Greener Acres or Greener Waters?
Potential U.S. Impacts of Agricultural
Trade Liberalization
Robert C. Johansson, Joseph Cooper, and Utpal Vasavada
This paper examines the elimination of all agricultural policy distortions in all trading countries and agricultural production decisions in the United States, as well as subsequent environmental quality in the presence and absence of nondegradation environmental standards.
The results suggest that trade liberalization has the potential to increase domestic production
and boost agricultural returns by as much as 8.5 percent. Consumer surplus would likely fall,
and the discharge of nutrients, sediment, and pesticides would likely increase. However, environmental policies can limit these adverse environmental impacts and mute the potential decrease in consumer surplus, while leaving increased returns to agricultural production.
Key Words: agriculture, trade reform, environment, nondegradation
Legislation of the United States requiring formal
environmental reviews, or environmental assessments, of major federal activities significantly
affecting the environment dates back thirty years.
Within the last decade, nongovernmental organizations (NGOs) and other interested parties have
called for extending these environmental reviews
to trade agreements (e.g., World Wildlife Federation 2001). In fact, U.S. law requires an environmental review of all new trade agreements
beginning in 2001 (U.S. Executive Order 13141,
1999). Such an environmental review would
likely be required for multilateral trade liberalization in the context of the World Trade Organization (WTO) negotiations in Doha, Qatar, in 2001.
There the WTO affirmed its commitment to
“correct and prevent restrictions and distortions in
world agricultural markets.” Further, the WTO
committed itself to “comprehensive negotiations
aimed at…substantial improvements in market
__________________________________________
The authors are economists with the Economic Research Service of the
U.S. Department of Agriculture. This paper was presented at the International Trade and the Environment Workshop sponsored by the
Northeastern Agricultural and Resource Economics Association and
the U.S. Environmental Protection Agency, National Center for Environmental Economics, in Halifax, Nova Scotia, on June 23, 2004. The
views expressed in this paper are those of the authors’ and do not
necessarily represent policies or views of the U.S. Environmental
Protection Agency or of the USDA or the Economic Research Service.
We are thankful for suggestions and critiques received from Daniel
Hellerstein and several anonymous reviewers.
access; reductions of, with a view to phasing out,
all forms of export subsidies; and substantial
reductions in trade-distorting domestic support”
(WTO 2001).
While it is not clear what consequences might
result from the environmental review of such
trade agreements, they may include multilateral
environmental agreements. There are approximately 200 multilateral environmental agreements
(MEAs) in place today, of which 20 contain trade
provisions (United Nations Environment Programme 2000). Trade agreements may themselves raise environmental quality by increasing
income—environmental quality is income elastic.
However, linking environmental side-agreements
to trade agreements may be an economically efficient method for avoiding adverse environmental
impacts of trade or for minimizing the impacts of
trade on environmental agreements. For example,
the Office of the U.S. Trade Representative
(USTR) states in its first environmental review of
a free trade agreement that “trade agreements can
provide positive opportunities for enhancing environmental protection” (USTR 2003). However,
even without MEAs linked to trade policy, environmental reviews of policy will also consider the
impacts of trade policy on current and future environmental policies. In the aforementioned environmental review, the USTR states that a core
Agricultural and Resource Economics Review 34/1 (April 2005) 42–53
Copyright 2005 Northeastern Agricultural and Resource Economics Association
Johansson, Cooper, and Vasavada
obligation of free trade agreements is a “commitment not to weaken or reduce the protections afforded by environmental laws in order to attract
trade or investment.” In light of the 2001 DohaWTO trade talks, we consider how adjustments to
agricultural trade liberalization might influence or
be influenced by national or regional environmental policies such as the Clean Water Act.
Background
Economic theory typically concludes that trade
liberalization increases overall economic welfare.
Although free trade is optimal from the viewpoint
of world welfare, it is not necessarily so from the
viewpoint of a single country unless the country
is small (Bhagwati and Panagariya 1996). For a
large country, with appropriate taxes and subsidies, a welfare level higher than that associated
with autarky can be attained. Devising Paretoimproving policy becomes difficult, though, in
the presence of negative externalities associated
with production, especially in the absence of
well-defined property rights, which can lead to
the underpricing of natural resources. In such
situations, the policymaker must balance welfare
improvements from trade against its environmental consequences when setting taxes, subsidies, or standards.
The question posed in this paper is, what are
the agri-environmental outcomes of liberalization,
since outcomes can be positive (decreased environmental damage and increased producer and
consumer surplus) or negative (increased environmental damage and decreased surplus)? While
a broad theoretical and empirical literature examines trade and the environment, this literature
focuses primarily on the manufacturing sector
(Frankel and Rose 2002, Antweiler, Copeland,
and Taylor 2001). Fewer quantitative studies have
examined the environmental implications of agricultural trade liberalization (Abler and Shortle
1992, Williams and Shumway 2000). These
analyses typically assume a change in the underlying trade conditions as a given, and estimate
potential production and input changes for a subset(s) of the agricultural sector. As environmental
impacts are not explicitly modeled in these studies, environmental inferences are extrapolated
from the estimated changes in production and
input use.
Greener Acres or Greener Waters? 43
The literature extending trade analysis to include environmental policies is likewise brief.
Both Anderson (1992) and López (1994) find
that, if countries fail to institute effective environmental policies, the environmental effects of
freer trade can be negative. On the other hand, if
effective environmental policies are in place,
freer trade will generally increase total benefits to
society (Anderson 1992). Diao and Roe (2003)
provide intuition on how trade and environmental
policies might interact to produce a “win-win”
situation, illustrating how declining farm incomes
following trade reform in Morocco could be cushioned when coupled with an environmental policy—water market reform in this case. Nonetheless, taken as a whole, the limited number of existing studies in conjunction with their limited
scope do not allow us to draw generalizations on
the environmental impacts in the United States
due to agricultural trade liberalization enacted in
isolation or in tandem with environmental policies. Further, previous analyses do not disaggregate production and environmental impacts regionally—an important step, as small environmental impacts in the national aggregate may be
significant regionally.
A stylized, graphical representation (Figure 1)
of trade liberalization and agricultural externalities for an exporting country with a comparative
advantage in the production of a composite agricultural commodity serves to illustrate our basic
points. The initial world price and domestic production level is {P0, Q0}, and production of the
negative agricultural externality is E0. The initial
emission function (G0) is determined by the interaction of scale, technique, and composition effects (Cole, Rayner, and Bates 1998) and can be
assumed to be non-decreasing in commodity production (illustrated as linear for the sake of this
discussion).
Now assume that trade liberalization is
achieved through trade policy change (e.g., tariffs), which would bring the new price-quantity
combination to {P1, Q1}. While this liberalization
increases domestic producer surplus and reduces
domestic consumer surplus, it also leads to an
increase in the domestic agricultural externality to
E1. How might potential increases in agricultural
pollution interact with current environmental
regulations or be viewed under a free trade
agreement environmental review? A first-best trade
44
April 2005
Agricultural and Resource Economics Review
Crop price per unit
Demand
(domestic)
S1
S0
Supply
(domestic)
P1
P0
Crop quantity
Q0
Q2
Q1
E0, E2
E1
G1
G0
Negative externality
Emission
function
Figure 1. Stylized Relationship Between Trade
Liberalization and Agricultural Externalities
model would seek to maximize consumer surplus
plus producer surplus plus environmental benefits. However, to reflect better actual policy, we
assume a second-best harmonization in which
environmental standards are in place that restrict
environmental impacts associated with trade liberalization. This is consistent with the USTR’s
“commitment not to weaken or reduce the protections afforded by environmental laws in order to
attract trade or investment.”
The U.S. Environmental Protection Agency
(EPA) has found that agriculture in the United
States is the leading source of pollution in 48
percent of impaired river miles, 41 percent of
impaired lake acres, and 18 percent of impaired
estuarine areas surveyed (EPA 2002a). Therefore,
it is likely that agriculture’s adjustments to agricultural trade liberalization could have observable
environmental effects in the United States. We
examine how national and regional nondegradation standards for water quality may interact with
agriculture’s adjustments to agricultural trade
liberalization. The EPA adopted nondegradation
provisions in 1975, requiring states to develop
these policies as part of the state’s water quality
standards (EPA 2004). These standards essentially require states to protect existing uses and
water quality conditions to support such uses and
are among the strongest regulatory powers in the
Clean Water Act (River Network 2004).1
Nondegradation provisions of the Clean Water
Act are implemented through the National Pollutant Discharge Elimination System (NPDES),
which controls point source discharge of pollutants. The courts have ruled that nondegradation
standards do not allow the EPA to regulate nonpoint source discharge of agricultural pollutants
(U.S. Court of Appeals for the Tenth Circuit
2001). That said, of the 21,845 impaired waterbodies detailed on the EPA 303d list, 43 percent
are attributable solely to nonpoint sources, and an
additional 47 percent have both nonpoint source
and point source contributions (EPA 2002b). For
each impaired waterbody, states must develop a
comprehensive pollutant management plan,
which specifies the maximum amount of a pollutant that a waterbody can receive from point and
nonpoint pollutant sources and how the necessary
reductions will be achieved. As part of their management plans, states can and do impose nonpoint
source controls [see, for example, nondegradation
standards for nonpoint sources in the Lake Superior Basin (EPA 2000)].
The horizontal line E2 in Figure 1 represents
such a nondegradation restriction. Enforcing this
restriction, while permitting the trade liberalization treaty to move forward, increases costs to
farmers as they change production practices to
limit the externality. This will shift supply inwards to S1 and decrease production from Q1 to
Q2. The new emissions function (G1) describes
the new interaction between technique, scale, and
composition effects. Returns to agricultural production under trade liberalization with environmental standards may be lower with respect to
trade liberalization with no restrictions, but may
still be higher than in the base case of no trade
liberalization. Whether or not the environmental
standards are welfare-enhancing overall depends
on the value of (E1 – E0) compared to change in
consumer surplus and returns to agricultural producers. Our goal is to develop an empirical model
1
Similar provisions are also found in section 4(b) of the Wilderness
Act and section 101(b)(1) of the Clean Air Act.
Greener Acres or Greener Waters? 45
Johansson, Cooper, and Vasavada
that allows interactions between multiple commodities, inputs, production practices, and externalities, for a trade liberalization scenario that is
actually under consideration.
Methodology
We extend previous empirical approaches by explicitly modeling the environmental impacts of
endogenous regional production, consumption,
and price changes for all major U.S. agricultural
sectors in response to an exogenous trade liberalization scenario. Production adjustments are
viewed in terms of technique, scale, and composition effects, which have specific regional, agrienvironmental implications. We extend the model
to include nondegradation standards and assess
the implications for consumer surplus and expected producer gains from trade liberalization.
Simulation Model for U.S. Agriculture
To estimate the endogenous adjustments to
changes in underlying trade conditions, we use a
multi-commodity, regional model [the U.S. Regional Agricultural Sector Model (USMP) (House
et al. 1999)] that incorporates agricultural commodity, supply, demand, and the environment to
simulate potential adjustments in production and
prices to policy (see, for example, Johansson and
Kaplan 2004). The USMP uses a positive math
programming approach (Howitt 1995) to calibrate
production levels and enterprises to regularly
updated production practice surveys (Padgitt et al.
2000), the USDA multi-year baseline (USDA
2003) and the National Resources Inventory
(USDA 1994). Simulations are manifest across 10
main production regions (r) and 45 sub-regions
(u) (see Figure 2), further delineated by erosion
class (highly erodible and non-highly erodible).
The model includes 22 inputs and the production
and consumption of 42 agricultural commodities
and processed products (Table 1), which are integrated into the flow of final commodity demand
and stock markets. The USMP considers domestic consumption, net trade, processing, and government stock demands. The model differentiates
more than 5,000 crop production enterprises according to cropping rotations, tillage practices,
and fertilizer rates. More than 90 livestock and
poultry production enterprises are delineated at
the region level by species.
Figure 2. U.S. Regional Agricultural Sector
Model (USMP) Spatial Coverage: Intersection
of 10 USDA Farm Production Regions and 25
USDA Land Resource Regions
The agriculture sector is assumed to be a spatially competitive market equilibrium system, but
partial in the sense that it does not compete with
other sectors (e.g., manufacturing) for factors of
production (e.g., land or labor). The model allows
for production scale effects, some composition
effects, such as a changing product mix, and
technique effects, in response to changes in economic incentives. For instance, nitrogen fertilizer
use can be reduced by decreasing acreage planted
(scale effect), by shifting to production of crops
that use less nitrogen fertilizer (composition effect), or by reducing nitrogen fertilizer application rates (technique effect). Estimated price and
production changes are simulated for commodity
production at the regional level and integrated
into the flow of final commodity demand and
stock markets.
This is accomplished using a constrained optimization approach, maximizing consumer and
producer surplus, consistent with a free market,
medium-run, spatial equilibrium, ℒ:
Z′Βd Z
P′Βs P
− P′Αs −
− Y′WY
2
2
INPV′ ΒsINPV
−INPv′ As −
− INPF′ WINP ;
2
(1) Max ℒ ≡ Z′Αd −
subject to
(2)
pp′cr Xcr + pp′liv Xliv + pp′y Y − Z ≥ 0
(commodity balancing);
(3)
pp′inpcr Xcr + pp′inpliv Xliv − INPV ≤ 0, ∀r
46
April 2005
Agricultural and Resource Economics Review
Table 1. Inputs and Outputs for Simulation Model
Inputs
Regional
cropland
pastureland
National
nitrogen fertilizer
potassium fertilizer
potash fertilizer
lime
other variable costs
public grazing land
custom farming operations
chemicals
seed
interest on operating capital
machinery and equipment repair
veterinary and medical costs
marketing and storage
ownership costs
labor and management costs
land taxes and rent
general farm overhead
irrigation water application
energy costs
insurance
Crops
corn
sorghum
barley
oats
wheat
cotton
rice
soybeans
silage
hay
Outputs
Livestock
fed beef for slaughter
nonfed beef for slaughter
beef calves for slaughter
beef feeder yearlings
beef feeder calves
cull beef cows
cull dairy cows
cull dairy calves
milk
hogs for slaughter
cull sows for slaughter
feeder pigs
Processed
soybean meal
soybean oil
livestock feed mixes
dairy feed supplements
swine feed supplements
fed beef
nonfed beef
veal
pork
broilers
turkeys
eggs
butter
American cheese
other cheese
ice cream
nonfat dry milk
manufacturing milk
ethanol
corn syrup
Note: The U.S. Regional Agricultural Sector Model (USMP) accounts for production of the major crop (corn, soybeans, sorghum,
oats, barley, wheat, cotton, rice, hay, silage) and confined livestock (beef, dairy, swine, and poultry) categories, comprising approximately 75 percent of agronomic production and more than 95 percent of confined livestock production (USDA 1997). We do
not consider potential applications of manure to rangeland, vegetable, horticulture, sugar, peanut, or silviculture operations.
(regional input balancing);
1
−
−ρ p , u
ρ p ,u
(4) α p ,u (∑ b δb ,u s p ,b ,u RACb ,u
)
− C p ,u ≤ 0, ∀p, u
(regional crop balancing);
(5) αb ,u (∑ t δb ,t ,u X b ,t ,u
−ρb , u
−
)
1
ρb , u
− RACb ,u ≤ 0, ∀b, u
(regional rotation balancing); and
(6)
Z, Y, Xcr , Xliv , INPV , INPF , RAC, C ≥ 0
(nonnegativity constraints).
Matrix Z represents demand for produced commodities (matrix P), across markets and regions.
Matrices A and B are the intercept and slope coefficients for product and market demand (superscript “d”) and supply (superscript “S”), respectively. Matrices Xcr and Xliv represent cropping
and livestock activities across regions and management practices. Vectors Y and Wy represent
processing activity levels and net costs of process, respectively. Matrix INP represents variable
(subscript “V”) and fixed (subscript “F”) inputs
into production. WINP represents cost per unit of
fixed inputs. The output parameters per share of
crop, livestock, and processing activities are represented by matrices ppcr, ppliv, and ppy, respectively. The input parameters per share of crop and
livestock production activities are represented by
matrices ppinpcr and ppinpliv, respectively. Substitution among the cropping activities is represented using nested constant elasticity of transformation (CET) functions [(4) and (5)]. The crop
and rotation balancing equations ensure that supply of land (Cp,u) in sub-region (u) is allocated to
a crop (p) and is at least as great as the demand
for it, given by the sum of rotational acres
(RACb,u) multiplied by the share of each crop
grown in that rotation (s p,b,u) subject to nonlinear
CET distribution (δb,u), shift (αp,u), and substitution (ρp,u) calibration parameters. Similarly, the
allocation of land to various tillage practices (t)
used in a crop rotation (b) must be no greater than
the amount of land in that rotation, also subject to
CET distribution (δb,t,u), shift (αb,u), and substitution (ρb,u) calibration parameters.
The nonlinear CET equations imply that there is
a declining marginal rate of transformation be-
Greener Acres or Greener Waters? 47
Johansson, Cooper, and Vasavada
tween land used in one crop rotation and land used
to produce the same crop as part of another rotation, and between one tillage activity in a particular
rotation and land used in other tillage activities
used with the same rotation. This implies that
changes in land allocated to various production
enterprises will not occur in a bang-bang fashion,
but will smoothly adjust to changes in relative returns across production enterprises. The transformation elasticities are specified so that model supply response at the national level is consistent with
domestic supply response in the USDA’s Food and
Agriculture Policy Simulator (Westcott, Young,
and Price 2002) and with trade response in the
USDA Economic Research Service (ERS)/Penn
State model (Stout and Abler 2003).
For this analysis, we examine environmental
parameters historically of concern for water quality and U.S. agri-environmental policy: pesticide
use, soil erosion, and nutrient (nitrogen and phosphorus) losses to water. Changes in the levels of
these parameters are estimated using the Environmental Policy Integrated Climate (EPIC)
model (Mitchell et al. 1998). For each crop production activity, the EPIC model simulates erosion (sheet, rill, and wind), nutrient and pesticide
cycling as a function of crop management (rotation, tillage, and fertilizer rates) given historic
weather, hydrology, soil temperature, and topography data.
model is an applied partial equilibrium, multiplecommodity, multiple-region model of agricultural
policy and trade, which simulates the agriculture
sector’s response to a scenario in which all countries eliminate their border protections and tradedistorting domestic support for all commodities
(Stout and Abler 2003). It is a gross trade model
accounting for exports and imports of each commodity in every region, but it does not distinguish
a region’s imports by their source or a region’s
exports by their destination.
The core set of policies “liberalized” across all
countries in this model include both specific and
ad valorem import and export taxes/subsidies,
tariff-rate quotas (TRQs), and producer and consumer subsidies.2 Also tariffs, fixed payments per
unit of output and per unit of intermediate output,
as well as any direct and whole-farm payments
that are based on area or that otherwise affect
crop mix were eliminated. Decoupled subsidies,
such as production flexibility contracts, are not
linked to production of specific crops, and therefore do not factor into this set of simulation models. For example, the model removes U.S. loan
rates for crops and marketing orders for dairy
products. For Japan, the model removes “markups” for rice and wheat. Policy coverage for the
Table 2. Changes in U.S. Production and
Prices for Selected Commodities Following
Trade Liberalization (%)
Trade Liberalization
The U.S. agricultural trade surplus is currently
expected to be about $1 billion for 2005 (Brooks,
Whitton, and Carter 2005). Historically, bulk
grains have been the largest share of U.S. exports;
however, since 2000, higher value animals and
animal products have formed the largest share of
U.S. exports (USDA 2004). The largest share of
food imports is fresh fruit and vegetable products.
Given that average global protection is higher for
grains and animal commodities than for fruit and
vegetables, we would expect trade liberalization
to generally favor U.S. producers by resulting in
increased world prices for these products
(Burfisher et al. 2001), as depicted in a stylized
fashion in Figure 1. We simulate changes in U.S.
production levels and prices likely to prevail after
all trade restrictions on agricultural products are
lifted between WTO member nations using the
ERS/Penn State WTO model (Table 2). This
Percent Change
Commodities
rice
wheat
corn
other coarse grains
soybeans
cotton
beef and veal
pork
poultry meat
butter
cheese
non-fat dry milk
fluid milk
whole dry milk
other dairy
Production
-1.20
-0.10
2.40
1.70
-0.70
0.00
-0.10
0.00
1.60
-15.00
-0.60
-15.00
1.70
-31.60
1.90
Price
13.20
4.80
16.50
13.50
7.50
4.50
10.60
7.50
13.00
-12.00
-1.90
-1.60
-1.20
-13.40
-1.10
Source: Derived from the USDA ERS/Penn State WTO model.
2
For a discussion of agricultural trade liberalization options see
Burfisher et al. (2001).
48
April 2005
European Union (EU) is also extensive. The
model also removes intervention prices (which
entail government purchases and then export subsidies), variable import levies, compensatory
payments, acreage set-asides, and base-area
bounds (which limit the total area of grains and
oilseeds by cutting off payments if the base-area
bound is reached). In addition, EU production
quotas for raw milk and sugar are removed.
Full elimination of all trade-distorting policies
(as defined according to the WTO) can be viewed
as an upper bound on possible U.S. production
changes due to a WTO/Doha trade liberalization
agreement, as the final extent of elimination of
trade-distorting policies under a WTO/Doha trade
agreement is impossible to predict. Arguably,
then, the most fruitful path for quantitative analysis is to examine the scenario of full elimination
of trade distortions, which would likely result in
the largest production and environmental impacts.
Policy Simulations
In our simplified illustration (recall Figure 1), we
depicted a price-taking country that cannot influence world prices. However, the United States is
a major supplier of many commodities, and large
adjustments to policy change are likely to have
implications for world prices. We capture this in
the import and export demand equations, which
are shifted in the U.S. regional model to replicate
as closely as possible the estimated ex post price
and quantity adjustments following trade liberalization (Table 2). This first simulation is termed
scenario T, indicating the adjustments to production and agri-environmental impacts following
WTO trade liberalization in agriculture. Following this simulation, nondegradation standards are
added to the model corresponding to E2 in Figure
1. Scenario T+N represents a trade liberalization
scenario where the amount of nitrogen and phosphorus runoff, pesticide use, and sheet and rill
erosion are held to ex ante national levels. Scenario T+R represents a trade liberalization scenario where the amounts of these same pollutants
are held to ex ante regional levels. These correspond to shifting the emission function to G1 in
Figure 1. Note that import and export demand
functions are adjusted in the regional agricultural
model to replicate the price and quantity changes
estimated by the ERS/Penn State WTO trade
model for the initial scenario (T ). The two scenar-
Agricultural and Resource Economics Review
ios with trade and environmental policy interactions utilize these adjusted demand functions and
capture the initial trade impacts of production and
price adjustments, but do not explicitly re-model
global trade levels and prices using the ERS/Penn
State WTO trade model. Therefore, to the extent
that U.S. environmental policies will continue to
reverberate in global commodity markets, subsequent world price adjustments are not fully captured in our modeling framework.
Agri-Environmental Results and Implications
Economic Impacts
The results suggest that net returns to agricultural
production closely follow the pattern illustrated in
our simple graphical representation. Returns to
production increase under trade liberalization, but
consumer surplus falls, reflecting the fact that
domestic consumers are facing higher commodity
prices following trade liberalization, albeit by a
smaller percentage compared to increases in net
returns (Table 3). Regionally, the largest value
increase in net returns occurs in the Corn Belt,
and the largest impacts on consumers occur in the
most populous areas, i.e., the Northeast and Pacific regions. Under trade liberalization and nondegradation standards we find that in general returns to production are actually marginally higher
(by as much as $120 million). This is primarily
due to the increase in no-till cultivation that occurs under the two environmental scenarios,
which is likely to be more profitable in the short
run compared to conventional tillage under environmental constraints.3 The decline in consumer
surplus is also marginally higher (by $6 million)
with environmental restrictions.
Changes in U.S. Cultivation
The largest adjustments to trade liberalization will
likely occur when there are no environmental
standards imposed (Table 4). Cropped acres
might increase by about 1.6 million acres, most of
which are likely to be conventionally tilled. For
3
Even though conventional tillage is not necessarily the most profitable means to cultivate crops for all farmers, it is nevertheless an
option used by many farmers, for many reasons (Hopkins and Johansson 2004). For example, our model does not incorporate possible longrun increases in management or chemical costs associated with no-till
management, which may explain why some producers continue to use
conventional tillage techniques.
Greener Acres or Greener Waters? 49
Johansson, Cooper, and Vasavada
Table 3. Changes in Economic Indicators Following Trade Liberalization (million $)
Regiona
Scenariob
NE
LS
CB
NP
AP
SE
DL
SP
MTN
PC
US
Change in net returns to agricultural production
T
390
333
1,667
435
513
527
471
184
180
-84
4,615
T+N
392
355
1,686
464
509
528
473
196
186
-54
4,734
T+R
391
359
1,685
485
512
528
472
197
187
-77
4,739
T
-2,512
-843
-1,600
-242
-1,112
-1,360
-416
-1,013
-758
-1,801
-11,657
T+N
-2,513
-843
-1,601
-242
-1,112
-1,361
-417
-1,014
-758
-1,801
-11,661
T+R
-2,513
-844
-1,601
-242
-1,112
-1,361
-417
-1,014
-758
-1,802
-11,663
Change in consumer surplus
Note: Source for base units taken for year 2010 and discounted to 2004 dollars using a discount rate of 5.02 percent (USDA
2003).
a
Region definitions: NE (Northeast) = CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, and VT; LS (Lake States) = MI, MN, and
WI; CB (Corn Belt) = IA, IL, IN, MO, and OH; NP (Northern Plains) = KS, ND, NE, and SD; AP (Appalachia) = KY, NC, TN,
VA, and WV; SE (Southeast) = AL, FL, GA, and SC; DL (Delta) = AR, LA, and MS; SP (Southern Plains) = OK and TX; MTN
(Mountain) = AZ, CO, ID, MT, NM, NV, UT, and WY; PC (Pacific) = CA, OR, and WA; US (United States).
b
Scenario definitions: T = global agricultural trade reform only; T + N = trade reform and national non-degradation environmental policy; T + R = trade reform and regional non-degradation environmental policies (all estimated monetary values are in 2004
dollars).
the most part, technique and composition adjustments can mitigate environmental parameters at
low cost or at a profit. For example, the increase
in acres using no-till residue management increases by a larger percentage with environmental
restrictions than without. The amount of additional acres coming into production after trade
liberalization also falls slightly with the imposition of national- and regional-level nondegradation policies for nutrients, pesticides, and erosion,
which implies more intensive management of
cropping enterprises.
Water Quality Parameters
Overall, in percentage terms, changes in the
amount of nitrogen discharge, phosphorus discharge, and erosion predicted by the model are
generally less than one percent (with pesticide use
increasing by 1.4 percent), indicating that agricultural trade liberalization may likely have little
overall impact on the environment. Nevertheless,
changes in total acres and acreage under the various tillage practices do help explain some of the
environmental changes that might occur under
various trade liberalization scenarios (Table 5).
If additional acres are brought into production
following trade liberalization, the amount of nitrogen and phosphorus runoff, pesticide use, and
erosion will increase if there are no environmental policies to restrict their discharge. For
example, the largest change in planted acres occurs in the Northern Plains region across all scenarios. The changes in nutrient discharge are
largest in this region. Nitrogen lost to water resources might increase in this region by as much
as 35 million pounds in the absence of nondegradation policies. However, even if a region does
not necessarily have a large increase in planted
acres, it can still experience increasing runoff due
to changes in tillage and crops. Even though
planted acreage increases by about one percent in
the Appalachia region, pesticide use increases by
nearly 6 percent.
With environmental standards, it is possible to
reduce the potential increases in national and regional runoff at minimal cost. For example, in the
Northern Plains, net returns may increase (over
and above trade-only increases) by between $30
and $55 million under nondegradation standards.
This is accomplished by adjusting the regional
distribution of corn, sorghum, wheat, and soybean operations, and by using no-till practices.
Moreover, while the value of these environmental changes is not known with certainty, they
have value to society. For example, a conservative estimate of the value of reducing sheet and
rill erosion is $2 per ton (Ribaudo et al. 1990).
50
April 2005
Agricultural and Resource Economics Review
Table 4. Changes in Tillage Practices Following Trade Reform (millions of acres)
Regiona
Scenariob
NE
LS
CB
NP
AP
0.1
0.0
0.3
0.2
-0.1
SE
DL
SP
MTN
PC
US
0.0
0.0
0.1
0.0
0.7
Conventional
T
0.0
T+ N
0.1
-0.1
0.6
0.1
0.0
0.0
0.0
-0.2
0.3
-0.1
0.7
T+ R
0.1
0.0
0.6
-0.2
0.0
0.0
0.1
-0.1
0.2
0.0
0.7
T
0.0
0.1
0.2
-0.1
0.0
0.0
0.0
0.1
0.0
0.0
0.3
T+ N
0.0
0.1
-0.1
-0.3
0.0
0.0
0.0
0.1
0.0
0.0
-0.2
T+ R
-0.1
0.1
-0.3
-0.3
0.0
0.0
0.0
0.2
0.0
0.0
-0.5
Mold-board
Mulch
0.0
0.1
0.2
-0.1
0.3
0.0
0.0
0.0
0.0
0.0
0.4
T+ N
-0.1
-0.3
-0.4
0.1
-0.2
0.0
0.0
0.1
-0.1
0.0
-0.8
T+ R
-0.1
-0.2
-0.4
0.2
0.0
0.0
0.0
0.1
0.0
0.0
-0.5
0.0
-0.1
-0.3
0.8
0.0
-0.1
0.0
0.0
0.0
0.5
T
No-till
T
0.0
T+ N
0.0
0.3
0.4
1.0
0.0
0.0
-0.2
0.0
0.0
0.0
1.5
T+ R
0.0
0.2
0.4
0.9
0.0
0.0
-0.1
0.0
0.0
0.0
1.4
Ridge-till
T
0.0
0.0
0.0
-0.2
0.0
0.0
0.0
0.0
0.0
0.0
-0.2
T+ N
0.0
0.0
0.0
-0.2
0.0
0.0
0.0
0.0
0.0
0.0
-0.2
T+ R
0.0
0.0
0.0
-0.2
0.0
0.0
0.0
0.0
0.0
0.0
-0.2
All tillage types
T
0.1
0.1
0.4
0.7
0.1
0.0
-0.1
0.1
0.1
0.0
1.6
T+ N
0.0
0.1
0.4
0.7
-0.1
0.0
-0.2
0.0
0.1
-0.1
1.0
T+ R
0.0
0.1
0.2
0.3
0.0
0.0
0.0
0.1
0.1
0.0
0.9
Note: Source for base units taken for year 2010 (USDA 2003).
a
Region definitions: NE (Northeast) = CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, and VT; LS (Lake States) = MI, MN, and
WI; CB (Corn Belt) = IA, IL, IN, MO, and OH; NP (Northern Plains) = KS, ND, NE, and SD; AP (Appalachia) = KY, NC, TN,
VA, and WV; SE (Southeast) = AL, FL, GA, and SC; DL (Delta) = AR, LA, and MS; SP (Southern Plains) = OK and TX; MTN
(Mountain) = AZ, CO, ID, MT, NM, NV, UT, and WY; PC (Pacific) = CA, OR, and WA; US (United States).
b
Scenario definitions: T = global agricultural trade reform only; T + N = trade reform and national non-degradation environmental policy; T + R = trade reform and regional non-degradation environmental policies.
Therefore, the benefits of a regional nondegradation constraint on soil erosion could be as high as
$16 million ($2 x 8 million tons of erosion),
which exceeds the reduction in consumer surplus
associated with trade liberalization alone versus
trade liberalization with environmental constraints ($6 million).
Conclusions
U.S. law mandates that the federal government
perform environmental assessments of all pro-
posed trade agreements (U.S. Executive Order
13141, 1999). Because the federal government
has little experience to date in estimating environmental consequences of agricultural trade
agreements, our approach can serve as one model
for such studies by others.
We also explore how nondegradation standards
for agricultural externalities might influence producer adjustments to trade policy. Our results
suggest that under a post-Doha trade liberalization scenario, agricultural trade liberalization is
likely to affect the environment in a variety of
Greener Acres or Greener Waters? 51
Johansson, Cooper, and Vasavada
Table 5. Changes in Environmental Quality Following Trade Reform (millions of units)
Regiona
Scenariob
NE
LS
CB
NP
T
3.7
9.8
19.2
35.2
AP
SE
DL
SP
MTN
PC
US
0.8
-0.5
79.2
Nitrogen losses to water (lbs.)
8.2
0.6
-1.5
3.7
T+ N
1.3
-4.8
12.8
20.8
-7.2
-0.3
-10.4
-6.0
3.4
-5.6
4.1
T+ R
0.0
-0.8
-0.1
1.8
-0.8
0.1
-0.4
1.0
0.6
-0.2
1.2
T
0.6
0.2
2.5
2.5
0.9
0.0
-0.3
0.3
0.0
0.0
6.7
T+ N
0.4
0.0
1.7
0.3
-0.7
-0.1
-1.2
-0.4
-0.1
0.0
-0.1
T+ R
0.1
0.1
0.1
-1.8
0.0
0.0
-0.2
0.1
-0.1
0.0
-1.9
Phosphorus losses to water (lbs.)
Total pesticide use (lbs. active ingredient)
T
0.1
0.4
1.3
2.0
1.6
-0.1
-0.1
0.1
0.1
0.0
5.4
T+ N
0.0
-0.3
0.3
1.4
-0.5
-0.1
-0.5
-0.1
0.0
-0.2
0.0
T+ R
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
-0.1
T
0.5
0.5
4.1
1.3
-0.5
0.2
0.0
0.0
6.4
Sheet and rill erosion (tons)
0.3
0.0
T+ N
0.4
-0.2
2.0
-0.3
-0.5
-0.2
-1.5
-0.3
0.0
0.0
-0.6
T+ R
0.0
-0.1
-0.3
-1.3
-0.1
-0.1
-0.4
0.0
0.0
0.0
-2.4
a
Region definitions: NE MI, CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, and VT; LS (Lake States) = (Northeast) = KS, IA,
IL, IN, MO, and OH; NP (Northern Plains) = MN, and WI; CB (Corn Belt) = AL, KY, NC, TN, VA, and WV; SE (Southeast) =
ND, NE, and SD; AP (Appalachia) = OK and TX; AR, LA, and MS; SP (Southern Plains) = FL, GA, and SC; DL (Delta) = CA,
OR, and AZ, CO, ID, MT, NM, NV, UT, and WY; PC (Pacific) = MTN (Mountain) = WA; US (United States).
b
Scenario definitions: T global agricultural trade reform only; = T + N trade reform and = national non-degradation environmental policy; T + R = trade reform and regional non-degradation environmental policies.
ways, some positive and others negative. Nondegradation standards at the national or regional
level can prevent harmful environmental impacts,
while leaving producers’ gains to trade relatively
unaltered.
Our modeling framework contains many of the
agri-environmental indicators that are traditionally the focus of U.S. agricultural policy. However, the set of indicators is by no means complete, nor do we have good estimates of their
value to society. Our results indicate that the
value of restricting the amount of sheet and rill
erosion alone may be greater than the potential
costs to consumers and producers when adjustments to agricultural trade liberalization are constrained by nondegradation standards. Future research extensions could incorporate environmental impacts (and valuation thereof) due to
changes in greenhouse gas emissions, manure
nutrient and bacterial discharges, and emissions
of pollutants associated with fuel usage, as well
as environmental amenities associated with agricultural production.
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