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
eaUSDA, Natural Resources Conservation Service, 808 E. Blacklands Road, Temple, TX 76502, USA bDepartment of Agricultural Economics, Texas A&M University, College Station, TX 77843-2124, USA cCorpus Christi Research Center, Texas Agricultural Experiment Station, Route 2 Box 589, Corpus Chrisi, TX
78406-9704, USA
dCapital One Financial, 2980 Fairview Park, Falls Church, VA 22042-1091, USA eBlackland Research Center, Texas Agricultural Experiment Station, TX, USA
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 impli-cations. 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 invol-ving 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
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1. Introduction
Technological developments, agricultural policy alterations, and environmental regulation revisions are changing the agricultural production and processing envir-onment. Agricultural scientists face demands to project the consequences of these forces. Agricultural economists have typically responded by creating economic cost-bene®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 simulta-neous analysis of economic and environmental impacts of change across the econ-omy 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 environ-mental data and models employ physical or coordinate-based boundaries. Further-more, 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 water-shed 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 con-sidered 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 contribu-tion 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 water-sheds.
As a demonstration we present results from analyses of soil erosion policy changes and crop variety releases.
2. Analytical background
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 crop-land, 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 pro-cessed 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 pro-vides 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 agri-cultural 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. Usa-ges 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
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 monu-mental 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, histor-ical 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 Agri-culture, 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
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 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.
C2 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.
C3 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.
C4 The cropped area irrigated in a county could not exceed from the maximum irrigated area observed but again deviation was allowed.
C5 The area of an individual crop in each county could be no greater than the maximum observed area of that crop with deviation allowed.
C6 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.
C7 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 con-straint (C8) and associated deviation variables.
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 pro-cedure 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 sub-state 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.
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 con-sumers 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 culti-vated 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 0.8 25.5 26.3
Texas rolling plains 0.7 12.9 13.6
Texas central blacklands 6.3 9.0 15.3
Texas east 1.5 ÿ0.2 1.3
Texas Edwards Plateau 0.3 ÿ0.6 ÿ0.3
Texas Coastal Bend 4.0 22.2 26.2
Texas south 1.0 4.6 5.6
Texas Trans Pecos 0.7 ÿ0.6 0.1
Texas Ð all regions 15.3 72.8 88.1
Delta statesa 13.1
ÿ30.4 ÿ17.3
Great Plains statesb 11.9
ÿ18.4 ÿ6.5
Total Ð other adopt regions 25.0 ÿ48.8 ÿ23.8
Rest of USA 176.6 ÿ197.6 ÿ21.0
Total USA 216.9 ÿ173.9 43.3
Foreign 220.7 3.7 224.4
Total world 437.6 ÿ169.9 267.7
a Rice-producing areas of Arkansas, Louisiana, Mississippi, Missouri.
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 indi-cators. 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 environ-mental results are of at least equal policy maker concern as the economic impacts.
Table 2
Changes in resource use from adoption of new crop varieties developed by the Texas Agricultural Experiment Station
Texas high plains 398 182.06 84.74 146.58 ÿ2.67
Texas rolling plains 8511.33 1.78 74.63 5.22
Texas central blacklands 32 195.02 5.79 35.41 2.47
Texas east 3330.52 1.42 ÿ11.13 1.66
Texas Edwards Plateau 0.0 0.0 ÿ4.41 ÿ6.48
Texas Coastal Bend 142 842.26 ÿ13.52 53.02 0.53
Texas south 27 137.56 5.91 6.72 ÿ12.59
Texas Trans Pecos ÿ6290.98 ÿ1.01 ÿ1.13 1.66
Texas Ð all regions 605 907.77 85.11 299.68 ÿ10.20
Delta statesa
ÿ454 307.48 ÿ92.96 ÿ79.69 16.75
Great Plains statesb
ÿ113 237.65 ÿ57.43 85.27 106.64
Total Ð other adopt regions 567 545.13 ÿ150.39 9.63 123.39
Rest of USA 101 025.75 ÿ4.82 ÿ392.28 166.86
Total USA 139 388.39 ÿ70.09 ÿ82.96 280.05
a Rice-producing areas of Arkansas, Louisiana, Mississippi, Missouri.
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.
Appendix. Land Allocation Model
The fundamental variable in the model is Landp,c,iwhich depicts the allocation of
cropland in countyp for cropcof irrigation type i. As mentioned above this allo-cation 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 regionsare identi®ed byp(s)):
X
pp s
Landp;c;iasmacres;c;i for relevant s;c;i: C1
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).
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).
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
c
Landp;c;iÿIrrdevp4maxirracrep for all p and iirrigation C4
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).
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
i
Landp;c;iMincropdev
ÿ
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.
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.
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 [Max-cropdev, 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:
minX
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