Directory UMM :Data Elmu:jurnal:A:Agricultural Systems:Vol63.Issue3.Mar2000:
Agricultural Systems 63 (2000) 147±159
www.elsevier.com/locate/agsy
Assessing regional impacts of change: linking
economic and environmental models
J.D. Attwood a, B. McCarl b,*, Chi-Chung Chen b,
B.R. Eddleman c, B. Nayda d, R. Srinivasan e
a
USDA, Natural Resources Conservation Service, 808 E. Blacklands Road, Temple, TX 76502, USA
Department of Agricultural Economics, Texas A&M University, College Station, TX 77843-2124, USA
c
Corpus Christi Research Center, Texas Agricultural Experiment Station, Route 2 Box 589, Corpus Chrisi, TX
78406-9704, USA
d
Capital One Financial, 2980 Fairview Park, Falls Church, VA 22042-1091, USA
e
Blackland Research Center, Texas Agricultural Experiment Station, TX, USA
b
Received 30 June 1999; received in revised form 5 December 1999; accepted 13 December 1999
Abstract
Increasingly, natural resource policy makers and program administrators are requiring that
analysis of proposed changes include estimates of both environmental and economic implications. That requirement poses a diculty for researchers since the spatial scale of models
used for environmental analysis and for economic analysis are structured dierently. In this
paper we show how the diering spatial scales can be reconciled in a national analysis involving an agricultural model with state- and county-level-based geographical boundaries and a
watershed model involving watershed boundaries. This type of modeling system integration
has been done in analysis of single or multiple watershed-level issues, but our paper is the ®rst
to show a method for a national-level analysis involving state- and substate-level economic
results and small watershed environmental results. The procedures and results are shown for a
national cropland erosion control policy and for the release by one state experiment station of
improved crop varieties. Published by Elsevier Science Ltd.
Keywords: Resource policy; Resource modeling; Agriculture; Watershed; Environment; Economic;
Mathematical programming; Agriculture Sector Model; SWAT model
* Corresponding author. Tel.: +1-409-845-1706; fax: +1-409-845-7504.
E-mail address: mccarl@tamu.edu (B. McCarl).
0308-521X/00/$ - see front matter Published by Elsevier Science Ltd.
PII: S0308-521X(99)00077-3
148
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
1. Introduction
Technological developments, agricultural policy alterations, and environmental
regulation revisions are changing the agricultural production and processing environment. Agricultural scientists face demands to project the consequences of these
forces. Agricultural economists have typically responded by creating economic costbene®t analyses. However, today's heightened environmental awareness coupled
with emerging geographic information system (GIS) and environmental modeling
capabilities have raised the demand for linked regional economic/environmental
appraisals (REEA). This study develops and illustrates procedures for the simultaneous analysis of economic and environmental impacts of change across the economy and the water resource.
Construction of a broadly based REEA can be dicult. Typically, agricultural
data and economic models are de®ned by political boundaries, but environmental data and models employ physical or coordinate-based boundaries. Furthermore, sectoral-level economic models are almost always more aggregate in their
geographic representation than are environmental models. In the last few years,
developments in GIS, and cartographic data have made possible large-scale detailed
analyses of agricultural land use and management (ESRI, 1995). We develop a way
to link a national economic agricultural sector model with an environmental watershed hydrology model. This work extends REEA methodology in three ways.
1. Most national REEA analyses limit the environmental eects addressed to
``edge of ®eld, bottom of root zone'' estimates of soil, nutrient, and pesticide
movements, but we expand the analysis to cover all national surface water
bodies.
2. National analyses using watershed hydrology models have generally not considered changes in land use and crop management. Our procedures employ an
agricultural sector model to develop scenario-speci®c economic, crop mix and
crop management implications, then put those results into the hydrologic
model yielding a simultaneous REEA. Again, national scope is the contribution as REEAs have been done for small geographic regions by synchronizing
the spatial scope of models.
3. A method is developed for consistently converting national crop mixes across
regions primarily based on political boundaries (generally states) into crop mix
alteration estimates for smaller physically de®ned regions, in this case watersheds.
As a demonstration we present results from analyses of soil erosion policy changes
and crop variety releases.
2. Analytical background
The analysis links together two models Ð ASM and SWAT. ASM, the Agricultural Sector Model (Chang et al., 1992; McCarl et al., 1992), provides estimates
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
149
of national and regional producer income, production costs, resource use and
values, exports, imports, commodity processing, welfare, crop management and crop
mix under a policy/technology scenario. SWAT, the Soil and Water Assessment
Tool (Arnold et al., 1998), provides estimates of watershed-level ¯ows, a key factor
in which is regional crop mix and management.
2.1. ASM
Conceptually, ASM (Chang et al., 1992; McCarl et al., 1998) is a mathematical
programming, US agricultural sector model which simulates market equilibrium
eects for resources (land, water, labor) and commodities (domestic use, imports
and exports of primary and secondary or processed items). ASM simulates US
agricultural production and resource supply at the 63 region levels (regions are
states except that CA, IA, IL, IN, OH, and TX are subdivided). Supply of cropland, pasture, rangelands, hired labor, family labor, groundwater and surface are
represented with price-dependent supply functions. Production and markets
are depicted for 44 primary commodities (22 crop and 22 livestock) and 35 processed secondary commodities (16 crop, 13 livestock, and six feed); and region crop
mixes are conditioned by 20 years of historical proportional crop mixes following
McCarl (1982).
Parameters adjusted when ASM is applied for a policy or technology scenario
simulation include crop yields, and input usage. Applications of ASM include
Chang et al. (1992), Adams et al. (1986, 1995), and Chang et al. (1994). ASM provides scenario-dependent results on crop mix, choice of irrigation methods, and in
some cases fertilization, tillage, and rotations employed.
2.2. SWAT
SWAT (Arnold et al., 1998; Srinivasan et al., 1998) simulates the eects of agricultural and other watershed management on water ¯ows and quality. The SWAT
input data include historical weather, natural vegetation, political boundaries,
reservoir management, crop mix, agricultural practices, land use, soils, watershed
boundaries, stream networks, soil properties, stream ¯ows, and crop budgets. Usages of SWAT include Srinivasan and Arnold's (1994) study on water management
and available ¯ows; Srinivasan et al.'s (1998) study on sedimentation and water
storage capacity; and Rosenthal et al.'s (1993) study on crop selection, irrigation
practices, and water ¯ow.
2.3. Geographic dierences between ASM and SWAT
ASM and SWAT subdivide the landscape in dierent ways. Consider their representations of Texas. ASM includes eight Texas substate regions which are groups of
counties. SWAT includes 204 watersheds as de®ned by eight-digit hydrologic cataloging units (HCU) which are partly or wholly in Texas (only 192 of these HCUs are
agriculturally related). The intersection between counties and HCU boundaries
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J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
results in 961 HCU parts of counties and/or 303 HCU parts of ASM regions for
Texas (USGS).
3. Linking ASM and SWAT
Conceptually an ASM crop mix and management solution can be passed to
SWAT for estimation of water ¯ow and quality impacts. To do this the crop mix
alterations in ASM must be dissagreggated to the HCUs. In Texas this requires that
crop mix alterations in eight ASM Texas substate regions be disaggregated to 192
HCUs. This will be done by creating a county-level data set consistent with the
ASM results and then re-aggregating that data to the HCU level.
Development of a HCU or county-level counterpart to the ASM crop mix would
not be necessary if we could use counties or HCUs as the ASM spatial speci®cation.
However, not only would such a model be very large but developing/maintaining
production budget, crop mix and resource data for such a scale would be a monumental undertaking. Thus, we run ASM at a more aggregate level and reduce the
solution crop mixes to the county level because counties are about the same size as
HCUs, boundaries are somewhat coincident, and county-level crop acreage data is
available. Then we reaggregate to HCUs.
ASM crop mixes are constrained to be a convex combination of regional, historical crop mixes following McCarl (1982) and Onal and McCarl (1991). However,
while this forces a consistency with mixes for the 63 ASM subregions applying the
subregional mix to the contained counties is not straight forward. The counties each
have dierent resource endowments and suitability for particular crops. One cannot
mechanically allocate the crops based on the proportional allocation of land across
the counties. We needed a procedure which would allocate crops to counties as
consistently as possible with observed mixes in the counties.
3.1. Exploitable data for developing county crop mixes sets from ASM results
The Census of Agriculture (US Bureau of Census, 1994), USDA National
Resources Inventory (NRI) and County Crops Data (US Department of Agriculture, 1996) all contain partial and/or periodic data sets on dry and irrigated area
of speci®c crops by county. These were used in conjunction with USDA Agricultural
Statistics to develop as complete as possible of a series of county-level irrigated and
dryland crop mixes.
3.2. Developing county-level crop mix solutions consistent with ASM region solutions
In disaggregating the ASM solution regional crop mixes to the county level, we
tried to make the resultant crop mixes as consistent as possible with historically
observed crop mixes. After several false starts where we used mechanical formulas
and heuristics to allocate the mixes we turned to a mathematical programming
approach. We set up a model where the fundamental choice variable was the area of
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
151
a particular crop allocated to an irrigation status in a county. We constrained this
choice so it best matched history and the ASM solution, but also allowed deviations
from the historical observations which we minimized in a linear programming
model. The constraints used and allowed deviations were
C1
C2
C3
C4
C5
C6
C7
The area allocated to each crop by irrigation status across all counties in an
ASM subregion had to equal the totals that were in the ASM solution for
that subregion.
The cropped area in each county could not exceed the maximum cropped
area in the historic data, but deviation above the maximum was allowed by
including a deviation variable.
The cropped area in a county could generally not be less than the minimum
cropped area in the historic data but we allowed deviation below that
minimum.
The cropped area irrigated in a county could not exceed from the maximum
irrigated area observed but again deviation was allowed.
The area of an individual crop in each county could be no greater than the
maximum observed area of that crop with deviation allowed.
The area of a crop in a county could be no less than the minimum amount
of land devoted to that crop with deviation allowed.
The ASM model followed the McCarl (1982) crop mix procedure and
chose a crop mix for regions corresponding to a particular year in history.
The area then allocated in a county was constrained to minimally deviate
from an interpolated county crop mix developed by interpolation between
the periodic NRI and census data using Agricultural Statistics for the whole
state. Both positive and negative deviations from that mix were allowed.
The model objective function (Appendix provides a mathematical de®nition)
minimizes the summed deviations across the counties: (1) above the maximum total
cropped area observed from C2; (2) below the minimum total cropped area (C3); (3)
above the maximum total irrigated area (C4); (4) above the maximum area of a crop
ever observed (C5); (5) below the minimum crop area ever observed (C6); and (6)
above or below the county area allocation de®ned using the historic crop mixes (C7).
Solution of the mathematical program gave a land allocation which was quite
similar to historical allocations. However, resultant maps showed sharp distinctions
at the ASM subregional borders. For example, when mapping the Oklahoma and
Texas border, one could see a quite dierent allocation pattern in the Oklahoma
border counties in comparison to adjacent Texas counties. Examination of the data
showed that land allocation in adjacent counties deviated from historical land use
interrelationships between the counties. This then led us to add yet one more constraint (C8) and associated deviation variables.
C8
The ratio of crop area relative to the area of the same crop in all adjacent
counties was required to equal the historical average ratio between the
counties with positive and negative deviations allowed.
152
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
Use of the model with this feature led to realistic county-level cropping patterns
consistent with the ASM solution and which were judged satisfactory for use in
SWAT and other predicted land use mapping exercises.
3.3. Reallocation of county crop mixes to watersheds
The ®nal step once the county crop mixes were obtained was to reallocate those
crop mixes into watershed HCUs. This was done by using the proportion of each
county's area in each of the watersheds based on geographic data used in setting up
the SWAT model.
4. An illustration: development of a national area allocation
Suppose we illustrate the results from the county land allocation model at
the national level. This involves taking the ASM 63 region solution and allocating it
to more than 3000 counties for 15 crops according to the model in the Appendix.
For economy of journal space, we only display results for irrigated corn. Fig. 1
contains several maps of county-level irrigated corn area relative to total cropped
area. Fig. 1a shows a map where the proportional share in the 63 ASM regions was
assumed to apply to each contained county. Fig. 1c shows the 1992 observed area
allocation. Fig. 1b shows the results from the land allocation model. Note the procedure allows us to generate a relatively consistent acreage allocation.
5. A second illustration: regional hydrological results consistent with economic
results
Suppose we illustrate the procedure with the resultant SWAT tie in. We did this in
an REEA examination of ®ve crop varieties developed in Texas (Clarke, 1997).
Adoption of these varieties in¯uences production and prices, both in and outside of
Texas resulting in dierent economic and environmental outcomes.
5.1. Type of impacts estimated
The ASM provides producers' and consumers' impact estimates for Texas substate regions, other adopting regions in the USA, the US in total, foreigners, and the
world in total. ASM also produces regional results on labor, land, and water use,
and expenditures on input use.
From the SWAT model, we get maps showing changes in precipitation runo and
nitrogen, phosphorus, potassium, and sediment loss with the runo. We only present the Texas results since the study was a prototype assessment and only involved
SWAT runs in Texas (note SWAT has to be run individually for each of the 192
Texas HCUs). We also only very brie¯y discuss the economic results since as there
have been a lot of previous economic assessments of this type; the focus of the paper
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
153
Fig. 1. Percentage of county irrigated area devoted to irrigated corn under dierent allocation rules and
in National Resources Inventory (NRI) data. (a) Subregional area distribution; (b) distribution using full
model; (c) actual area allocation in NRI data for 1992.
154
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
is on the linkage of the two models. Those wishing more detail on the economic
results may obtain the detailed report by Clarke (1997).
5.2. Economic results
Table 1 shows that society as a whole, foreign producers and consumers, all US
consumers, and some US producer groups bene®t from the new varieties. However,
three adopting regions of Texas, several other adopting regions of the US show
producer losses. Texas producers as a group gain $72.8 million while Texas consumers gain $15.3 million. Producers' losses in the rest of the USA are sucient to
largely oset consumer gains in those regions; however, for the entire USA the net
total gain is $43.3 million. For the total world, consumers' gains of $437.6 million
exceed producers' losses of $169.9 million.
Table 2 shows that except for some small regional decreases, the crop varieties
stimulate an increase in use of total cropland, irrigated land, and irrigation water.
For the USA as a whole, cropland use decreases by less than 1% of currently cultivated cropland. Table 2 shows that pasture use decreases in Texas, but only by
about 3.5% of the cropland increase for the state. Pasture use increases in other
adopting regions and in the USA as a whole.
5.3. SWAT environmental results
The SWAT model was run for the Texas watersheds over 30 years of observed
weather data (1960±89) with and without the new crop varieties. The results we
Table 1
Economic bene®t from adoption of new crop varieties developed by Texas Agricultural Experiment Station
Region
Consumers' (million $)
Producers' (million $)
Total (million $)
Texas high plains
Texas rolling plains
Texas central blacklands
Texas east
Texas Edwards Plateau
Texas Coastal Bend
Texas south
Texas Trans Pecos
Texas Ð all regions
Delta statesa
Great Plains statesb
Total Ð other adopt regions
Rest of USA
Total USA
Foreign
Total world
0.8
0.7
6.3
1.5
0.3
4.0
1.0
0.7
15.3
13.1
11.9
25.0
176.6
216.9
220.7
437.6
25.5
12.9
9.0
ÿ0.2
ÿ0.6
22.2
4.6
ÿ0.6
72.8
ÿ30.4
ÿ18.4
ÿ48.8
ÿ197.6
ÿ173.9
3.7
ÿ169.9
26.3
13.6
15.3
1.3
ÿ0.3
26.2
5.6
0.1
88.1
ÿ17.3
ÿ6.5
ÿ23.8
ÿ21.0
43.3
224.4
267.7
a
Rice-producing areas of Arkansas, Louisiana, Mississippi, Missouri.
Wheat-producing areas of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota,
Wyoming, and cotton-producing areas of Oklahoma.
b
155
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
Table 2
Changes in resource use from adoption of new crop varieties developed by the Texas Agricultural
Experiment Station
Region
Water
(1000 m3)
Irrigated land
(1000 ha)
Cropland
(1000 ha)
Pasture land
(1000 ha)
Texas high plains
Texas rolling plains
Texas central blacklands
Texas east
Texas Edwards Plateau
Texas Coastal Bend
Texas south
Texas Trans Pecos
Texas Ð all regions
Delta statesa
Great Plains statesb
Total Ð other adopt regions
Rest of USA
Total USA
398 182.06
8511.33
32 195.02
3330.52
0.0
142 842.26
27 137.56
ÿ6290.98
605 907.77
ÿ454 307.48
ÿ113 237.65
567 545.13
101 025.75
139 388.39
84.74
1.78
5.79
1.42
0.0
ÿ13.52
5.91
ÿ1.01
85.11
ÿ92.96
ÿ57.43
ÿ150.39
ÿ4.82
ÿ70.09
146.58
74.63
35.41
ÿ11.13
ÿ4.41
53.02
6.72
ÿ1.13
299.68
ÿ79.69
85.27
9.63
ÿ392.28
ÿ82.96
ÿ2.67
5.22
2.47
1.66
ÿ6.48
0.53
ÿ12.59
1.66
ÿ10.20
16.75
106.64
123.39
166.86
280.05
a
Rice-producing areas of Arkansas, Louisiana, Mississippi, Missouri.
Wheat-producing areas of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota,
Wyoming, and cotton-producing areas of Oklahoma.
b
highlight involve changes between the with and without new varieties cases in
surface water: sediment load, water running in from croplands (hereafter called
runo), nitrogen load, and phosphorous load.
Not all of the 192 HCUs exhibited a large change in environmental health indicators. In order to avoid false signals, all HCUs that had less than 1% cropland or
less than 0.1 kg of N or P loss per hectare were not considered for mapping. This left
65 HCUs for which change information is displayed.
We chose cases with greater than 5% change to display. In turn 35 HCUs had a
change >5% in water borne sediment (Fig. 2a). Of these, 10 HCUs exhibited
lower sediment loss and 25 yielded greater sediment loads. In terms of runo, only
three basins changed by more than 5% in three basins (Fig. 2b). One experienced
decrease in stream ¯ow while two had more water reaching the stream. Nitrogen
results (Fig. 2c) show 12 basins had greater than 5% reductions in loading while 18
showed increases. Phosphorus results were similar (Fig. 2d) with 18 basins exhibiting
decreases and 24 exhibiting increases.
6. Concluding comments
The methodology developed herein allows one to disaggregate economic model
results for use in geographically speci®c environmental simulators. Often environmental results are of at least equal policy maker concern as the economic impacts.
156
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
Fig. 2. Changes in Texas environmental health indicators as projected by SWAT. (a) Sediment loss;
(b) runo; (c) nitrogen losses; (d) phosphorus losses.
Acknowledgments
Seniority of authorship is shared by the ®rst two authors. The other authors all
made equal contributions. This paper arose out of eorts supported by a USDA,
NRCS Cooperative agreement with the Texas Agricultural Experiment Station
(TAES) and out of the USAID grant #PCE-G-00-97-00051-00, Impact Methods to
Predict and Assess Contributions of Technology.
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
157
Appendix. Land Allocation Model
The fundamental variable in the model is Landp,c,i which depicts the allocation of
cropland in county p for crop c of irrigation type i. As mentioned above this allocation is constrained by eight relationships.
The area allocated to counties within an ASM region for each crop and irrigation
type must equal the area found within the ASM solution (asmacre) for that region
(counties in region s are identi®ed by p(s)):
X
Landp;c;i asmacres;c;i for relevant s; c; i:
C1
pp s
Total cropped area across all crops in a county is less than the maximum amount
observed historically (maxuse). But use above the maximum is allowed through the
deviation variable (Maxusedev).
XX
Landp;c;i ÿ Maxusedev
p 4maxusep for relevant p
c
C2
i
The area in a county across all crops is greater than the minimum amount
observed historically (minuse), but we allow deviation below the minimum through
the deviation variable (Minusedev).
XX
Landp;c;i Minusedevÿ
p 5minusep for relevant p
c
C3
i
The irrigated area in a county is no more than the maximum amount observed
historically (maxirracre), but we allow deviation above the maximum through the
deviation variable (Irrdev).
X
Landp;c;i ÿ Irrdev
C4
p 4maxirracrep for all p and i irrigation
c
The area used in a county for a crop can be no more than the maximum amount
observed (maxcrop) but we allow a deviation above the maximum through the
deviation variable (Maxcropdev).
X
Landp;c;i ÿ Maxcropdev
C5
p;c 4maxcropp;c for relevant p; c
i
The area in a county for a crop can be no more than the minimum amount
observed historically (mincrop), but we allow a deviation below the minimum
through the deviation variable (Mincropdev).
X
Landp;c;i Mincropdevÿ
C6
p;c 5mincropp;c for relevant p; c
i
158
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
The area allocation must exhibit minimum deviation from the mix developed by
the historic crop mix approach using the percentage utilization of the historical crop
mix solution from ASM (asmmix) with deviations allowed through the variables
Asmmixdev.
X
Landp;c;i Asmmixdevÿ
p;c ÿ Asmmixdevp;c
i
asmmixp;c for relevant p; c
C7
The interrelationship between land allocation by crop in a county and all adjacent
counties minimally deviates from the historic proportional area share by crop in this
county (p) divided by proportional share in the adjacent county (pl) with deviation
variables Adjdev.
X Landp;c;i
i
totlandp
ÿ
X Landp1;c;i
i
totlandp1
adjratp;p1;c Adjdevÿ
p;p1;c ÿ Adjdevp;p1;c
0 for relevant p; p1; c
C8
The objective function minimizes the sum of all deviation variables. This includes
deviations: (1) above maximum and below minimum cropland usage [Maxusedev
from (C2) and Minusedev from (C3)]; (2) above maximum observed irrigated area
[Irrdev from (C4)]; (3) above maximim and below minimum area for a crop [Maxcropdev, Mincropdev from (C5) and (C6)]; (4) above and below ASM extrapolated
mix [Asmmixdev from (C7)]; and (5) above and below the historic proportion of
area in adjacent counties (Adjdev) from (C8). The equation is as follows:
min
X
XX
X
ÿ
Maxusedev
Irrdevp
Maxcropdev
p Minusedevp
p;c
p
p
Mincropdevÿ
p;c
p
c
XX
ÿ
Asmmixdev
p;c Asmmixdevp;c
p
c
XXX
ÿ
Adjdev
p;p1;c Adjdevp;p1;c
p
p1
c
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www.elsevier.com/locate/agsy
Assessing regional impacts of change: linking
economic and environmental models
J.D. Attwood a, B. McCarl b,*, Chi-Chung Chen b,
B.R. Eddleman c, B. Nayda d, R. Srinivasan e
a
USDA, Natural Resources Conservation Service, 808 E. Blacklands Road, Temple, TX 76502, USA
Department of Agricultural Economics, Texas A&M University, College Station, TX 77843-2124, USA
c
Corpus Christi Research Center, Texas Agricultural Experiment Station, Route 2 Box 589, Corpus Chrisi, TX
78406-9704, USA
d
Capital One Financial, 2980 Fairview Park, Falls Church, VA 22042-1091, USA
e
Blackland Research Center, Texas Agricultural Experiment Station, TX, USA
b
Received 30 June 1999; received in revised form 5 December 1999; accepted 13 December 1999
Abstract
Increasingly, natural resource policy makers and program administrators are requiring that
analysis of proposed changes include estimates of both environmental and economic implications. That requirement poses a diculty for researchers since the spatial scale of models
used for environmental analysis and for economic analysis are structured dierently. In this
paper we show how the diering spatial scales can be reconciled in a national analysis involving an agricultural model with state- and county-level-based geographical boundaries and a
watershed model involving watershed boundaries. This type of modeling system integration
has been done in analysis of single or multiple watershed-level issues, but our paper is the ®rst
to show a method for a national-level analysis involving state- and substate-level economic
results and small watershed environmental results. The procedures and results are shown for a
national cropland erosion control policy and for the release by one state experiment station of
improved crop varieties. Published by Elsevier Science Ltd.
Keywords: Resource policy; Resource modeling; Agriculture; Watershed; Environment; Economic;
Mathematical programming; Agriculture Sector Model; SWAT model
* Corresponding author. Tel.: +1-409-845-1706; fax: +1-409-845-7504.
E-mail address: mccarl@tamu.edu (B. McCarl).
0308-521X/00/$ - see front matter Published by Elsevier Science Ltd.
PII: S0308-521X(99)00077-3
148
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
1. Introduction
Technological developments, agricultural policy alterations, and environmental
regulation revisions are changing the agricultural production and processing environment. Agricultural scientists face demands to project the consequences of these
forces. Agricultural economists have typically responded by creating economic costbene®t analyses. However, today's heightened environmental awareness coupled
with emerging geographic information system (GIS) and environmental modeling
capabilities have raised the demand for linked regional economic/environmental
appraisals (REEA). This study develops and illustrates procedures for the simultaneous analysis of economic and environmental impacts of change across the economy and the water resource.
Construction of a broadly based REEA can be dicult. Typically, agricultural
data and economic models are de®ned by political boundaries, but environmental data and models employ physical or coordinate-based boundaries. Furthermore, sectoral-level economic models are almost always more aggregate in their
geographic representation than are environmental models. In the last few years,
developments in GIS, and cartographic data have made possible large-scale detailed
analyses of agricultural land use and management (ESRI, 1995). We develop a way
to link a national economic agricultural sector model with an environmental watershed hydrology model. This work extends REEA methodology in three ways.
1. Most national REEA analyses limit the environmental eects addressed to
``edge of ®eld, bottom of root zone'' estimates of soil, nutrient, and pesticide
movements, but we expand the analysis to cover all national surface water
bodies.
2. National analyses using watershed hydrology models have generally not considered changes in land use and crop management. Our procedures employ an
agricultural sector model to develop scenario-speci®c economic, crop mix and
crop management implications, then put those results into the hydrologic
model yielding a simultaneous REEA. Again, national scope is the contribution as REEAs have been done for small geographic regions by synchronizing
the spatial scope of models.
3. A method is developed for consistently converting national crop mixes across
regions primarily based on political boundaries (generally states) into crop mix
alteration estimates for smaller physically de®ned regions, in this case watersheds.
As a demonstration we present results from analyses of soil erosion policy changes
and crop variety releases.
2. Analytical background
The analysis links together two models Ð ASM and SWAT. ASM, the Agricultural Sector Model (Chang et al., 1992; McCarl et al., 1992), provides estimates
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
149
of national and regional producer income, production costs, resource use and
values, exports, imports, commodity processing, welfare, crop management and crop
mix under a policy/technology scenario. SWAT, the Soil and Water Assessment
Tool (Arnold et al., 1998), provides estimates of watershed-level ¯ows, a key factor
in which is regional crop mix and management.
2.1. ASM
Conceptually, ASM (Chang et al., 1992; McCarl et al., 1998) is a mathematical
programming, US agricultural sector model which simulates market equilibrium
eects for resources (land, water, labor) and commodities (domestic use, imports
and exports of primary and secondary or processed items). ASM simulates US
agricultural production and resource supply at the 63 region levels (regions are
states except that CA, IA, IL, IN, OH, and TX are subdivided). Supply of cropland, pasture, rangelands, hired labor, family labor, groundwater and surface are
represented with price-dependent supply functions. Production and markets
are depicted for 44 primary commodities (22 crop and 22 livestock) and 35 processed secondary commodities (16 crop, 13 livestock, and six feed); and region crop
mixes are conditioned by 20 years of historical proportional crop mixes following
McCarl (1982).
Parameters adjusted when ASM is applied for a policy or technology scenario
simulation include crop yields, and input usage. Applications of ASM include
Chang et al. (1992), Adams et al. (1986, 1995), and Chang et al. (1994). ASM provides scenario-dependent results on crop mix, choice of irrigation methods, and in
some cases fertilization, tillage, and rotations employed.
2.2. SWAT
SWAT (Arnold et al., 1998; Srinivasan et al., 1998) simulates the eects of agricultural and other watershed management on water ¯ows and quality. The SWAT
input data include historical weather, natural vegetation, political boundaries,
reservoir management, crop mix, agricultural practices, land use, soils, watershed
boundaries, stream networks, soil properties, stream ¯ows, and crop budgets. Usages of SWAT include Srinivasan and Arnold's (1994) study on water management
and available ¯ows; Srinivasan et al.'s (1998) study on sedimentation and water
storage capacity; and Rosenthal et al.'s (1993) study on crop selection, irrigation
practices, and water ¯ow.
2.3. Geographic dierences between ASM and SWAT
ASM and SWAT subdivide the landscape in dierent ways. Consider their representations of Texas. ASM includes eight Texas substate regions which are groups of
counties. SWAT includes 204 watersheds as de®ned by eight-digit hydrologic cataloging units (HCU) which are partly or wholly in Texas (only 192 of these HCUs are
agriculturally related). The intersection between counties and HCU boundaries
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J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
results in 961 HCU parts of counties and/or 303 HCU parts of ASM regions for
Texas (USGS).
3. Linking ASM and SWAT
Conceptually an ASM crop mix and management solution can be passed to
SWAT for estimation of water ¯ow and quality impacts. To do this the crop mix
alterations in ASM must be dissagreggated to the HCUs. In Texas this requires that
crop mix alterations in eight ASM Texas substate regions be disaggregated to 192
HCUs. This will be done by creating a county-level data set consistent with the
ASM results and then re-aggregating that data to the HCU level.
Development of a HCU or county-level counterpart to the ASM crop mix would
not be necessary if we could use counties or HCUs as the ASM spatial speci®cation.
However, not only would such a model be very large but developing/maintaining
production budget, crop mix and resource data for such a scale would be a monumental undertaking. Thus, we run ASM at a more aggregate level and reduce the
solution crop mixes to the county level because counties are about the same size as
HCUs, boundaries are somewhat coincident, and county-level crop acreage data is
available. Then we reaggregate to HCUs.
ASM crop mixes are constrained to be a convex combination of regional, historical crop mixes following McCarl (1982) and Onal and McCarl (1991). However,
while this forces a consistency with mixes for the 63 ASM subregions applying the
subregional mix to the contained counties is not straight forward. The counties each
have dierent resource endowments and suitability for particular crops. One cannot
mechanically allocate the crops based on the proportional allocation of land across
the counties. We needed a procedure which would allocate crops to counties as
consistently as possible with observed mixes in the counties.
3.1. Exploitable data for developing county crop mixes sets from ASM results
The Census of Agriculture (US Bureau of Census, 1994), USDA National
Resources Inventory (NRI) and County Crops Data (US Department of Agriculture, 1996) all contain partial and/or periodic data sets on dry and irrigated area
of speci®c crops by county. These were used in conjunction with USDA Agricultural
Statistics to develop as complete as possible of a series of county-level irrigated and
dryland crop mixes.
3.2. Developing county-level crop mix solutions consistent with ASM region solutions
In disaggregating the ASM solution regional crop mixes to the county level, we
tried to make the resultant crop mixes as consistent as possible with historically
observed crop mixes. After several false starts where we used mechanical formulas
and heuristics to allocate the mixes we turned to a mathematical programming
approach. We set up a model where the fundamental choice variable was the area of
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
151
a particular crop allocated to an irrigation status in a county. We constrained this
choice so it best matched history and the ASM solution, but also allowed deviations
from the historical observations which we minimized in a linear programming
model. The constraints used and allowed deviations were
C1
C2
C3
C4
C5
C6
C7
The area allocated to each crop by irrigation status across all counties in an
ASM subregion had to equal the totals that were in the ASM solution for
that subregion.
The cropped area in each county could not exceed the maximum cropped
area in the historic data, but deviation above the maximum was allowed by
including a deviation variable.
The cropped area in a county could generally not be less than the minimum
cropped area in the historic data but we allowed deviation below that
minimum.
The cropped area irrigated in a county could not exceed from the maximum
irrigated area observed but again deviation was allowed.
The area of an individual crop in each county could be no greater than the
maximum observed area of that crop with deviation allowed.
The area of a crop in a county could be no less than the minimum amount
of land devoted to that crop with deviation allowed.
The ASM model followed the McCarl (1982) crop mix procedure and
chose a crop mix for regions corresponding to a particular year in history.
The area then allocated in a county was constrained to minimally deviate
from an interpolated county crop mix developed by interpolation between
the periodic NRI and census data using Agricultural Statistics for the whole
state. Both positive and negative deviations from that mix were allowed.
The model objective function (Appendix provides a mathematical de®nition)
minimizes the summed deviations across the counties: (1) above the maximum total
cropped area observed from C2; (2) below the minimum total cropped area (C3); (3)
above the maximum total irrigated area (C4); (4) above the maximum area of a crop
ever observed (C5); (5) below the minimum crop area ever observed (C6); and (6)
above or below the county area allocation de®ned using the historic crop mixes (C7).
Solution of the mathematical program gave a land allocation which was quite
similar to historical allocations. However, resultant maps showed sharp distinctions
at the ASM subregional borders. For example, when mapping the Oklahoma and
Texas border, one could see a quite dierent allocation pattern in the Oklahoma
border counties in comparison to adjacent Texas counties. Examination of the data
showed that land allocation in adjacent counties deviated from historical land use
interrelationships between the counties. This then led us to add yet one more constraint (C8) and associated deviation variables.
C8
The ratio of crop area relative to the area of the same crop in all adjacent
counties was required to equal the historical average ratio between the
counties with positive and negative deviations allowed.
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J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
Use of the model with this feature led to realistic county-level cropping patterns
consistent with the ASM solution and which were judged satisfactory for use in
SWAT and other predicted land use mapping exercises.
3.3. Reallocation of county crop mixes to watersheds
The ®nal step once the county crop mixes were obtained was to reallocate those
crop mixes into watershed HCUs. This was done by using the proportion of each
county's area in each of the watersheds based on geographic data used in setting up
the SWAT model.
4. An illustration: development of a national area allocation
Suppose we illustrate the results from the county land allocation model at
the national level. This involves taking the ASM 63 region solution and allocating it
to more than 3000 counties for 15 crops according to the model in the Appendix.
For economy of journal space, we only display results for irrigated corn. Fig. 1
contains several maps of county-level irrigated corn area relative to total cropped
area. Fig. 1a shows a map where the proportional share in the 63 ASM regions was
assumed to apply to each contained county. Fig. 1c shows the 1992 observed area
allocation. Fig. 1b shows the results from the land allocation model. Note the procedure allows us to generate a relatively consistent acreage allocation.
5. A second illustration: regional hydrological results consistent with economic
results
Suppose we illustrate the procedure with the resultant SWAT tie in. We did this in
an REEA examination of ®ve crop varieties developed in Texas (Clarke, 1997).
Adoption of these varieties in¯uences production and prices, both in and outside of
Texas resulting in dierent economic and environmental outcomes.
5.1. Type of impacts estimated
The ASM provides producers' and consumers' impact estimates for Texas substate regions, other adopting regions in the USA, the US in total, foreigners, and the
world in total. ASM also produces regional results on labor, land, and water use,
and expenditures on input use.
From the SWAT model, we get maps showing changes in precipitation runo and
nitrogen, phosphorus, potassium, and sediment loss with the runo. We only present the Texas results since the study was a prototype assessment and only involved
SWAT runs in Texas (note SWAT has to be run individually for each of the 192
Texas HCUs). We also only very brie¯y discuss the economic results since as there
have been a lot of previous economic assessments of this type; the focus of the paper
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
153
Fig. 1. Percentage of county irrigated area devoted to irrigated corn under dierent allocation rules and
in National Resources Inventory (NRI) data. (a) Subregional area distribution; (b) distribution using full
model; (c) actual area allocation in NRI data for 1992.
154
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
is on the linkage of the two models. Those wishing more detail on the economic
results may obtain the detailed report by Clarke (1997).
5.2. Economic results
Table 1 shows that society as a whole, foreign producers and consumers, all US
consumers, and some US producer groups bene®t from the new varieties. However,
three adopting regions of Texas, several other adopting regions of the US show
producer losses. Texas producers as a group gain $72.8 million while Texas consumers gain $15.3 million. Producers' losses in the rest of the USA are sucient to
largely oset consumer gains in those regions; however, for the entire USA the net
total gain is $43.3 million. For the total world, consumers' gains of $437.6 million
exceed producers' losses of $169.9 million.
Table 2 shows that except for some small regional decreases, the crop varieties
stimulate an increase in use of total cropland, irrigated land, and irrigation water.
For the USA as a whole, cropland use decreases by less than 1% of currently cultivated cropland. Table 2 shows that pasture use decreases in Texas, but only by
about 3.5% of the cropland increase for the state. Pasture use increases in other
adopting regions and in the USA as a whole.
5.3. SWAT environmental results
The SWAT model was run for the Texas watersheds over 30 years of observed
weather data (1960±89) with and without the new crop varieties. The results we
Table 1
Economic bene®t from adoption of new crop varieties developed by Texas Agricultural Experiment Station
Region
Consumers' (million $)
Producers' (million $)
Total (million $)
Texas high plains
Texas rolling plains
Texas central blacklands
Texas east
Texas Edwards Plateau
Texas Coastal Bend
Texas south
Texas Trans Pecos
Texas Ð all regions
Delta statesa
Great Plains statesb
Total Ð other adopt regions
Rest of USA
Total USA
Foreign
Total world
0.8
0.7
6.3
1.5
0.3
4.0
1.0
0.7
15.3
13.1
11.9
25.0
176.6
216.9
220.7
437.6
25.5
12.9
9.0
ÿ0.2
ÿ0.6
22.2
4.6
ÿ0.6
72.8
ÿ30.4
ÿ18.4
ÿ48.8
ÿ197.6
ÿ173.9
3.7
ÿ169.9
26.3
13.6
15.3
1.3
ÿ0.3
26.2
5.6
0.1
88.1
ÿ17.3
ÿ6.5
ÿ23.8
ÿ21.0
43.3
224.4
267.7
a
Rice-producing areas of Arkansas, Louisiana, Mississippi, Missouri.
Wheat-producing areas of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota,
Wyoming, and cotton-producing areas of Oklahoma.
b
155
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
Table 2
Changes in resource use from adoption of new crop varieties developed by the Texas Agricultural
Experiment Station
Region
Water
(1000 m3)
Irrigated land
(1000 ha)
Cropland
(1000 ha)
Pasture land
(1000 ha)
Texas high plains
Texas rolling plains
Texas central blacklands
Texas east
Texas Edwards Plateau
Texas Coastal Bend
Texas south
Texas Trans Pecos
Texas Ð all regions
Delta statesa
Great Plains statesb
Total Ð other adopt regions
Rest of USA
Total USA
398 182.06
8511.33
32 195.02
3330.52
0.0
142 842.26
27 137.56
ÿ6290.98
605 907.77
ÿ454 307.48
ÿ113 237.65
567 545.13
101 025.75
139 388.39
84.74
1.78
5.79
1.42
0.0
ÿ13.52
5.91
ÿ1.01
85.11
ÿ92.96
ÿ57.43
ÿ150.39
ÿ4.82
ÿ70.09
146.58
74.63
35.41
ÿ11.13
ÿ4.41
53.02
6.72
ÿ1.13
299.68
ÿ79.69
85.27
9.63
ÿ392.28
ÿ82.96
ÿ2.67
5.22
2.47
1.66
ÿ6.48
0.53
ÿ12.59
1.66
ÿ10.20
16.75
106.64
123.39
166.86
280.05
a
Rice-producing areas of Arkansas, Louisiana, Mississippi, Missouri.
Wheat-producing areas of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota,
Wyoming, and cotton-producing areas of Oklahoma.
b
highlight involve changes between the with and without new varieties cases in
surface water: sediment load, water running in from croplands (hereafter called
runo), nitrogen load, and phosphorous load.
Not all of the 192 HCUs exhibited a large change in environmental health indicators. In order to avoid false signals, all HCUs that had less than 1% cropland or
less than 0.1 kg of N or P loss per hectare were not considered for mapping. This left
65 HCUs for which change information is displayed.
We chose cases with greater than 5% change to display. In turn 35 HCUs had a
change >5% in water borne sediment (Fig. 2a). Of these, 10 HCUs exhibited
lower sediment loss and 25 yielded greater sediment loads. In terms of runo, only
three basins changed by more than 5% in three basins (Fig. 2b). One experienced
decrease in stream ¯ow while two had more water reaching the stream. Nitrogen
results (Fig. 2c) show 12 basins had greater than 5% reductions in loading while 18
showed increases. Phosphorus results were similar (Fig. 2d) with 18 basins exhibiting
decreases and 24 exhibiting increases.
6. Concluding comments
The methodology developed herein allows one to disaggregate economic model
results for use in geographically speci®c environmental simulators. Often environmental results are of at least equal policy maker concern as the economic impacts.
156
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
Fig. 2. Changes in Texas environmental health indicators as projected by SWAT. (a) Sediment loss;
(b) runo; (c) nitrogen losses; (d) phosphorus losses.
Acknowledgments
Seniority of authorship is shared by the ®rst two authors. The other authors all
made equal contributions. This paper arose out of eorts supported by a USDA,
NRCS Cooperative agreement with the Texas Agricultural Experiment Station
(TAES) and out of the USAID grant #PCE-G-00-97-00051-00, Impact Methods to
Predict and Assess Contributions of Technology.
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
157
Appendix. Land Allocation Model
The fundamental variable in the model is Landp,c,i which depicts the allocation of
cropland in county p for crop c of irrigation type i. As mentioned above this allocation is constrained by eight relationships.
The area allocated to counties within an ASM region for each crop and irrigation
type must equal the area found within the ASM solution (asmacre) for that region
(counties in region s are identi®ed by p(s)):
X
Landp;c;i asmacres;c;i for relevant s; c; i:
C1
pp s
Total cropped area across all crops in a county is less than the maximum amount
observed historically (maxuse). But use above the maximum is allowed through the
deviation variable (Maxusedev).
XX
Landp;c;i ÿ Maxusedev
p 4maxusep for relevant p
c
C2
i
The area in a county across all crops is greater than the minimum amount
observed historically (minuse), but we allow deviation below the minimum through
the deviation variable (Minusedev).
XX
Landp;c;i Minusedevÿ
p 5minusep for relevant p
c
C3
i
The irrigated area in a county is no more than the maximum amount observed
historically (maxirracre), but we allow deviation above the maximum through the
deviation variable (Irrdev).
X
Landp;c;i ÿ Irrdev
C4
p 4maxirracrep for all p and i irrigation
c
The area used in a county for a crop can be no more than the maximum amount
observed (maxcrop) but we allow a deviation above the maximum through the
deviation variable (Maxcropdev).
X
Landp;c;i ÿ Maxcropdev
C5
p;c 4maxcropp;c for relevant p; c
i
The area in a county for a crop can be no more than the minimum amount
observed historically (mincrop), but we allow a deviation below the minimum
through the deviation variable (Mincropdev).
X
Landp;c;i Mincropdevÿ
C6
p;c 5mincropp;c for relevant p; c
i
158
J.D. Attwood et al. / Agricultural Systems 63 (2000) 147±159
The area allocation must exhibit minimum deviation from the mix developed by
the historic crop mix approach using the percentage utilization of the historical crop
mix solution from ASM (asmmix) with deviations allowed through the variables
Asmmixdev.
X
Landp;c;i Asmmixdevÿ
p;c ÿ Asmmixdevp;c
i
asmmixp;c for relevant p; c
C7
The interrelationship between land allocation by crop in a county and all adjacent
counties minimally deviates from the historic proportional area share by crop in this
county (p) divided by proportional share in the adjacent county (pl) with deviation
variables Adjdev.
X Landp;c;i
i
totlandp
ÿ
X Landp1;c;i
i
totlandp1
adjratp;p1;c Adjdevÿ
p;p1;c ÿ Adjdevp;p1;c
0 for relevant p; p1; c
C8
The objective function minimizes the sum of all deviation variables. This includes
deviations: (1) above maximum and below minimum cropland usage [Maxusedev
from (C2) and Minusedev from (C3)]; (2) above maximum observed irrigated area
[Irrdev from (C4)]; (3) above maximim and below minimum area for a crop [Maxcropdev, Mincropdev from (C5) and (C6)]; (4) above and below ASM extrapolated
mix [Asmmixdev from (C7)]; and (5) above and below the historic proportion of
area in adjacent counties (Adjdev) from (C8). The equation is as follows:
min
X
XX
X
ÿ
Maxusedev
Irrdevp
Maxcropdev
p Minusedevp
p;c
p
p
Mincropdevÿ
p;c
p
c
XX
ÿ
Asmmixdev
p;c Asmmixdevp;c
p
c
XXX
ÿ
Adjdev
p;p1;c Adjdevp;p1;c
p
p1
c
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