baseline) to an alternative equally profitable
crop. Economic profits on this marginal production would be the
same before and after the regulatory scenarios. The change from
the baseline in total revenue earned by producers would be:
(5) dR = P1Ql - PQQo
and since price equals marginal cost, the cost savings would be:
(6) CTS = PQ(Q0 - C^).
The change in costs for the acreage remaining in production is
(7) dTC = AidC.
Accordingly, the change in producer surplus from the baseline is
defined as
(8) dPS = dR + CTS - dTC.
The change in consumer surplus from the baseline was approximated
using the following relationship:
(9) dCS = -(P. - P0)(Qi * Q0)/2
where:
dR = change in total revenue
CTS = cost savings
Q. = production in year i
dTC = change in total production cost
A. = commodity acreage in year i
dC = change in cost per acre from the regulatory
scenario
dPS = change in producer surplus
dCS = change in consumer surplus.
This model presumes that all other variables not considered will
remain constant and thus have no affect on the model results.
2.0 Data Inputs
National information was compiled on baseline price, harvested
acreage, production, farm size, and yield for each of the six
specialty crops. The baseline commodity prices, harvested acre-
ages, and production quantities used in this study are an average
from 1981-1985 as obtained from various issues of Agricultural
Statistics (Table C-l). Commodity prices were adjusted by the
GNP Implicit Price Deflator to reflect constant 1986 dollars.
C-2
-------
Table C-l. Average prices, production and acreages
Irish Potatoes
U.S.
ID-UA
ND-MN
HE
Green Peas
U.S.
Ul
UA
Apples
WA '
NY
Ml
Peanuts
Additional!
U.S.
GA-AL
NC-VA
TX-OK
Quotas
U.S.
GA-AL
NC-VA
TX-OK
Caneberries
(Raspberries)
U.S.
WA
OR
Tomatoes
Processing
CA
U.S.
Fresh
FL
U.S.
Average
price
1981-1985
(1986 dollars)
5.02/cwt.
4.71/cwt.
4.77/cwt.
4.12 cwt.
253. OO/ ton
239.64/ton
250. OO/ ton
264. OO/ ton
287. CO/ ton
246. OO/ ton
193. OO/ ton
599.14/ton
549.S6/ ton
579.07/ton
56S.47/ ton
587. BO/ ton
587. 80/ ton
587. 80/ ton
587. BO/ ton
.641/lb.
.643/1b.
.638/lb.
72.40/ton
75.68/ton
559. 70/ ton
522. OO/ ton
Average
acreage
harvested
1981-1985
(1000)
1.280
437
194
98
3,180
857
638
N.A.
100.8
64.1
46.6
1.112.6
603.3
199.2
250.4
314.9
162.7
53.7
67.5
107.5*
29.0
25.0
225.3
280.4
45.4
123.7
Average
production
1981-1985
359.282.000 cut.
146,083.000 cwt.
33,031.000 cwt.
24.926.000 cwt.
490,040 tons
134.400 tons
100.430 tons
4.064.500 tons
1.343,000 tons
517,000 tons
426,000 tons
1,500.053 tons
916,799 tons
179,649 tons
218,555 tons
424.564 tons
247,237 tons
75.416 tons
58,940 tons
38.979.000 Ibs.
15.934,000 Ibs.
13,360.000 Ibs.
5.944.000 tons
6.981.000 tons
660,000 tons
1.385.000 tons
Typical
farm
size
(acres)
—
725
1.000
600
—
540
1.500
200
150
200
...
500
400
1.100
—
500
400
1.100
—
30
11
1.200
—
500
—
Average
yield/acre
1981-1985
280.7 cwt.
386.0 cwt.
170.3 cwt.
254.4 cwt.
1.54 tons
1.57 tons
1.57 tons
8.70 tons
13.32 tons
8.06 tons
9.14 tons
1.35 tons
1.52 tons
1.40 tons
.88 tons
1.35 tons
1.52 tons
1.40 tons
.88 tons
3.625 Ibs.*
5.494 Ibs.
5.344 Ibs.
26.6 tons
24.9 tons
14.6 tons
11.2 tons
1982.
-------
In order to assess -the impacts of regulatory costs on per acre
net returns, a definition of a typical commercial farm, in terms
of acreage, was necessary. Such estimates were obtained from a
poll of extension crop production specialists (a DELPHI approach)
and from estimates obtained in crop enterprise production budgets.
Because farm size is highly variable within each region, the
estimates presented in Table C-l and used in the impact analysis
must be interpreted with caution.
Estimates of supply and demand elasticities were obtained from
several sources, both published and unpublished. Elasticity
estimates are presented in Table C-2.
National estimates of variable cost and yield changes associated
with environmental regulations for each specialty crop under
three scenarios were provided by EPA. The yearly estimates are
provided as the change from a base year prior to the initiation
of regulatory impacts (Table C-3).
3.0 Model Results
Results of the National Price-Quantity Model are presented in
Tables C-4 through C-18 as the percent change in production,
price, consumer surplus and producer surplus from a base year of
no regulatory impacts. Effects of each policy scenario are
examined under each of the four specialty crops. Data limitations
prevented analyses of peanuts and caneberries.
C-4
-------
Table C-2. Supply and demand elasticities
Demand Elasticities I/
Potatoes -.3688
Apples -.2015
Tomatoes (fresh) -.5584
Tomatoes (processing) -.3811
Other fresh vegetables (peas) -.2102
Supply Elasticities
Short-run
Peas .31 2/
Tomatoes 1.35 3_/
Potatoes .87 4/
Apples .11 4/
Sources: !_/ USDA, ERS, By Kuo S. Huang, U.S. Demand for Food;
A Complete System of Price and Income Effects.
Technical Bulletin Number 1714, December 1985.
2/ Askari, Hedsein, and Jonn T. Cummings, Estimating
Agricultural Supply Response with the Nerlove
Model; A Survey, International Economic Review,
Vol. 18, No. 2, June 1977.
3_/ Chern, W.S. "Acreage Response and Demand for
Processing Tomatoes in California". American
Journal of Agricultural Economics. May 1976.
4_/ Unpublished estimates provided by USDA.
C-5
-------
Table C-3. Regulatory cost and yield impact estimates for specialty crops
Change in variable cost from base year ())
Vear
Scenario 1
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Scenario 2
1983
1984
<"> 1985
en 1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Scenario 3
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Apples
0
0
0
0
0
4.86
4.86
4.86
4.86
6.78
6.51
6.23
5.96
5.68
0
0
0
0
0
4.86
4.86
-1.71
.12
13.30
12.34
11.39
10.43
9.48
0
0
0
0
0
4.86
4.86
-1.71
3.45
18.32
16.58
14.84
13.10
11.36
Potatoes
0
.26
.23
.19
4.41
5.05
.40
.88
.08
.05
.17
2.30
2.03
1.76
0
.69
.66
.62
4.84
5.48
4.83
13.77
11.75
13.78
11.23
8.68
6.74
4.80
0
.86
.83
.79
5.01
5.65
5.00
10.17
8.70
12.44
10.26
8.08
6.51
4.94
Tomatoes
Fresh
0
.66
.56
.47
.38
7.03
6.94
6.84
6.75
6.75
6.75
6.75
6.75
6.75
0
.66
.56
.47
.38
7.03
6.94
22.95
20.56
18.26
15.95
13.65
11.35
9.05
0
.66
.56
.47
.38
7.03
6.94
-13.08
-10.33
-7.48
-4.64
-1.79
1.05
3.90
Proc.
0
.66
.56
.47
.38
7.03
6.94
6.84
6.75
6.75
6.75
6.75
6.75
6.75
0
.66
.56
.47
.38
7.03
6.94
6.97
6.86
6.84
6.82
6.80
6.78
6.76
0
.66
.56
.47
.38
7.03
6.94
5.99
6.02
6.14
6.26
6.38
6.50
6.62
Peas
0
0
0
0
3.46
3.74
3.25
2.75
2.26
2.06
1.53
.99
.95
.90
0
0
0
0
3.46
3.74
3.25
2.75
2.26
2.64
2.02
1.40
1.27
1.15
0
0
0
0
3.46
3.74
3.25
2.75
2.26
2.06
1.53
.99
.95
.90
Peanuts
.01
1.66
1.42
1.19
1.60
2.21
1.58
5.13
4.25
3.60
2.95
2.30
1.75
1.19
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
.01
1.66
1.42
1.19
1.60
2.21
1.58
26.09
22.21
22.12
17.97
13.82
9.77
5.71
Apples
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.050
.043
.036
.028
.021
.014
.007
0
0
0
0
0
0
0
.050
.043
.036
.028
.021
.014
.01
Change in yield from base year
Potatoes
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.048
.041
.034
.027
.021
.014
.007
Tomatoes
Fresh
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.196
.168
.140
.112
.084
.056
.028
Proc.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.050
.043
.036
.029
.021
.014
.007
Peas
0
0
0>
o|
o1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Peanuts
0
.013
.011
.010
.080
.067
.055
.045
.033
.022
.011
.001
.001
.001
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
0
.013
.011
.010
.080
.067
.055
.253
.211
.279
.223
.168
.122
.077
-------
Table C-4. Production and welfare impacts from Scenario I
environmental regulations affecting apples
Percent change
from Base Year 1987
Change in welfare
from Base Year 1987
Year Production
Price
Consumer
Surplus
Producer
Surplus
Net
1988
1989
1990
1991
1992
1993
1994
1995
1996
-0.015
-0.015
-0.015
-0.015
-0.021
-0.020
-0.019
-0.018
-0.018
0.0747
0.0747
0.0747
0.0747
0.1042
0.1000
0.0958
0.0916
0.0874
-799,261
-799,261
-799,261
-799,261
-1,114,985
•1,069,880
•1,024,780
-979,676
-934,574
•1,463,990
•1,463,990
•1,463,990
•1,463,990
•2,042,235
•1,959,628
•1,877,029
•1,794,423
•1,711,818
-2,263,251
-2,263,251
-2,263,251
-2,263,251
-3,157,220
-3,029,508
-2,901,809
-2,774,099
-2,646,392
C-7
-------
Table C-5. Production and welfare impacts from Scenario II
environmental regulations affecting apples
Percent change
from Base Year 1987
Change in welfare
from Base Year 1987
Year Production
Price
Consumer
Surplus
Producer
Surplus
Met
1988
1989
1990
1991
1992
1993
1994
1995
1996
-0.015
-0.015
-0.367
-0.318
-0.305
-0.248
•0.191
-0.135
-0.081
0.0747
0.0747
1.8230
1.5764
1.5144
1.2296
0.9489
0.6724
0.3997
-799,261
-799,261
•19,465,134
-16,836,028
•16,174,993
•13,136,997
•10,141,456
-7,187,490
-4,274,242
-1,463,990
-1,463,990
•35,590,977
•30,791,483
•29,584,370
•20,034,741
•18,559,526
•13,157,250
-7,826,479
-2,263,251
-2,263,251
-55,056,111
-47,627,511
-45,759,363
-37,171,738
-28,700,982
-20,344,740
-12,100,721
C-8
-------
Table C-6. Production and welfare impacts from Scenario III
environmental regulations affecting apples
Percent change Change in welfare
from Base Year 1987 from Base Year 1987
Consumer Producer
Year Production Price Surplus Surplus Net
1988 -0.015 0.0747 -799,261 -1,463,990 -2,263,251
1989 -0.015 0.0747 -799,261 -1,463,990 -2,263,251
1990 -0.367 1.8230 -19,465,134 -35,590,977 -55,056,111
1991 -0.328 1.6299 -17,406,795 -31,833,637 -49,240,432
1992 -0.321 1.5944 -17,028,442 -31,142,821 -48,171,263
1993 -0.261 1.2966 -13,852,108 -25,341,353 -39,193,461
1994 -0.202 1.0031 -10,720,085 -19,617,380 -30,337,465
1995 -0.144 0.7139 -7,631,449 -13,969,365 -21,600,814
1996 -0.086 0.4288 -4,585,308 -8,395,821 -12,981,129
C-9
-------
Table C-7. Production and welfare impacts from Scenario I
environmental regulations affecting potatoes
Percent change
from Base Year 1983
Year
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Production
-0.005
-0.004
-0.004
-0.088
-0.100
-0.088
-0.097
-0.081
-0.080
-0.063
-0.046
-0.040
-0.035
Price
0.0142
0.0122
0.0101
0.2375
0.2721
0.2373
0.2632
0.2198
0.2182
0.1711
0.1239
0.1095
0.0952
Change-in welfare
from Base Year 1983
Consumer
Surplus
-236,332
-202,571
-168,810
-3,958,133
-4,534,309
-3,954,689
-4,386,196
-3,662,095
-3,636,317
-2.850.87L
-2,065,289
-1,825,783
-1,586,266
Producer
Surplus
-100,181
-85,870
-71,559
-1,677,149
-1,921,165
-1,675,690
-1,858,441
-1,551,762
-1,540,844
-1,208,126
-875,292
-773,808
-672,31-3
Net
-336,513
-288,441
-240,369
-5,635,282
-6,455,474
-5,630,379
-6,244,637
-5,213,857
-5,177,161
-4,058,997
-2,940,581
-2,599,591
-2,258,579
c-io
-------
Table C-8. Production and welfare impacts from Scenario II
environmental regulations affecting potatoes
Percent change
from Base Year 1983
Year
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Production
-0.014
-0.013
-0.012
-0.096
-0.109
-0.096
-0.274
-0.234
-0.274
-0.223
-0.173
-0.134
-0.095
Pri ce
0.0374
0.0353
0.0333
0.2607
0.2953
0.2605
0.7424
0.6338
0.7428
0.6054
0.4680
0.3634
0.2588
Change in welfare
from Base Year 1983
Consumer
Surplus
-622,822
-589,064
-555,306
-4,344,305
-4,920,431
-4,340,861
-12,359,750
-10,553,621
-12,366,205
-10,081,485
-7,795,613
-6,054,275
-4,312,263 .
Producer
Surplus
-264,001
-249,693
-235,384
-1,840,700
-2,084,674
-1,839,241
-5,232,215
-4,468,530
-5,234,943
-4,268,846
-3,301,768
-2,564,735
-1,827,130
Net
-886 ,823
-838,757
-790,690
-6,185,005
-7,005,105
-6,180,102
-17,591,965
-15,022,151
-17,601,148
-14,350,331
-11,097,381
-8,619,010
-6,139,393
C-ll
-------
Table C-9. Production and welfare impacts from Scenario III
environmental regulations affecting potatoes
Rercent change
from Base Year 1983
Year
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Production
-0.017
-0.016
-0.016
-0.100
-0.112
-0.099
-1.518
-1.292
-1.176
-0.940
-0.708
-0.491
-0.278
Price
0.0465
0.0445
0.0425
0.2699
0.3045
0.2697
4.1172
3.5024
3.1878
2.5494
1.9199
1.3324
0.7530
Change in welfare
from Base Year 1983
Consumer
Surplus
-775,612
-741,855
-708,097
-4,496,968
-5,073,075
-4,493,524
-68,115,431
-58,010,709
-52,830,082
-42,299,748
-31,892,323
-22,157,065
-12,535,455
Producer
Surplus
-328,760
-314,452
-300,145
-1,905,352
-2,149,309
-1,903,893
-28,653,782
-24,431,352
-22,262,674
-17,846,511
-13,471,380
-9,369,425
-5,306,492
Net
-1,104,372
-1,056,307
-1,008,243
-6,402,320
-7,222,384
-6,397,417
-96,769,213
-82,442,061
-75,092,756
-60,146,259
-45,363,703
-31,526,490
-17,841,947
C-12
-------
Table C-10. Production and welfare impacts from Scenario I
environmental regulations affecting fresh tomatoes
Percent change
from Base Year 1983
Year
1984
1985
1986
1987
.1988
1989
1990
1991
1992
1993
1994
1995
1996
Production
-0.004
-0.004
-0.003
-0.003
-0.048
-0.047
-0.046
-0.046
-0.046
-0.046
-0.046
-0.046
-0.046
Price
0.0079
0.0068
0.0057
0.0045
0.0851
0.0839
0.0828
0.0817
0.0817
0.0817
0.0817
0.0817
0.0817
Change in welfare
from Base Year 1983
Consumer
Surplus
-57,473
-49,263
-41,052
-32,842
-615,108
-606,901
-598,694
-590,487
-590,487
-590,487
-590,487
-590,487
-590,487
Producer
Surplus
-23,772
-20,376
-16,980
-13,584
-254,366
-250,973
-247,580
-244,187
-244,187
-244,187
-244,187
-244,187
-244,187
Net
-81,245
-69,639
-58,032
-46,426
-869,474
-857,874
-846,274
-834,674
-834,674
-834,674
-834,674
-834,674
-834,674
U'S-EPi ^quarters Library
no a"COde3201
° Pennsylvania Avenue NW
Washington DC 20460
C-13
-------
Table C-101. Production and welfare impacts from Scenario I
environmental regulations affecting processing tomatoes
Percent change
from Base Year 1983
Year
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Production
-0.010
-0.009
-0.007
-0.006
-0.111
-0.109
-0.108
-0.106
-0.106
-0.106
-0.106
-0.106
-0.106
Price
0.0272
0.0233
0.0194
0.0155
0.2910
0.2871
0.2832
0.2793
0.2793
0.2793
0.2793
0.2793
0.2793
Change in welfare
from Base Year 1983
Consumer
Surplus
-143,609
-123,094
-102,579
-82,064
-1,536,544
-1,516,050
-1,495,556
-1,475,061
-1,475,061
-1,475,061
-1,475,061
-1,475,061
-1,475,061
Producer
Surplus
-150,002
-128,574
-107,145
-85,717
-1,605,324
-1,583,907
-1,562,490
-1,541,073
-1,541,073
-1,541,073
-1,541,073
-1,541,073
•-1,541,073
Net
-293,611
-251,668
-209,724
-167,781
-3,141,868
-3,099,957
-3,058,046
-3,016,134
-3,016,134
-3,016,134
-3,016,134
-3,016,134
-3,016,134
C-14
-------
Table C-12. Production and welfare impacts from Scenario II
environmental regulations affecting fresh tomatoes
Percent change
from Base Year 1983
Year
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Production
-0.004
-0.004
-0.003
-0.003
-0.048
-0.047
-0.155
-0.139
-0.123
-0.108
-0.092
-0.077
-0.061
Price
0.0079
0.0068
0.0057
0.0045
0.0851
0.0839
0.2777
0.2488
0.2209
0.1930
0.1652
0.1373
0.1095
Change in welfare
from Base Year 1983
Consumer
Surplus
-57,473
-49,263
-41,052
-32,842
-615,108
-606,901
-2,006,558
-1,797,741
-1,596,756
-1,394,866
-1,193,818
-992,739
-791,629
Producer
Surplus
-23,772
-20,376
-16,980
-13,584
-254,366
-250,973
-829,328
-743,082
-660,058
-576,647
-493,571
-410,469
-327,341
Net
-81,245
-69,639
-58,032
-46,426
-869,474
-857,874
-2,835,886
-540,823
-256,814
-1,971,513
-1,687,389
-1,403,208
-1,118,970
C-15
-------
Table C-13. Production and welfare impacts from Scenario II
environmental regulations affecting processing tomatoes
Percent change
from Base Year 1983
Year
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Production
-0.010
-0.009
-0.007
-0.006
-0.111
-0.109
-0.110
-0.108
-0.108
-0.108
-0.107
-0.107
-0.107
Price
0.0039
0.0034
0.0028
0.0023
0.0422
0.0416
0.0418
0.0412
0.0410
0.0409
0.0408
0.0407
0.0406
- Change- in welfare
from Base Year 1983
Consumer
Surplus
-143,609
-123,094
-102,579
-82,064
-1,536,544
-1,516,050
-1,523,111
-1,499,086
-1,494,718
-1,490,350
-1,485,982
-1,481,614
-1,477,245
Producer
Surplus
-150,002
-128,574
-107,145
-85,717
-1,605,324
-1,583,907
-1,591,286
-1,566,179
-1,561,615
-1,557,050
-1,552,485
-1,547,920
-1,543,355
Net
-293,611
-251,668
-209,724
-167,781
-3,141,868
-3,099,957
-3,114,397
-3,065,265
-3,056,333
-3,047,400
-3,038,467
-3,029,534
-3,020,600
C-16
-------
Table C-14. Production and welfare impacts from Scenario III
environmental regulations affecting fresh tomatoes
Percent change
from Base Year 1983
Year
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Production
-0.004
-0.004
-0.003
-0.003
-0.048
-0.047
-9.520
-7.892
-6.372
-4.947
-3.609
-2.351
-1.165
Price
0.0079
0.0068
0.0057
0.0045
0.0851
0.0839
17.0482
14.1338
11.4106
8.8589
6.4634
4.2099
2.0863
Change in welfare
from Base Year 1983
Consumer
Surplus
-57,473
-49,263
-41,052
-32,842
-615,108
-606,901
-120,000,000
-98,000,000
-80,000,000
-62,000,000
-46,000,000
-30,000,000
-26,000,000
Producer
Surplus
-23,772
-20,376
-16,980
-13,584
-254,366
-250,973
-46,000,000
-39,000,000
-32,000,000
-25,000,000
-19,000,000
-12,000,000
-6,167,852
Net
-81,245
-69,639.
-58,032
-46,426
-869,474
-857,874
-166,000,000
-137,000,000
-112,000,000
-87,000,000
-65,000,000
-42,000,000
-32,167,852
C-17
-------
Table C-15. Production and welfare impacts from Scenario III
environmental regulations affecting processing tomatoes
Percent change
from Base Year 1983
Year
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Production
-0.010
-0.009
-0.007
-0.006
-0.111
-0.109
-1.664
-1.430
-1.201
-0.976
-0.753
-0.535
-0.319
Price
0.0272
0.0232
0.0194
0.0155
0.2910
0.2871
4.3654
3.7515
3.1515
2.5602
1.9768
1.4027
0.8367
Change in welfare
from Base Year 1983
Consumer
Surplus
-143,609
-123,094
-102,579
-.82,064
-1,536,544
-1,516,050
-23,000,000
-20,000,000
-17,000,000
-13,000,000
-10,000,000
-7 ,400 ,000
-4,413,759
Producer
Surplus
-150,002
-128,574
-107,145
-85,717
-1,605,324
-1,583,907
-24,000,000
-21,000,000
-17,000,000
-14,000,000
-11,000,000
-7,700,000
-4,613,538
Net
-293,611
-251,668
-209,724
-167,781
-3,141,868
-3,099,957
-47,000,000
-41,000,000
-34,000,000
-27,000,000
-21,000,000
-15,100,000
-9,027,297
C-18
-------
Table C-16. Production and welfare impacts from Scenario I
environmental regulations affecting peas
Percent change
from Base Year 1986
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Production
-0.111
-0.120
-0.104
-0.089
-0.073
-0.066
-0.049
-0.032
-0.030
-0.029
Price
0.5297
0.5724
0.4967
0.4211
0.3454
0.3156
0.2334
0.1512
0.1446
0.1380
Change in welfare
from Base Year 1986
Consumer
Surplus
-656,421
-709,320
-615,598
-521,862
-428,111
-391,204
-289,309
-187,396
-179,271
-171,145
Producer
Surplus
-444,848
-480,675
-417,197
-353,700
-290,182
-265,174
-196,122
-127,046
-121,539
-116,031
Net
-1,101,269
-1,189,995
-1,032,795
-875,562
-718,293
-656,378
-485,431
-314,442
-300,810
-287,176
C-19
-------
Table C-17. Production and welfare impacts from Scenario II
environmental regulations affecting peas
Percent change
from Base Year 1986
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Production
-0.111
-0.120
-0.104
-0.089
-0.073
-0.085
-0.065
-0.045
-0.041
-0.037
Price
0.5297
0.5724
0.4967
0.4211
0.3454
0.4037
0.3089
0.2141
0.1949
0.1758
Change in welfare
from Base Year 1986
Consumer
Surplus
-656,421
-709,320
-615,598
-521,862
-428,111
-500,359
-382,887
-265,392
-241,669
-217,946
Producer
Surplus
-444,848
-480,675
-417,197
-353,700
-290,182
-339,131
-259,538
-179,912
-163,834
-147,754
Net
-1,101,269
-1,189,995
-1,032,795
-875,562
-718,293
-839,490
-642,425
-445,304
-405,503
-365,700
C-20
-------
Table C-18. Production and welfare impacts from Scenario III
environmental regulations affecting peas
Percent change
from Base Year 1986
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Production
-0.111
-0.120
-0.104
-0.089
-0.073
-0.066
-0.049
-0.032
-0.030
-0.029
Price
0.5297
. 0.5724
0.4967
0.4211
0.3454
0.3156
0.2334
0.1512
0.1446
0.1380
Change in welfare
from Base Year 1986
Consumer
Surplus
-656,481
-709,320
-615,598
-521,862
-428,111
-391,204
-289,309
-187,396
-179,271
-171,145
Producer
Surplus
-444,848
-480,675
-417,197
-353,700
-290,182
-265,174
-196,122
-127,046
-121,539
-116,031
Net
-1,101,269
-1,189,995
-1,032,795
-875,562
-718,293
-656,378
-485,431
-314,442
-300,810
-287,176
C-21
-------
APPENDIX D
REPFARM Model and Results
By
Mike Salassi !_/
Terry Dinan 2/
I/ Agriculture and Rural Economics Division, Economic Research
Service, U.S. Department of Agriculture.
2/ Office of Policy Analysis, U.S. Environmental Protection
Agency.
-------
Appendix D
REPFARM Model and Results
1.0 Description of REPFARM Model
REPFARM is a whole-farm, recursive programming-simulation model
which is capable of using a wide variety of farm policy, produc-
tion, and market environments in order to provide financial
impact information for a variety of representative farms across
the United States. REPFARM essentially links a set of accounting
decision subroutines with a set of optimizing subroutines. The
optimizing subroutines annually adjust the mix of crop enterprises
produced on the farm based upon estimated returns for each
enterprise. The accounting subroutines calculate farm income and
expenses, value of assets and liabilities, as well as other
financial information associated with the production decisions
made each year.
REPFARM is capable of simulating the annual production and
financial operations of a representative farm for a period of
1-10 years. The model utilizes user-specified data sets which
contain information relative to the partiqular representative
farm being simulated. Information about a particular farm
contained in a data set includes farm size, acres owned and
leased, initial values of farm assets and liabilities, off-farm
income, family living expenses, itemized expenses for the farm
such as taxes and insurance, as well as acreages, yields, produc-
tion costs, and labor requirements of each crop enterprise produced
on the farm and herd size, input costs, and labor requirements of
each livestock enterprise produced on the farm. Additional
information which must also be supplied by the user on an annual
basis includes itemized inflation indexes for various production
expense items, interest rates for short-term, intermediate-term,
and long-term loans, machinery depreciation rates, income tax
rates, market prices for all crop and livestock enterprises
included on the farm as well as farm policy -data such as loan
rates, target prices, crop set-asides, diversion payment rates,
and payment limitations.
REPFARM can simulate a representative farm in a deterministic or
stochastic mode. In the deterministic mode, the farm is simulated
with specified crop and livestock market prices and crop yields
for each year of simulation. Model output consists of annual
financial statements for the farm. These financial statements
include itemized income statements, cashflow statements, and
balance sheets. Additional production information is also provided
relating to the acreage and production of each crop enterprise.
In the stochastic mode, several iterations are performed for each
year of simulation using variable crop yields and crop and
livestock market prices. Model output in this mode consists
D-l
-------
primarily of annual mean and variance estimates of selected
financial measures and production items. REPFARM was simulated
in the deterministic mode in this study.
Three key assumptions that were made in the baseline projections
of each of the REPFARM models are:
1) production costs were assumed to increase at two percent
per year,
2) crop yield.was assumed to increase at two percent per
year, and
3) the current farm bill was assumed to be in effect
through 1990 and policy variables were held constant at
the 1990 level for the remaining forecast period.
If these assumptions overestimate the financial well-being of the
representative producers in the baseline, then the ability of the
producers to bear the costs of environmental regulations will be
overestimated. Likewise, if these assumptions result in an
underestimation of producers well-being, then the ability of
producers to bear the costs of environmental regulations will be
underestimated.
2.0 Description of Representative Farms
Representative farms evaluated in this study were developed from
data obtained from the USDA's 1986 Farm Costs and Returns Survey.
Three general types of farms considered included a Mississippi
cotton soybean farm, and Illinois corn soybean farm, and a Kansas
wheat cattle farm. For each one of these general farm types, two
representative farm data sets were constructed: one representing
a farm in an average financial position and another representing
a farm in a vulnerable financial position. Representative farm
data sets for farms in an average financial position were developed
from data on all farms meeting the specified state/enterprise
definition. Representative farm data sets for farms in a vul-
nerable financial position were developed from data on all farms
meeting the state/enterprise definition plus the additional
requirements of a negative net cash income and a debt to asset
ratio greater than 0.40.
2.1 Illinois Corn Soybean Farms
The two representative Illinois corn soybean farms were developed
from survey information on farms in Illinois which were classified
as cash grain farms (cash grain sales represented the largest
portion of gross income for the farm) and produced corn and
soybeans. Survey observations fitting this description represent
D-2
-------
an expanded number of 30,837 farms in Illinois (Table D-l) and
were used to estimate the characteristics of the corn soybean
farm in an average financial position (Table D-2). Of these
30,837 farms, approximately 9.9% were determined to be in a
vulnerable position (as defined above) and survey observations
relating to this group of farms were used to develop the charac-
teristics of the corn soybean farm in a vulnerable financial
position (Table 0-2).
2.2 Mississippi Cotton Soybean Farms
The two representative Mississippi cotton soybean farms were
developed from survey information on farms in Mississippi which
were classified as field crop farms (field crop sales represented
the largest portion of gross income for the farm) and produced
cotton and soybeans. Survey observations fitting this description
represent an expanded number of 1,798 farms in Mississippi (Table
0-1) and were to estimate the characteristics of the cotton
soybean farm in an average financial position (Table 0-3). Of
these 1,798 farms, approximately 14.2% were determined to be in a
vulnerable financial position (as defined above) and survey
observations relating to this group of farms were used to develop
the characteristics of the cotton soybean farm in a vulnerable
financial position (Table 0-3).
2.3 Kansas Wheat Cattle Farms
The two representative Kansas wheat cattle farms were developed
from survey information on farms in Kansas which produced wheat
and had sales of cattle. Survey observations fitting this
description represent an expanded number of 19,966 farms in
Kansas (Table D-l) and were used to estimate the characteristics
of the wheat cattle farms in an average financial position (Table
D-4). Of these 19,966 farms, approximately 7.1% were determined
to be in a vulnerable financial position (as defined above) and
survey observations relating to this group of farms were used to
develop the characteristics of the wheat cattle farm in a vulner-
able position (Table 0-4).
3.0 EPA Supplied REPFARM Inputs
EPA actions are entered into the REPFARM model as:
* changes in variable production costs,
* changes in fixed production costs,
* changes in crop yields, and
* changes in crop and livestock prices.
The changes in crop and livestock prices were obtained from AGSIM
and are described in Appendix B. The first year cost and yield
impacts assumed for each of the REPFARM models are described in
0-3
-------
Table D-l
1986 Farm Numbers
Illinois Corn Soybean;
Corn Belt —
Illinois
345,871 total farms
220,763 farms produce corn for grain
112,489 classified as cash grain farms
producing corn and soybeans I/
65,672 total farms
49,083 farms produce corn for grain
30,837 classified as cash grain farms producing
corn and soybeans I/
Mississippi Cotton Soybean;
Delta States - 73,747 total farms
- 7,438 farms produce cotton
- 3,576 classified as field crop farms producing
cotton and soybeans 2/
Mississippi
- 27,542 total farms
- 3,435 farms produce cotton
- 1,798 classified as field crop farms producing
cotton and soybeans 2_/
Kansas Wheat Cattle;
Northern
Plains
Kansas
153,884 total farms
84,097 farms produce wheat
50,143 produce wheat and raise cattle
54,024 total farms
31,000 farms produce wheat
19,966 produce wheat and raise cattle
I/ Cash grain farms are farms on which the largest portion of
gross income is accounted for by sales of cash grains such as
corn, soybeans or wheat.
2_/ Field crop farms are farms on which the largest portion of
gross income is accounted for by sales of field crops such as
cotton or tobacco.
Source: 1986 Farm Costs and Returns Survey
D-4
-------
Table D-2
Initial Characteristics of Representative Farms
Simulated for EPA's Agricultural Sector Study
Illinois Corn Soybean Farms
Farm acreage:
Cropland owned
Cropland rented
Pastureland owned
Pastureland rented
Total land operated
Cropland, percent tillable
Average
Financial
Position
160
363
0
0
523
98%
Number of full-time hired workers
Value of assets ($) I/:
Cropland & buildings 194,293
Pastureland 0
Farm machinery 86,920
Livestock 0
Non-farm investments 12,777
Beginning cash reserve 2,000
Debt to Asset Ratio .28
Off-farm income ($) 17,766
Family living expenses ($) 15,500
Crop acreage 2/:
Corn " 325
Soybeans 190
Crop yields (bu.) 3_/:
Corn 122.4
Soybeans 36.8
Vulnerable
Financial
Position
92
445
0
0
537
84%
130,656
0
85,980
0
6,736
2,000
.67
36,072
15,500
280
173
109.5
32.8
As of January 1, 1987.
Planted acreage plus set-aside acreage.
State average yields (1981-1987) were used for representative
producers in average financial condition. (Source: Crop
Production, 1983, 1986, and 1987 Annual Summaries). These
yields were adjusted (based on survey information) for
vulnerable producers.
Source: Data developed from 1986 Farm Costs and Returns Survey
D-5
-------
Table D-3
Initial Characteristics of Representative Farms
Simulated for EPA's Agricultural Sector Study
Mississippi Cotton Soybean Farms;
Farm acreage:
Cropland owned
Cropland rented
Pastureland owned
Pastureland rented
Total land operated
Cropland, percent tillable
Average
Financial
Position
413
1,016
0
0
1,429
81%
Number of full-time hired workers 2
Value of assets ($) I/:
Cropland & buildings
Pastureland
Farm machinery
Livestock
Non-farm investments
Beginning cash reserve
Debt to Asset Ratio
Off-farm income ($)
Family living expenses ($)
Crop acreage 2_/:
Cotton
Soybeans
Crop yields 3/:
Cotton (Ib.)
Soybeans ( bu . )
429,943
0
140,557
0
11,506
2,000
.33
16,856
15,500
545
611
722.5
22.0
Vulnerable
Financial
Position
409
1,442
0
0
1,851
84%
2
340,204
0
153,280
0
15,069
2,000
.83
5,193
15,500
657
889
722.5
18.7
I/
As of January 1, 1987.
Planted acreage plus set-aside acreage.
State average yields (1981-1987) were used. (Source
Production, 1983, 1986, and 1987 Annual Summaries).
Crop
Source: Data developed from 1986 Farm Costs and Returns Survey
D-6
-------
Table 0-4
Initial Characteristics of Representative Farms
Simulated for EPA's Agricultural Sector Study
Kansas Wheat Cattle Farms:
Farm acreage:
Cropland owned
Cropland rented
Pastureland owned
Pastureland rented
Total land operated
Cropland, percent tillable
Average
Financial
Position
326
431
224
296
1,277
77%
Vulnerable
Financial
Position
318
743
176
409
1,646
78%
Number of full-time hired workers
Value of assets (§) I/:
Cropland & buildings 145,356
Pastureland 50,176
Farm machinery 69,740
Livestock 9,390
Non-farm investments 15,187
Beginning cash reserve 2,000
Debt to Asset Ratio .31
Off-farm income (?) 20,123
Family living expenses ($) 15,500
114,326
39,424
80,143
24,540
8,571
2,000
.85
15,366
15,500
Crop acreage 2/:
Wheat
Soybeans
Sorghum
Corn
342
39
165
37
430
123
223
52
Crop yields (bu.) 3/:
Wheat
Soybeans
Sorghum
Corn
35.4
26.5
62.8
120.8
32.2
15.4
60.9
97.0
Continued..
D-7
-------
Table D-4. (Continued)
Kansas Wheat Cattle Farms:
Livestock inventory:
Cows
Replacement heifers
Feeder steers 4/
Average
Financial
Position
15
3
75
Vulnerable
Financial
Position
40
6
50
I/ As of January 1, 1987.
2/ Planted acreage plus set-aside acreage.
3/ State average yields (1981-1987) were used for representative
producers in average financial condition. (Source: Crop
Production, 1983, 1986, and 1987 Annual Summaries). These
yields were adjusted (based on survey information) for
vulnerable producers.
4/ Feeder steers are purchased and sold within the calendar year,
Source: Data developed from 1986 Farm Costs and Returns Survey
D-8
-------
Tables D-5 through D-7. These cost and yield effects were provided
by EPA Program Offices. Impacts of pesticide cancellations were
assumed to dissipate evenly over a seven year period.
4.0 REPFARM Output
The impact of EPA actions on the financial condition of each of
the representative farms was determined by examining:
* the change in net cash farm income due to EPA actions, and
* the change in debt asset ratios due to EPA actions.
Three major field crop and livestock farms in two financial
conditions were created, resulting in a total of six different
representative farms:
* an Illinois Corn Soybean Farm
- in average financial condition
- in vulnerable financial condition
* a Mississippi Cotton Soybean Farm
- in average financial condition
- in vulnerable financial condition
* a Kansas Wheat Cattle Farm
- in average financial condition
- in vulnerable financial condition
For each REPFARM in each scenario, two alternative sets of impacts
were considered:
* A Maximum Impact Case; In this case it is assumed that
the producer is impacted by every regulation that may
possibly affect a producer of that type.
* An Average Impact Case; In this case it is assumed that
the producer experiences the average impact of producers
of that type - e.g., if 50% of all producer of a given
type experience a $2.00/acre cost, we would assume a
$1.00/acre cost for the average impacted producer.
The net cash farm income and debt to asset ratios of each of these
farms is examined for each of the three alternative EPA scenarios
defined in this study. This output is presented in Figures Dl -
018.
D-9
-------
Table D-5
Potential Impacts on Illinois Corn Soybean Farm I/
Variable Cost; First Year Impacts
Scenario Action Crop Cost 2/ Yield(%) Acres(%)3/
1-3 Alachlor-restricted
use
corn .50 0 38.6
soybeans .50 0 25.4
1-3 Farm Worker Safety
corn .98 0 90
soybeans .62 80
1 Corn Rootworm
Insecticides Plan
I
corn .70 0 20
2 Groundwater Plan II:
alachlor
corn 1.80 0 1.5
soybeans 1.60 0 1
2 Groundwater Plan II:
cyanazine
corn 17.87 -11.07 0.2
2 Groundwater Plan II:
atrazine
corn 17.87 -11.07 1.6
2 Corn Rootworm
Insecticides Plan
II
corn -8.50 -24.0 34
3 Groundwater Plan III:
alachlor
corn 1.80 0 6.1
soybeans 1.60 0 8.3
3 Groundwater Plan III:
cyanazine
corn 17.87 -11.07 4.3
3 Groundwater Plan III:
atrazine
corn 17.87 -11.07 14.6
Continued...
D-10
-------
Scenario
Action
Table D-5 (continued)
Crop Cost y Yield(%) Acres(%)3/
Corn Rootworm
Insecticides Plan
III
corn
-8.50 -24.0
34
Fixed Costs:
Scenario
1-3
Action
Underground Storage Tank
1-3
Enclosed Cabs
Lead Ban
1-3
SARA Title III,
Section 302-304
Impact
Insurance: $2,500/yr.
2 tank tightness test @
$500, there are 5,428
USTs in the cornbelt
distributed over 310,000
farms.
Cost of enclosing cab =
$2,500. Assumed the 1/3
of all cabs must be
enclosed.
Assumed impacted farm
incurred 1,000 cost to
rebuild a tractor, truck
or combine engine.
Predicted 7,280 trucks,
4,865 combines and 23,112
tractors in cornbelt
would need to be rebuilt.
Cost = $50/covered farm.
Assumed 1/3 of all farms
covered.
I/ Supplied by EPA Program Offices.
2/ Cost per acre (1986$).
I/ Percent of indicated crop acres in the cornbelt likely to be
~~ affected.
D-ll
-------
Table D-6
Potential Impacts for Mississippi Cotton Soybean Farm I/
Variable Costs; First Year Impacts
Scenario Action Crop Cost 2/ Yield(%) Acres(%)3/
1-3 Dinoseb Cancellation
cotton 5.00 -1.5 24.1
soybeans 16.00 0 10.5
1-3 Toxaphene
cancellation
soybeans 6.8 0 1.2
1-3 Chlorodimeform -
cancellation of
yield enhancement
cotton 3.88 0 24
1-3 Alachlor-restricted
use
soybeans .50 0 10
1-3 Farm Worker safety
cotton .44 0 95
soybeans .65 0 85
1-2 Groundwater Plan I
& II: aldicarb
cotton 6.42 0 0.4
1 Groundwater Plan II:
alachlor
soybeans 1.60 0 1
2 Organophosphates
Plan II
cotton 4.15 0 1
2 Groundwater Plan II:
cynazine
cotton 5.00 6 1.3
3 Groundwater Plan III:
alachlor
soybeans 1.60 0 5
3 Organophosphates
Plan III
cotton 8.92 0 93.5
Continued...
D-12
-------
Table D-6 (continued)
Scenario Action Crop Cost 2/ Yield(%) Acres(%)3/
Groundwater Plan III:
aldicarb
cotton 6.42 0
Groundwater Plan III:
cyanazine
cotton 5.00 6
2.4
23.1
Fixed Costs:
Scenario
1-3
Action
1-3
1-3
Underground Storage Tank
Enclosed Cabs
SARA Title III,
Sections 302-304
Lead Ban
Impact
Insurance = $2,500/yr
Tank tightness test (2) =
$500. There are 2,099 UST
in the Delta distributed
over 132,000 farms.
Cost of Enclosing Cab =
$2,500. Assumed that 1/3
of all cabs must be
enclosed.
Cost = $50/covered farm.
Assumed 1/3 of all farms
covered.
Assumed impacted farm
incurred $1,000 cost to
rebuild a tractor, truck
or combine engine.
Assumed 1,150 tractors,
1,124 trucks and 303
combines in Delta need
to be rebuilt.
I/ Supplied by EPA Program Offices.
|/ Cost per acre (1986$).
3/ Percent of indicated crop acres in the cornbelt likely to be
affected.
D-13
-------
Table D-7
Potential Impacts for Kansas Wheat Cattle Farm I/
Variable Costs: First Year Impacts
Scenario Action Crop Cost 2/ Yield(%) Acres(%)3/
1-3 Alachlor-Restricted
Use
corn .50 0 37.1
soybeans .50 0 19
1-3 Farm Worker
corn .98 0 90
soybeans .65 0 75
wheat .45 0 80
1 Corn Rootworm
Insecticides Plan I
corn .70 0 35
2 Groundwater Plan II:
alachlor
corn 1.82 0 0.3
soybeans 1.60 0 0.1
sorghum 1.82 0 0.2
2 Groundwater Plan II:
atrazine
corn 18.41 -1 0.5
sorghum 18.41 -1 0.5
2 Groundwater Plan II:
cyanazine
corn 18.41 -1 0.2
2-3 Corn Rootworm
Insecticides Plan
II, III
corn -8.50 -16 58
2-3 Fungicides Plan II,
III
wheat -3.71 -44 0.7
3 Groundwater Plan III:
alachlor
corn 1.82 0 1.3
soybeans 1.60 0 0.5
sorghum 1.82 0 3.4
Continued...
D-14
-------
Scenario
Table D-7 (continued)
Action Crop Cost 2/ Yield(%) Acres(%)3/
Groundwater Plan III:
atrazine
corn 18.41
sorghum 18.41
Groundwater Plan III:
cyanazine
corn 18.41
sorghum 18.41
Fixed Costs;
Scenario
1-3
Action
Underground Storage Tanks
1-3
Enclosed Cabs
1-3
SARA Title III:
Sections 302-304
Lead Ban
-1 9.6
-1 11.4
-1 2.7
-1 0.10
Impact
Insurance = $2,500/yr
Tank Tightness Test =
$500/each (need 2)
There are 4,045 UST
in the Northern Plains
distributed over
196,000 farms.
Cost of Enclosing cab
= $2,500. Assumed 1/3
of all cabs must be
enclosed.
Cost = $50/covered
farms. Assumed 1/3
of all farms are
covered.
Assumed impacted farm
incurred $1,000 cost
to rebuild a tractor,
truck or combine
engine. Assumed
8,580 trucks, 8,380
tractors and 3,015
combines in the Northern
Plains would need to
be rebuilt.
I/ Supplied by EPA Program Offices.
2 Cost per acre (1986$).
3_/ Percent of indicated crop areas in the cornbelt likely to
be affected.
D-15
-------
Illinois Corn Soybean Farm: Scenario 1
Average Financial Condition
CO
oo
CD
E-3T
0~O
O C
C CO
— c/)
(A
CO
O
00
45 r-
40
35
30-
25
Average
Maximum
Base
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
CO
DC
0)
(/)
0)
CD
Q
0.45r-
0.4
0.35
0.3
0.25
0.2
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
Figure D-l. EPA impacts on net cash farm income and debt
asset ratio for a representative Illinois corn soybean farm
in average financial condition: Scenario 1
n-i fi
-------
Illinois Corn Soybean Farm: Scenario 1
Vulnerable Financial Condition
10,-
CO
00
CD
E-5T
OT3
O C
C CTJ
— tf)
en
CD
O
-5
-10-
-15
Average
Maximum
Base
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
g
"to
QC
0)
in
3
CD
Q
0.75
0.7
0.65
o.e|
o.ssl
1987
1989 1991 1993 1995
1988 1990 1992 1994
Year
1996
Figure D-2. EPA impacts on net cash farm income and debt
asset ratio for a representative Illinois corn soybean farm
in vulnerable financial condition: Scenario 1
D-17
-------
Illinois Corn Soybean Farm: Scenario 2
Average Financial Condition
Q
0.44 r-
0.4
0.36
0.32
0.28
0.24
0.2
1987 1989199119931995
1988 1990 1992 1994 1996
Year
Figure D-3. EPA impacts on net cash farm income and debt
asset ratio for a representative Illinois corn soybean farm
in average financial condition: Scenario 2
D-18
-------
Illinois Corn Soybean Farm: Scenario 2
Vulnerable Financial Condition
-------
Illinois Corn Soybean Farm: Scenario 3
Average Financial Condition
CO
at
«
Q
0.44
0.4
0.36
0.32
0.28
0.24-
0.2
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
Figure D-5. EPA impacts on net cash farm income and debt
asset ratio for a representative Illinois corn soybean farm
in average financial condition: Scenario 3
D-20
-------
Illinois Corn Soybean Farm: Scenario 3
Vulnerable Financial Condition
to
00
o>
-------
MS Cotton Soybean Farm: Scenario 1
Average Financial Condition
(O
CO
O)
Q
0.4,-
0.36
0.32
0.28
0.24
0.2
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
Figure D-7. EPA impacts on net cash farm income and debt
asset ratio for a representative Mississippi cotton soybean
farm in average financial condition: Scenario 1
D-22
-------
MS Cotton Soybean Farm: Scenario 1
Vulnerable Financial Condition
o>
Q
0.9r-
0.85
0.8
0.75
0.7
0.65
0.6
0.55
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
Figure D-8. EPA impacts on net cash farm income and debt
asset ratio for a representative Mississippi cotton soybean
farm in vulnerable financial condition: Scenario 1
D-23
-------
MS Cotton Soybean Farm: Scenario 2
Average Financial Condition
o>
0>
OT3
O C
C (0
— (0
0)
(0
O
09
100
90
80
70
60
50
40
30
20
Average
Maximum
Base
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
CO
CC
GO
(A
cn
-O
o>
Q
0.4r-
0.36
0.32
0.28
0.24
0.2
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
Figure D-9. EPA impacts on net cash farm income and debt
asset ratio for a representative Mississippi cotton soybean
farm in average financial condition: Scenario 2
D-24
-------
E-ST
81
C (0
— c/>
§1
"t
£
(O
(0
O
0)
z
MS Cotton Soybean Farm: Scenario 2
Vulnerable Financial Condition
80
70
60
50
40
30
20
10
Average
Maximum
Base
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
CO
CC
.O
-------
MS Cotton Soybean Farm: Scenario 3
Average Financial Condition
-------
MS Cotton Soybean Farm: Scenario 3
Vulnerable Financial Condition
8
o>
80
70
60
50
40
30
20
10
0
Average
Maximum
Base
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
OJ
QC
0)
(/)
(A
0)
Q
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
Figure D-12. EPA impacts on net cash farm income and debt
asset ratio for a representative Mississippi cotton soybean
farm in vulnerable financial condition: Scenario 3
D-27
-------
Kansas Wheat Cattle Farm: Scenairo 1
Average Financial Condition
co
CO
o>
-------
Kansas Wheat Cattle Farm: Scenario 1
Vulnerable Financial Condition
2r-
tO
00
O)
OT3
O C
C flj
— o
il
(A
(0
O
CB
z
-2
-4
-6
-8
•10
•12
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
.0
QC
i
-------
Kansas Wheat Cattle Farm: Scenairo 2
Average Financial Condition
CO
GO
O)
0)
E«T
OT3
O C
C (Q
CO
nj
O
GO
20
15
10
-5
-10
-15
Average
Maximum
Base
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
OJ
-------
Kansas Wheat Cattle Farm: Scenario 2
Vulnerable Financial Condition
CD
GO
O)
<0
E-W
8?
C (0
— w
§1
OJ
O
CD
5
0
-5
-10
-15
-20
-25
-30
-35
-40
Average
Maximum
Base
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
1.2,-
(0
DC
09
en
in
-------
Kansas Wheat Cattle Farm: Scenairo 3
Average Financial Condition
CO
GO
cn
0-
-5-
-10-
Average
Maximum
Base
-15
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
0.5r-
.g
ra
CC
(A
(/I
2
0)
Q
0.45
0.4
0.35
0.3
0.25
0.2
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
Figure D-17. EPA impacts on net cash farm income and debt
asset ratio for a representative Kansas wheat cattle farm
in average financial condition: Scenario 3
D-32
-------
Kansas Wheat Cattle Farm: Scenario 3
Vulnerable Financial Condition
CD
00
o>
0)
8 -a
c
C CO
— v)
il
(A
0)
O
5
0
-5
-10
-15
-20
-25
-30
-35
Average
Maximum
Base
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
1.2r-
o
"a
CC
«
(/)
w
1
0.8
0.6
.a
0)
a
0.4
0.2
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
Figure D-18. EPA impacts on net cash farm income and debt
asset ratio for a representative Kansas wheat cattle farm
in vulnerable financial condition: Scenario 3
D-33
-------
APPENDIX E
Income Budget Analysis and Results
By
Craig Simons
and
Roger Lloyd I/
I/ OPRA Incorporated
-------
Appendix E
Income Budget Analysis and Results
1.0 Budgeting Analysis
To more clearly assess regulatory impacts on an individual unit
of production for a given commodity and region, a budgeting analysis
was used. Baseline conditions were defined as net returns to
management and land for one acre of production prior to any
regulatory action. These conditions were calculated from regional
production cost and yield estimates and national price estimates.
Total production cost estimates were obtained from crop enterprise
budgets compiled by the USDA Cooperative Extension Service in
each appropriate state. Crop enterprise budgets typically catego-
rize total costs as variable and fixed. Variable costs are those
which vary according to the level of production. Fixed costs are
those which (in the short run) are unrelated to production levels.
Enterprise budgets vary in their treatment of expensing the cost
of owner provided inputs. For this study, the cost of owner
provided land and management were excluded. Any net returns
would then be attributable to these factors of production. To
the extent possible, all budgets were adjusted to be comparable.
In instances where a production region consisted of two or more
states (e.g., Idaho and Washington potatoes) a production weighted
total cost of production was calculated. All costs were adjusted
by the Index of Prices Paid by Farmers to reflect 1986 dollars.
The baseline-conditions were then adjusted by the cost and yield
impact estimates and the national price change estimates (developed
from the national price-quantity model and adjusted for regional
differences) to estimate the post-impact net returns per acre
for each regulatory scenario by region and crop. It is expected
net returns per acre will typically decrease from the influence
of regulatory impacts because of:
1. increased variable costs per acre of production, and
2. decreases in yield which lowers production and thus lowers
revenue per acre.
Ameliorating these negative effects on net revenue would be an
increase in price caused by a national decline in supply due to
decreased production nationwide.
Algebraically, the farm income budgeting model can be expressed as:
NR. = NR + dTR - dC.
1 O
E-l
-------
Since TR is dependent on price and production,
dTR = P.Q. - PoQQ.
Thus,
NR. = NRQ
P.Q. - PQQo - dC.
Where:
NR. = Net returns per acre of commodity production
after the regulatory scenario,
NR
Net returns per acre or commodity production
before the regulatory scenario,
dTR = change in total revenue,
dC = change in total costs;
P. = commodity price after the regulatory scenario,
P = commodity baseline price
Q. = commodity, production per acre after the regulatory
scenario, and
Q = commodity production per acre under baseline
conditions.
2.0 Data Inputs
Production cost estimates and baseline net returns for each
specialty crop production region (Table E-l) along with an estimate
of an average price and production (Appendix C, Table C-l) were
required to complete this analysis. Regional estimates of average
and maximum variable cost and yield changes associated with
environmental regulations for each specialty crop under each
scenario were provided by EPA. First year production cost and
yield changes are presented in Tables E-2 through E-5.
3.0 Model Results
Regulatory impacts on net returns which consider effects on product
price, quantity of production and production costs are presented
graphically in Figures E-l through E-9. Average and maximum
impacts are measured from a baseline net return (no regulatory
impact) for each of the specialty crops under the three policy
scenarios.
E-2
-------
Table E-l.
Baseline production costs and net returns
Crop/Region
Per acre production costs
Variable Fixed Total
costs costs costs
Baseline
net returns
(1986$)
Irish Potatoes
ID -
ND -
ME
WA
MN
983
332
762
.14
.90
.67
229
235
149
.22
.19
.88
1,212
568
912
.36
.09
.55
606
243
134
.00
.00
.00
Green Peas
WI
WA
Apples
WA
NY
MI
Peanuts
GA -
NC -
TX -
I/
AL
VA
OK
132
245
2,593
1,785
1,112
322
338
222
.35
.81
.41
.00
.70
.16
.65
.27
47
59
897
162
544
126
185
88
.20
.68
.66
.07
.44
.84
.98
.99
179
314
3,491
1,947
1,657
449
524
311
.55
.49
.07
.07
.14
.00
.63
.26
197
78
327
217
76
286
386
186
.00
.00
.00
.00
.00
.00
.00
.00
Caneberries
(Red Raspberries)
WA
OR
Tomatoes
FL (Fresh)
CA (Processing)
3,274.21 1,588.81 4,863.02
3,962.45 1,922.78 5,885.23
6,310.31
1,092.05
351.59 6,661.90
174.50 1,266.55
NA
NA
1,510.00
659.00
I/ Net returns are for additional peanuts. Net returns for
~ quota peanuts are $298, $444 and $206 for GA-AL, NC-VA and
TX-OK, respectively.
Source: Crop enterprise budgets from the individual states.
E-3
-------
Table E-2
Potential Impacts for Selected Apple Producers
Variable Cost; First Year Impact
Scenario Action
1-3 Farm Worker Safety
1 Organophosphates Plan
2 Organophosphates Plan
3 Organophosphates Plan
1 Groundwater Plan I
2 Groundwater Plan II
3 Groundwater Plan II
1 Fungicides Plan I
2 Fungicides Plan II
3 Fungicides Plan II
Region
WA
NY
MI
I WA
NY
MI
II WA
NY
MI
III WA
NY
MI
WA
NY
MI
WA
NY
MI
WA
NY
MI
WA
NY
MI
WA
NY
MI
WA
NY
MI
Cost I/
5.40
5.40
5.40
2.00
2.00
2.00
25.08
14.38
14.38
33.08
9.39
9.39
0.0
0.0
0.0
11.83
10.90
10.90
11.83
10.90
10.90
0.0
0.0
0.0
0.0
-13.06
-13.06
0.0
-13.06
-13.06
Yield(%)
0
0
0
0
0
0
0
0
0
-2
-2
-2
0
0
0
0
0
0
0
0
0
0
0
0
0
-20
-20
0
-20
-20
Acres
90
90
90
86
100
100
62
75
75
86
100
100
0
0
0
5
10
10
25
45
45
0
0
0
0
83
58
0
83
58
Continued,
E-4
-------
Table E-2 (continued)
Fixed costs:
Scenario
1-3
1-3
1-3
Action
SARA Title III
Section 302-304
Enclosed Cabs
Underground Storage Tanks
Lead Phasedown
Impact
Cost = $50/covered farm.
Assumed 1/3 of all farms
covered.
Cost = $2,500. Assumed
1/3 of all cabs must be
enclosed.
Some farms may incur costs
due to Underground Storage
Tank regulations, however,
due to the significant
amount of uncertainty as to
whether specialty crop farms
would have covered UST's.
These costs were not included.
Under a total ban of lead
in gasoline for agricultural
use, farmers having gasoline
powered tractors, combines,
and trucks may incur a cost
to rebuild the valves.
This cost would be approxi-
mately $1,000 for a combine
and a truck, and $750 for a
tractor. These costs were
not included in the budget
analyses for apple producers.
I/ Cost per acre (1986$)
E-5
-------
Table E-3
Potential Impacts for Selected Potato Producers
Variable Cost; First Year Impacts
Scenario Action
1-3 EDB Cancellation
1-3 Dinoseb Cancellation
1-3 Farm Worker Safety
1 Groundwater Plan I
2 Groundwater Plan II
3 Groundwater Plan III
1 Organophosphates Plan
2 Organophosphates Plan
3 Organophosphates Plan
1 Fungicides I
2 Fungicides II
Region
WA/ID
MN/ND
ME
WA/ID
MN/ND
ME
WA/ID
MN/ND
ME
WA/ID
MN/ND
ME
WA/ID
MN/ND
ME
WA/ID
MN/ND
ME
I WA/ID
MN/ND
ME
II WA/ID
MN/ND
ME
III WA/ID
MN/ND
ME
WA/ID
MN/ND
ME
WA/ID
MN/ND
ME
Cost I/
16.80
18.48
18.48
8.51
8.51
8.51
1.43
1.43
1.43
0.00
10.00
11.00
0.00
10.00
11.00
39.13
10.00
11.00
1.00
1.00
1.00
5.88
5.88
5.88
7.00
7.00
7.00
0.00
0.00
0.00
8.81
6.61
11.05
Yield(%)
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-8
-8
-8
0
0
0
0
0
0
Acres(%)
2.2
1.1
1.1
50.0
50.0
50.0
90.0
90.0
90.0
0.0
3.5
1.9
0.0
3.5
1.9
12.4
14.6
7.5
74.0
74.0
74.0
68.0
68.0
68.0
74.0
74.0
74.0
0.0
0.0
0.0
7.0
54.0
80.0
Continued..
E-6
-------
Table E-3 (continued)
Fungicides III
WA/ID
MN/ND
ME
-0.60
-0.45
-0.75
-8
-8
-8
12.0
80.0
80.0
Fixed costs:
Scenario
1-3
1-3
1-3
Action
SARA Title III
Section 302-304
Enclosed Cabs
Underground Storage Tanks
Lead Phasedown
Impact
Cost = $50/covered farm.
Assumed 1/3 of all farms
covered.
Cost = $2,500. Assumed
1/3 of all cabs must be
enclosed.
Some farms may incur costs
due to Underground Storage
Tank, regulations, however,
due to the significant
amount of uncertainty as to
whether specialty crop farms
would have covered UST's.
These costs were not included.
Under a total ban of lead
in gasoline for agricultural
use, farmers having gasoline
powered tractors, combines,
and trucks may incur a cost
to rebuild the valves.
This cost would be approxi-
mately $1,000 for a combine
and a truck, and $750 for a
tractor. These costs were
not included in the budget
analyses for potato producers.
I/ Cost per acre (1986$)
E-7
-------
Table E-4
Potential Impacts for Selected Pea Producers
Variable Costs; First Year Impacts
Scenario Action
1-3 Dinoseb Cancellation
1-3 Farm Worker Safety
1 Organophosphates Plan I
2 Organophosphates Plan II
3 Organophosphates Plan III
Region Cost I/ Yield(%) Acres(%)
WA
WI
WA
WI
WA
WI
WA
WI
WA
WI
10.40
0.00
0.86
0.86
1.00
1.00
2.92
2.92
3.08
3.08
0
0
0
0
0
0
0
0
0
0
75
0
90
90
30
30
30
30
35
35
Fixed costs:
Scenario
1-3
1-3
1-3
Action
SARA Title III
Section 302-304
Enclosed Cabs
Underground Storage Tanks
Impact
Cost = $50/covered farm.
Assumed 1/3 of all farms
covered.
Cost = $2,500. Assumed
1/3 of all cabs must be
enclosed.
Some farms may incur costs
due to Underground Storage
Tank regulations, however,
due to the significant
amount of uncertainty as to
whether specialty crop farms
would have covered UST's.
These costs were not included.
Continued..
E-8
-------
Table E-4 (continued)
Lead Phasedown
Under a total ban of lead
in gasoline for agricultural
use, farmers having gasoline
powered tractors/ combines,
and trucks may incur a cost
to rebuild the valves.
This cost would be approxi-
mately $1,000 for a combine
and a truck, and $750 for a
tractor. These costs were
not included in the budget
analyses for pea producers.
I/ Cost per acre (1986$)
E-9
-------
Table E-5
Potential Impacts for Selected Tomato Producers
Variable Costs; First Year Impacts
Scenario
1-3
1-3
1
2
3
Action
EDB Cancellation
Farm Worker Safety
Fungicides Plan I
Fungicides Plan II
Fungicides Plan III
Region
CA
FL
CA
FL
CA
FL
CA
FL
CA
FL
Cost
22.65
22.65
7.50
7.50
0.00
0.00
1.50
20.93
-3.39
-20.34
0
0
0
0
0
0
0
0
-20
-20
2.9
2.9
90.0
90.0
0.0
0.0
9.0
77.0
25.0
98.0
Fixed costs:
Scenario
1-3
1-3
1-3
Action
SARA Title III
Section 302-304
Enclosed Cabs
Underground Storage Tanks
Impact
Cost = $50/covered farm.
Assumed 1/3 of all farms
covered.
Cost = $2,500. Assumed
1/3 of all cabs must be
enclosed.
Some farms may incur costs
due to Underground Storage
Tank regulations/ however,
due to the significant
amount of uncertainty as to
whether specialty crop farms
would have covered UST's.
These costs were not included.
Continued..
E-10
-------
Table E-5 (continued)
Lead Phasedown
Under a total ban of lead
in gasoline for agricultural
use, farmers having gasoline
powered tractors, combines,
and trucks may incur a cost
to rebuild the valves.
This cost would be approxi-
mately $1,000 for a combine
and a truck, and $750 for a
tractor. These costs were
not included in the budget
analyses for tomato producers.
I/ Cost per acre (1986$)
E-ll
-------
Impacts on WA Apple Net Returns
330|-
325-
320-
315
Impacts on NY Apple Net Returns
220,-
216-
210
1887 1888 1881 1893 1895
1888 IBM i&82 is9< IBM
Yeai
20!
1887 1868 1881 1893 1895
1888 1890 1882 1894 1896
Yew
I
i—«
ro
Impacts on Ml Apple Net Returns
70-
Average
Maximum
Base
1967 1989 1881 1993 1895
1868 1890 1892 1894 1896
Year
Figure E-l. Scenario 1. reBulatory impacts on apple net returns
-------
Impacts on WA/ID Potato Net Returns
Impacts on MN/ND Potato Net Returns
2
g
610,-
605 -
600 -
695
i
245r-
240-
235 -
230
225
-TB87 1989 1991 1993 1995
1968 1990 1992 1994 1996
Yeai
220
1087 1989 1991 1993 199S
IB88 1990 1992 1994 1996
Yflitf
Impacts on ME Potato Net Returns
MOr-
U
Aveiaga
Maximum
Basa
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Yea/
Figure E-2. Scenario 1, regulatory impacts on potato net returns
-------
Impacts on CA Tomato Net Returns
670.-
660 -
e&o
640
1987 IMS 1991 1993 1995
1090 1992 1994 1990
Yea/
Impacts on FL Tomato Net Returns
1520.-
1510 -
1500-
1490
fc fc fc h h -*
1987 1989 1991 1993 1995
1990 1992 1994 1996
Vaar
Avuidge
Maximum
Baso
Impacts on Wl Pea Net Returns
205r-
200 -
195
1901
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
i
I
dk
a
t
Impacts on WA Pea Net Returns
K -
70
1987 1989 1991 1993 1995
1980 1990 1992 1994 1996
Year
Figure E-3. Scenario 1, regulatory impacts on tomato and pea net returns
-------
Impacts on WA Apple Net Returns
Impacts on NY Apple Net Returns
1987
1089 1991 1993 1995'
1990 1992 1994 1998
Yew
250r-
200-
160-
100-
60-
0 -
•SO -
•100 -
•ISO -
•200
1987 1989 1991 1993 1995
1968 1990 1092 1094 I09B
Yaw
I
i
Impacts on Ml Apple Net Returns
ISOr-
100-
50 -
0 -
•50-
•100 -
•150-
200-
•250
Average
Bast
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Yew
Fiyiire E-4. Scenario 2, regulatory impacts on apple net returns
-------
Impacts on WA/ID Potalo Net Returns
Impacts on MN/ND Potato Net Returns
615,-
610
605-
600
595 -
590
i
1
245,-
240
235
230 -
225
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
220
1687 1989199119931995
1988 1990 1992 1994 19-JB
Year
Yuar
Impacts on ME Potato Net Returns
Avewje
MaikiHiro
Base
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Yeai
Figure E-5. Scenario 2 repulnrnru imm
-------
Impacts on CA Tomato Net Returns
670,-
660 -
650 -
Impacts on FL Tomato Net Returns
1520r-
1510-
1500 -
1400
Year
107 1989 1001 1693 1895
toaa loao 1092 1094
Yuaf
Average
Maximum
Base
Impacts on Wl Pea Net Returns
200,-
195-
190
1987 1989 1991 1993 1995
1988 1090 1992 1994 1096
Ydar
i
Impacts on WA Pea Net Returns
80r-
70 -
fiS
1987 1989 1991 1993 1995
1988 1090 1992 1994 1006
Yaar
Figure E-6. Scenario 2, regulatory impacts on tomato and pea net returns
-------
Impacts on WA Apple Net Returns
Impacts on NY Apple Net Returns
250r-
200-
150-
100-
60-
0 -
1987 1989 1991 1993 1995
1988 1990 1992 1994 199ft
YMT
•100-
-160 -
•200
1987 1989 1991 1993 1995
I9B8 1990 1992 1994 1996
Y«ar
I
!-•
00
Impacts on Ml Apple Net Returns
I00|-
so -
0-
-50 -
•100 -
-150-
200-
-250-
•300
Mainuim
Base
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Yeai
Figure li-7. Scenario 3, regulatory impacts on apple net returns
-------
Impacts on WA/ID Potato Net Returns
Impacts on MN/ND Potato Net Returns
700,-
650-
600 -
550
500
450
UJ
a
u
S
I
255r-
240
225
210-
195-
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
1987 1989 1991 1993 1995
1988 1990 1992 1994 1998
l
•-•
10
Year
Year
Impacts on ME Potato Net Returns
!
Avaiaga
Maximum
Base
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
Figure E-3. Scenario 3, regulatory Impacts on potato net returns
-------
Impacts on CA Tomato Net Returns
Impacts on FL Tomato Net Returns
1987 1989 1991 1993 1995
1988 1990 1992 1994 1998
Average
Maximum
Base
1907 19B9 1991 1993 1995
1988 1990 1992 1994 1996
Year
i
ro
o
Impacts on Wl Pea Net Returns
Impacts on WA Pea Net Returns
205,-
200-
195
1987 1989 1991 1993 1995
1988 I9a0 1992 1994 1996
u
5
80,-
75-
70-
65
1987 1989 1991 1993 1995
1988 1990 1992 1994 1996
Year
Yeai
Figure E-9. Scenario 3, regulatory impacts on tomato and pea net returns
-------
APPENDIX F
Data Problems and Assumptions
By
Robert Torla I/
I/ Office of Pesticide Programs, U.S. Environmental Protection
Agency
-------
Appendix F
Data Problems and Assumptions
The agricultural sector study relied on a wide range of information
sources of varying quality. This section summarizes the data
sources and briefly discusses the limitations of the data.
1.0 Basic Crop Production Information
Basic crop production data was obtained from annual publications
of the USDA National Agricultural Statistics Service (NASS) where
data were available. For apples and caneberries there was not a
consistent data source. Production and price information for
apples was obtained from USDA, while information on acres harvested
was obtained from the Bureau of Census. Different estimation
techniques were used in these two sources and they were collected
in different time periods. However, apples are a relatively slow
growing perennial crop, so differences in time frames of a few
years are probably not particularly important. There were limited
caneberry data available in statistical publications from some
important states. The production data sources used in this study
are listed below.
A. Crop Production, Annual Summary for relevant years,
National Agricultural Statistics Board, USDA.
B. Vegetables, Annual Summary for relevant years, National
Agricultural Statistics Board, USDA.
C. 1982 Census of Agriculture, Bureau of Census, USDC.
D. Non Citrus Fruits and Nuts, Annual Summary for relevant
years, National Agricultural Statistics Board, USDA.
E. Various state annual reports of agricultural statistics
for relevant years.
2.0 Time Frames for Actions
We attempted to project the year in which actions might take
place and, for past actions, relied on historical information as
to when actions actually occurred. Projections for future actions
were based on an examination of likely dates for actions to take
place.
For all pesticide specific actions we projected that impacts
would dissipate evenly over a seven year period as users adjusted
their practices and new pest control products became available.
F-l
-------
There is some question regarding the accuracy of this assumption.
Clearly, if new technologies exist to ameliorate the impacts of a
regulatory action, they would tend to be registered (if necessary
and they meet the criteria) and adopted within a seven year
period. In addition, the cancellation of a pesticide would
create some incentive to replace it. However, there is no
certainty that such new technologies exist or if they do not
currently exist, would be developed, registered, marketed, and
adopted within a seven year time frame. The incentive to develop
and market new technologies would tend to be greater for the
major field crops, where large potential markets exist. There
are also some data which suggest that new pesticides would be
more expensive than older ones which have been cancelled.
3.0 Pesticide Usage Data
Quality of pesticide usage data vary widely. There are adequate
regional (multi state) level usage data for most major field
crops (corn, cotton, sorghum, wheat, and soybeans). Pesticide
usage data for barley, oats, and hay are sporadic, with the most
recent data being from the 1970's. Therefore, usage estimates
developed by the registrants were used for these crops. In
general, the usage data bases for major field crops are designed
to be statistically reliable at the 10 percent level for the
sample region. USDA has on occasion, collected statistically
reliable state level data for selected major field crops in
selected states.
Specialty crop pesticide usage data are highly erratic. USDA
last collected pesticide usage data for tomatoes, green peas,
apples, and potatoes in the 1970's. Latest USDA peanut pesticide
usage data are for 1982 and there are no data for caneberries.
State collected pesticide usage data were utilized when available.
However, there are no regular periodic state usage surveys.
California collects and reports all pesticide usage for restricted
use materials and commercial applicators. This results in usage
data which should be very reliable for restricted use materials;
but are of questionable usefulness for unrestricted use materials.
The Pesticide Program has access to some proprietary pesticide
usage estimates for major field crops and selected specialty
crops. However, the reliability of these estimates is largely
unknown. For major pesticides on major crops, these estimates
agree with available data collected in statistically designed
surveys. However, for minor pesticides and specialty crops, usage
estimates obtained from proprietary sources are often inconsistent
with available statistically designed surveys.
Analysis of the proposed pesticides in groundwater actions required
projections of pesticide use at the county level. However, there
are no public data collected to be statistically reliable at the
F-2
-------
county level. Data provided by a contractor was used to predict
pesticide usage at the county level. However, this data base is
composed of information drawn from available reports and expert
opinion or local Cooperative Extension Service personnel and 'is not
based on a statistically valid sample. The Federal government
does not have data to check the reliability of any of these
estimates.
4.0 Comparative Efficacy and Costs of Alternative Controls
Inputs developed and cleared by the program offices were used
for past and near actions. The rigor of these analyses varied
considerably. In some instances, potential yield impacts were
not investigated and a zero yield loss was assumed. In other
situations, rigorous analyses of the magnitude of possible yield
losses were available.
In general, available pesticide crop trials are not designed to
generate statistically reliable estimates of the differences in
yields among substitute chemicals. The objective of the crop
trials is to demonstrate that the pesticide provides some control
of the pest and not to reveal how pesticides compare with each
other.
For actions expected to take place further in the future (generally
beyond about one year), various sources of information were
employed. The following reports generated by, or for, and cleared
by the program offices were used:
Preliminary Benefit Analysis of EOB
Preliminary Benefit Analysis of Toxaphene
Preliminary Benefit Analysis of EPN
Preliminary Benefit Analysis of 2,4,5-T
Preliminary Benefit Analysis of Silvex
Preliminary Benefit Analysis of Carbon Tetrachloride
Regulatory Impact Analysis: Worker Protection Standards for
Agricultural Pesticides
Regulatory Impact Analysis in Support of Rulemaking Under
Sections 302, 303 and 304 of Title III of the Superfund
Amendments and Reauthorization Act of 1986
Regulatory Impact Analysis of Proposed Technical Standards for
Underground Storage Tanks
F-3
-------
Regulatory Impact Analysis of Proposed Financial Responsi-
bility Requirements for Underground Storage Tanks Containing
Petroleum
Preliminary Benefit Analysis of Dinocap
Preliminary Benefit Analysis of Chlordimeform
Preliminary Benefit Analysis of Ethyl Parathion
Preliminary Benefit Analysis of Aldicarb
Abbreviated Benefit Analysis of Dinoseb.
4.1 Corn and Soybeans
Publications from the USDA Commodity Assessment of Pesticide Use
on Corn and Soybeans and Potential Bans of Corn and Soybean
Pesticides, by Craig Osteen and Fred Kuchler USDA, ERS, Agricul-
tural Economic Report Number 546 as well as some unpublished
supporting commodity assessment data information (made available
by the USDA) provided comparative efficacy for corn and soybeans.
This provided a consistent data base which appears reasonable for
the actions proposed for the future. The commodity assessment data
base was constructed by obtaining expert opinion of estimates of
product cost and yield effects due to losses of pesticides. The
USDA has not updated this report and the estimates are somewhat
dated. In some cases, the cost of alternatives provided in the
Commodity Assessment was not appropriate for this analysis. In
these cases the Commodity Assessment was supplemented with
information from the Economic Analysis Branch (EAB) price files.
Efficacy data for corn and soybeans is probably the most reliable
of all crops considered in this analysis.
Concerns about groundwater contamination were assumed to result
in the cancellation of both alachlor and the triazines in selected
areas. In reality alachlor and the triazines are partial sub-
stitutes; however, the Commodity Assessment never considered the
question of the loss of both alachlor and the triazines. In the
absence of any information on how production costs and yields would
change under the cancellation of both alachlor and the triazines,
we used the commodity assessment data, which indicate the efficacy
information associated with the cancellation of each one, assuming
the other remains on the market. Logic indicates that the simple
addition of impacts probably underestimated the impact of cancel-
ling both, but the degree of underestimation is unknown.
F-4
-------
4.2 Remaining Major Field Crops (Wheat/ Cotton, Sorghum, Barley,
Oats, Hay)
4.2.1 Wheat, Barley, Oats
There was only one significant future action that affected wheat.
Yield change estimates developed for EPA by the registrants were
used. There was no significant Agency review of these estimates
(Benefits Estimates for Maneb, Pennwalt Corporation, December
1987 & Response of the Rohm and Haas Company to the Special
Review for EBDC Fungicides, Rohm and Haas Company, October 1987).
4.2.2 Cotton
EPA policy actions assumed in this analysis have potentially
significant affects on cotton production. Estimates of impacts
were developed rather rapidly using judgments of EAB staff members
Possible actions are in areas where a number of alternative
controls exist. Therefore, it is likely that the estimates
developed are reasonable.
4.2.3 Sorghum
No efficacy data were available for sorghum. For herbicides it
was assumed that the cost and percent yield changes would be the
same as those for corn since the crops, pesticides, and pest
spectra are similar. This could be a significant limitation
since sorghum tends to be grown in drier and warmer areas than
corn. The actual performance of the herbicides could be different
under these conditions. The impacts of other actions were
developed internally based on judgement. Other pesticides are
of limited importance in the production of sorghum, therefore,
our estimates are probably within reason even though not well
documented.
4.2.4 Hay
Possible actions were very limited. Only a small portion of the
acres planted are impacted (less than one percent).
4.3 Specialty Crops
4.3.1 Peanuts
Most information for impact estimates for alachlor and aldicarb
(groundwater) were available from reports previously cleared by
the program office (see above). We estimated portions of acres
that would be affected based on knowledge of the soils where the
crop is grown. Industry estimates of fungicide cost and yield
impacts were used, although they had not been subject to internal
review. Insecticide cost and yield effects were developed intern-
ally based on information on alternatives and possible target
F-5
-------
pests. Although we feel reasonably comfortable with estimates
for the individual actions/ we feel very uncomfortable with the
simple addition as a means of aggregating yield impacts across
chemicals. This problem, in addition to lack of information on
supply elasticities for peanuts, prevented us from providing a
complete analysis of the impact of EPA actions on peanut growers.
4.3.2 Apples
Cost and yield impact information provided by industry was utilized
for fungicides. Cost information for other pesticides used on
apples was estimated internally based on knowledge of registered
materials and labeled target pests. Yield impacts were estimated
internally based on limited information on yield impacts from
selected pesticides.
4.3.3 Potatoes
Aldicarb (pesticide- in groundwater) information was available
from an existing Agency study. Fungicide information was available
from an industry report submitted to the Agency. Remaining impacts
were estimated internally as they were for apples.
4.3.4 'Green Peas and Tomatoes
Pesticide industry estimates were available for fungicides.
Only limited information (primarily materials registered and
target pests) was available to estimate cost and yield impacts
associated with other future actions. We had some limited
estimates from a contract publication (with no knowledge of how
these estimates were obtained) on most common target pests and
usage of various materials. Yield and cost impacts were estimated
internally with little or no foundation, other than past experience
on larger crops.
4.3.5 Caneberries
Virtually no information was available except for pesticide
registrations and target pests on labels. This was the situation
for most past actions as well as possible future actions. The
following informational reports were used:
Abbreviated Benefit Analysis of Dinoseb (Since the dinoseb
action was still in litigation at the time inputs were
developed for the study, estimates of impacts as developed
for the regulatory action were used for this analysis).
Preliminary Benefit Analysis of Aldicarb
Preliminary Benefit Analysis of Alachlor
Regulatory Impact Analysis: Registration fees under FIFRA
F-6
-------
Regulatory Impact Analysis: Data requirements for Registering
Pesticides
Benefit Estimates for Maneb, Pennwalt Corporation, December
1987
Response of the Rohm and Haas Company to the Special Review
for EBDC Fungicides, Rohm and Haas Company, October 1987.
5.0 Elasticities
Price elasticities used for the major field crops were those
contained within the simulation model (AGSIM). While the estimated
elasticities may be subject to criticism, they were generated in
a consistent manner within the same model. Price elasticities
for the specialty crops were short-run farm level elasticities
and were obtained from whatever reasonable sources were available.
These estimates of supply and demand elasticities may have been
estimated from different data bases using different techniques.
5.1 Apples
Obtained elasticities of supply from a USDA/ERS report "An
Econometric Model of the U.S. Apple Market," June 1985. Elasticity
of demand estimates from K. Huang, USDA/ERS, 1985.
5.2 Caneberries
Estimates of elasticities were not found.
5.3 Peanuts
Discussions with economists familiar with peanut production
(both with USDA and in major peanut production areas) indicated
that there are no reasonably reliable peanut elasticity of supply
estimates available. Elasticities of demand are from K. Huang,
USDA/ERS. However, these are questionable due to the nature of
perceived demand for domestic peanuts produced under quota and
additional peanuts (peanuts for export and oil).
5.4 Peas, Potatoes and Tomatoes
Elasticities of demand were obtained from K. Huang, USDA/ERS,
1985. Elasticities of supply for peas were obtained from Ascari and
Gummings, International Economic Review, 1977. Elasticity of
supply for potatoes was obtained from unpublished work by G.
Zepp, USDA/ERS, 1987. Elasticity of supply for tomatoes was
obtained from Churn and Just, Giannini Monograph, 1978.
F-7
-------
APPENDIX G
Cumulative Probability Cost Distribution
By
Terry Dinan I/
I/ Office of Policy Analysis, U.S. Environmental Protection Agency
-------
Appendix G
Cumulative Probability Cost Distribution
Since we are simultaneously examining the impact of several EPA
policies, a fundamental issue that had to be determined was:
how do we define an "impacted" farmer? For example, Illinois
corn soybean farmers may be affected by the cancellation of several
different pesticides, may incur insurance costs if they have an
underground storage tank that meets certain criteria, and may
incur an expense to rebuild their tractor engine if all lead is
banned from gasoline and they have a leaded gasoline tractor.
How many of these potential costs do we assume that the "impacted"
farmer incurs? For each producer we examine two alternative sets
of impacts:
* A Maximum Impact Case; In this case it is assumed that
the producer is affected by every regulation that may
possibly affect a producer of that type.
* An Average Impact Case; In this case it is assumed
that the producer experiences the average impact of
producers of that type - e.g., if 10% of all producers
of a given type experienced a cost of $1,000, we would
use a cost of $100 ($1,000 x 0.10) for the average
impact case.
Examining these two cases, however, only provides two snapshots
of possible impacts without providing the full picture of how
cost and yield impacts are likely to be distributed across
producers. To provide more insight into the likely distribution
of these initial cost and yield impacts, we constructed a cumula-
tive probability cost curve for each representative farm in
average financial position. The following example demonstrates
what these cumulative probability cost curves reveal.
Suppose a given farmer may be affected by three possible regula-
tions, each having the following associated cost and probability
of affecting a given producer:
Probability Probability
Regulation Cost of Impact of No Impact
A $100 .30 .70
B $200 .20 .80
C $300 .10 .90
Provided the probabilities of incurring the costs of the three
regulations are independent, the possible set of outcomes and
associated costs and probabilities may be defined as:
G-l
-------
Regulations
Affected by;
A
B
c
NONE
A,B
B,C
A,C
ALL
Cost
$100
$200
$300
$0
$300
$500
$400
$600
Probability I/
.216
.126
.056
.504
.054
.014
.024
.006
I/ Note the probability of being impacted by Regulation A =
P(A) x P(NB) x P(NC), where P(A) = the probability of being
affected by regulation A, and P(NB), P(NC) = the probability
of not being affected by B and C, respectively.
By ranking these possible outcomes in order of cost, and adding
up the associated probabilities, we can arrive at the following
cumulative probabilities:
Regulations
Affected by;
NONE
A
B
C
A,B
A,C
B,C
ALL
Cumulative
Probability
.504
.720
.846
.902
.956
.980
.994
1.00
Then, plotting the cost on the x-axis and the cumulative probabil-
ity on the y-axis, we can use this information to generate the
following cumulative probability cost curve:
Cumulative Probability Cost Curve
0.9
0.8
0.7
0.6
0.5
0.4
OJ
0.2
0.1
0
100 200 300 400 SOO GOO
Cost
G-2
-------
This cost curve indicates the probability of incurring a cost less
than or equal to a given level. For example, it indicates that
any given farmer has a probability of .846 of incurring a cost
that is less than or equal to $200.
To shed insight into the probability that the farms examined in
this report would actually incur any given level of cost, we
generated a cumulative probability cost curve for each of the
representative farms in average financial position. In the above
example, all of the costs were assumed to be independent. In
reality, however, this may not be the case. For example, farmers
who use a certain type of pesticide on their corn may very likely
be using the same pesticide on their soybeans, if the pesticide
is used on a certain pest that is found on both corn and soybeans.
In generating the cumulative probability cost curve for each
representative farm, we tried to account for the correlation
among different costs. The assumptions we used for each represen-
tative farm are outlined below:
Illinois corn soybean farm assumptions:
1. If a farmer is using any chemical, then he incurs Farm
Worker Safety Costs.
2. If a farmer is using alachlor on his soybeans, then he
is using alachlor on his corn.
3. If a farmer is using a corn rootworm insecticide on his
corn, then he is using a triazine on his corn.
4. If a farmer is using alachlor on his corn, then he is
using a triazine on his corn.
Mississippi cotton soybean farm assumptions:
1. If a farmer is using any chemical, then he incurs Farm
Worker Safety Costs.
2. If a farmer is using dinoseb on his soybeans, then he
is using dinoseb on his corn.
Kansas wheat cattle farm assumptions:
1. If a farmer is using any chemical, then he incurs Farm
Worker Safety Costs.
2. If a farmer is using alachlor on his soybeans, then he
is using alachlor on his corn.
3. If a farmer is using a triazine on his corn, then he is
using a triazine on his sorghum.
4. If a farmer is using alachlor on his corn, then he is
using a triazine on his corn.
Incorporating these assumptions into the method described in the
above example, we generated a cumulative probability cost curve
for each representative farm in each scenario (Figures G-l through
G-5). Any given point on the curve may be interpreted as the
G-3
-------
ILLINOIS CORN SOYBEAN FARM: SCENARIO 1
5
3
AVERAGE FINANCIAL POSITION
9.8 10 12
(Thousands)
DISCOUNTED PRESENT COST (1987-1998)
1 4
18
Maximum
Impact
Case
Figure G-la.
Scenario 1, cumulative probability cost curve for the representative
Illinois corn soybean farm in average financial condition
2
3
ILLINOIS CORN SOYBEAN FARM: SCENARIO 2
0.1 -
AVERAGE FINANCIAL POSITION
20 40
(Thousands)
DISCOUNTED PRESENT COST (1987-1998)
Av«ragt
Impact
Case
Maxim*
Impact
Case
Figure G-lb. Scenario 2, cumulative probability cost curve for the representative
Illinois corn soybean farm in average financial condition
G-4
-------
I
a
£
ILLINOIS CORN SOYBEAN FARM: SCENARIO 3
0.9 -
0.1 -
AVERAGE FINANCIAL POSITION
20 4O
(Thousomla)
DISCOUNTED PRESENT COST (1987-1998)
Avcraqt
[•Met
Cist
SO
[moact
Case
:igure G-2a. Scenario 3, cumulative probability cost curve for the representative
Illinois corn soybean farm in average financial condition
2
o
£
MS COTTON SOYBEAN FARM: SCENARIO 1
AVERAGE FINANCIAL POSITION
1
0.9
0.3
0.7
0.8
0.* -
0.3 -
02 -
0.1
20
60
4O
(Thousonds)
DISCOUNTED PRESENT COST (1987-1996)
Average
[•pact
Cast
80
Maximum
Imoact
Case
Figure G-2b. Scenario 1, cumulative probability cost curve for the representative
Mississippi cotton soybean farm in average financial condition
G-5
-------
MS COTTON SOYBEAN FARM: SCENARIO 2
AVERAGE FINANCIAL CONOfTlCN
*O
80
80
(Thousands)
DISCOUNTED PRESENT COST (1987-1998)
I«W«ct
100
120
.Maximum
Impact
Case
Figure G-3a. Scenario 2, cumulative probability cost curve for the representative
Mississippi cotton soybean farm in average financial condition
MS COTTON SOYBEAN FARM: SCENARIO 3
i -i
AVERAGE FINANCIAL POSTHON
2O
«O
80
Average
I"Pact
Cast
80
(Thousands)
DISCOUNTED PRESENT COST (1987-1998)
100
Maximu
Case
Figure G-3b.
Scenario 3, cumulative probability cost curve for the representative
Mississippi cotton soybean farm in average financial condition
G-6
-------
KANbAb WHt/J UAI I i_L fAKiV;
AVERAGE FINANCIAL POSITION
0
I
Q.
\
I
1 -
0.9 -
o.a -
0.7 -
0.9 -
0.3 -
0.* -
0.3 -
0.2 -
0.1 -
0 -
(
a — mxoBfr^*""^
J
j
f
go0
r*
i
i
i
i
i
i
1 IT 1 1 1 1 1 1 1 1 1 1 1 r i i i
J 2 * 8 fl 10 12 »* '8 1
(Thousands)
DISCOUNTED PRESENT COST (1987-1998)
Avvrtq* Max in
[npict ["KM!
Cue Case
a
turn
t
Figure G-Aa.
Scenario 1, cumulative probability cost curve for the representative
Kansas wheat cattle farm in average financial condition
KANSAS WHEAT CATTLE FARM: SCENARIO 2
AVERAGC FINANCIAL POSITION
0.9 -
o.a -
0.7 -
0.9 -
0.3 -
0.*
0.3
0.2
0.1
3
Average
Immct
Case
—1-
10
-r
—r— —i—
20 30
(Thousands)
DISCOUNTED PRESENT COST (1987-1998)
Maximum
Imoact
Case
Figure G-4b.
Scenario 2, cumulative probability cost curve for the representative
Kansas wheat cattle farm in average financial condition
G-7
-------
KANSAS WHEAT CATTLE FARM: SCENARIO 3
>
5
£
o
Q.
s
2
U
Average
Impact
Cast
AVERAGE FINANCIAL POSITION
10
20 30
(Thousands)
DISCOUNTED PRESENT COST (19B7-1996)
Maximum
Impact
Case
Figure G-5a.
Scenario 3, cumulative probability cost curve for the representative
Kansas wheat cattle farm in average financial condition
G-8
-------
probability that the representative farm will incur a cost equal
to or less than a given level. For example, the curve in Figure
G-la indicates that the representative Illinois corn soybean farm
in Scenario 1 has a .50 probability of incurring a discounted
present value of cost and yield impacts (1987-1996) of less than
or equal to $2", 000. The discounted present value of cost and
yield impacts corresponding to the average and maximum impact
cases are indicated on each curve.
If all Illinois corn soybean farms had the same number of acres
of each crop as the representative farm, Figure G-la could be
interpreted as the percent of farms likely to incur cost and
yield impacts less than or equal to a given level. Since farms
will vary in the number of crop acres that they plant, their
present discounted value of impacts under any particular combina-
tion of regulations will vary from the representative farm.
(Recall that the representative farm does not truly represent all
farms but is only a composite of farms of a given type.) These
curves, therefore, are only meant to provide some insight into
the distribution of cost and yield impacts for farms of a given
type, but do not represent accurate cost and yield impacts for
any particular farm (other than the average farm), or the true
distribution of impacts across farms.
G-9
-------
APPENDIX H
Recommendations for Acquiring Better Pesticide Usage Data
By
Susan Slotnick I/
I/ Office of Standards and Regulations, U.S. Environmental
Protection Agency
-------
Appendix H
Recommendations for Acquiring Better Pesticide Usage Data
In this agricultural sector study, the lack of current and reliable
pesticide usage data has limited the ability to accurately assess
the economic impact of EPA actions, particularly on the specialty
crops. The quality of the usage data used in the report is
described in Appendix F. To summarize, data for the major crops
were usually adequate only at the regional level. For small-area
crops, the data were old and/or of unknown statistical validity.
For no crop was information available nationwide at the county
level which is the minimum level of disaggregation needed for
measuring the impact of ground water regulatory actions. The
gaps identified in Appendix F could affect the study results
because the measurement of economic impacts of EPA actions depends
on the cost and yield effects of pesticide cancellation which in
turn depend on usage data.
The agricultural sector study is only one example of the many EPA
analyses that depend on basic pesticide data for accurate estima-
tion of economic and other effects of pesticide regulation.
Because this study is an excellent illustration of the difficulty
the data limitations present, it is an opportunity to discuss
those limitations, their consequences for economic and risk
analyses of pesticide use, and what can be done to improve the
situation.
As seen in the agricultural sector study, two types of basic
pesticide data are fundamental to assessing a pesticide's economic
importance: performance and usage. A current project in the
Office of Pesticide Programs directly addresses the incompleteness
of the performance data by strengthening data requirements placed
on pesticide manufacturers. For that reason, the discussion here
is limited to usage data, defined roughly as the amount a par-
ticular pesticide and its alternatives are used on a crop, how
many acres are treated with each pesticide, in which locations, at
what rate, and by what methods. For the sake of brevity, the
focus is on agricultural pesticide use, although data problems
exist with nonagricultural use as well.
1.0 Why Pesticide Usage Data are Important
The agricultural sector study is just one of several recent
special analyses relying on pesticide usage data. Some of the
special studies could be of far-reaching importance for future
pesticide use, for example, preparation for the Agency's En-
dangered Species Program and targeting of water wells for the
national groundwater monitoring program. For risk/benefit analyses
on individual pesticides and for other regular pesticide assess-
ments (e.g., exemptions for local use), usage data and performance
data form the foundation upon which scientists and economists
build their quantitative estimates of a pesticide's importance.
H-l
-------
Without complete information, often the case with small area
crops, analysts must rely on educated guesses, adding uncertainty
to their final conclusions. In the recent case of the herbicide
dinoseb, usage information on alternatives was not readily
available and analysts had inadequate time to gather it. This
lack of data contributed to a successful legal challenge by
growers of some small crops, causing EPA to exempt those crops
from the suspension decision already made. Furthermore, usage
data are an integral part of exposure assessments, which in turn
play a key role in deciding whether a pesticide is placed in
Special Review.
2.0 Current State of Usage Data
The agricultural pesticide usage data currently available are
very uneven in quality and coverage. For the major crops such as
corn, soybeans, cotton, and wheat, current survey data are
available from USDA and private sources and are likely to be
collected periodically in the foreseeable future. Information on
major crops falls short of OPP's needs because it often excludes
minor producing areas and are often not disaggregated to a small
enough geographic level. Considerably greater problems occur
with small-area crops, for example, there has been .no publicly-
available survey of pesticide use on citrus since 1977. For the
specialty crops studied in this report as well as the whole
spectrum of fruits, vegetables, and other crops, usage data are
rarely what they need to be: current, reliable, disaggregated at
least to the state level, and publicly available.
3.0 Recommendations for Acquiring Better Data
The Benefits and Use Division (BUD) of the Office of Pesticide
Programs has made a concerted effort to upgrade its usage data,
but is often met with budgetary constraints. BUD recently
estimated that it would cost $3 million to acquire adequate
survey usage data on crops and nonagricultural sites of importance
to OPP. That expenditure would be needed every three or five years.
However, the Office of Pesticide Programs is not the only organi-
zation needing pesticide usage data, and the list is growing
because of heightened concern about pesticide health and environ-
mental effects, for example groundwater contamination. Other
organizations which recently used pesticide usage data are:
Department of Agriculture,
EPA Office of Drinking Water, Non-Point Source Branch,
EPA Office of Ground Water Protection,
individual registrants,
Food and Drug Administration,
National Agricultural Chemicals Association,
H-2
-------
* state environmental, water quality, and public health
programs, and
* U.S. Geological Service, Water Resources Division.
For some of the options that follow, a cost-sharing arrangement
between EPA an~d~other interested organizations could make the
data acquisition far more affordable.
Below are possible options for generating better pesticide usage
data. Each has different costs and benefits.
1. Conduct a set of jointly-funded periodic surveys of
pesticide users
Each set would cover certain sites, such as major
crops, small area crops, crops in certain regions,
pesticide-intensive crops in areas of groundwater
vulnerability, or nonagricultural sites. A different
group of sponsoring organizations would fund each set.
Fees would be charged to non-sponsoring users.
2. Set up cost-sharing between EPA and states to conduct
surveys
This is a more limited version of option #1. In order
to receive EPA funds, states would have to design the
surveys to meet certain specifications so the data.
would fit EPA's needs. This might be the most efficient
approach for small crops.
3. "Socialize" private data collection services
These services currently poll farmers nationwide on
pesticide usage. EPA and other interested parties
could contract to completely fund the data collection,
in order to be able to control the survey methods and
site coverage, and to ensure the data is public.
4. Attach questions to existing USDA surveys currently
used for other purposes
This is already being done to a limited extent; the
new questions would be much more detailed.
5. Attach questions to the U.S. Census of Agriculture
The Census currently asks farmers questions on all
crops as well as usage of pesticide in broad categories.
To be useful for most EPA analyses, additional questions
would be added that are detailed at the active ingredient
level.
H-3
-------
6. Require data from registrants
Registrants are required to generate pesticide toxicity
and performance data to support pesticide registrations.
If usage data were also required, the cost to the
government would be lower than with other options,
though there could be problems with confidentiality.
7. A combination of the above
Existing USDA surveys cover only a subset of the crops
relevant to EPA. Pesticide usage questions could be
attached to those surveys while data on remaining
crops could be collected jointly by a consortium as in
#1 and #3.
An interagency committee composed of EPA, USDA, FDA, and DOI,
meets on occasion to share pesticide usage data. To date, there
has been no joint funding of data. Working through the committee,
the OPP Benefits and Use Division and the OPPE Office of Policy
Analysis have begun an initiative to acquire better data.
4.0 Summary
There is a clear need for more detailed, precise estimates of
pesticide usage, both agricultural and non-agricultural. Recent
renewed interest in pesticide-related environmental and health
problems has increased the number of organizations needing such
information. Because there are many hundreds of different
pesticidal active ingredients and hundreds of different crops and
nonagricultural sites across the country, acquiring high quality
information on a regular basis is expensive. Yet without it, the
accuracy of economic valuation of pesticides is uncertain. If
such accuracy is deemed important enough, some increased effort
will be needed to acquire the necessary data.
There are several ways to generate better usage data. Detailed
questions could be attached to existing surveys designed for other
purposes, EPA could require the data from registrants, or a consor-
tium of interested private, federal, and state organizations
could be formed to share the costs of new surveys. Since there
is a wide variety of use sites, a different arrangement might be
made for different types of sites.
Each approach would differ from a cost-benefit standpoint. To
the extent EPA can pool resources with other users of pesticide
data, costs can be lowered. The benefit of better data will be
greater efficiency in the assessments of pesticide use, a higher
quality of analysis, and subsequently, more informed decisions on
pesticide regulation.
H-4
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