Economics of Pest and Disease Outbreaks in Tree Fruit...
52
Economics of Pest and Disease Outbreaks in Tree Fruit Xiaojiao Jiang Postdoctoral Associate School of Economic Sciences Washington State University Hulbert Hall 101 PO Box 646210 Pullman, WA 99164-6210 Email:[email protected] Thomas L. Marsh Professor and IMPACT Director School of Economic Sciences Washington State University Hulbert Hall 101 PO Box 646210 Pullman, WA 99164-6210 Email: [email protected] The funding for this project was provided by the United States Department of Agriculture APHIS program and Washington State University IMPACT Center.
Transcript of Economics of Pest and Disease Outbreaks in Tree Fruit...
Economics of Pest and Disease Outbreaks in Tree Fruit
Xiaojiao Jiang
Postdoctoral Associate
School of Economic Sciences
Washington State University
Hulbert Hall 101
PO Box 646210
Pullman, WA 99164-6210
Email:[email protected]
Thomas L. Marsh
Professor and IMPACT Director
School of Economic Sciences
Washington State University
Hulbert Hall 101
PO Box 646210
Pullman, WA 99164-6210
Email: [email protected]
The funding for this project was provided by the United States Department of Agriculture
APHIS program and Washington State University IMPACT Center.
1
Economics of Pest and Disease Outbreaks in Tree Fruit
Abstract
A conceptual intertemporal economic model is specified with dynamic supply response
for a single tree fruit, as well as consumer demand and international trade. An
equilibrium displacement approach is calibrated for the U.S. pear industry to simulate
baseline and empirical scenarios from domestic and international shocks due to pest or
disease outbreaks. Hypothetical fire blight outbreaks are simulated to compare the stream
of outcomes and the measures of economic welfare of producers and consumers. More
severe fire blight outbreaks worsen the consequences for the pear industry. For a given
level of fire blight outbreak, scenarios with trade sanction and consumer shock exhibit the
highest magnitudes of economic impacts, followed by scenarios with only domestic
supply shocks, and then scenarios with trade sanction.
Keywords: tree fruit, perennial crops, pest and disease outbreak, simulation model
JEL Codes: F17, Q11, Q17, Q18
2
Economics of Pest and Disease Outbreaks in Tree Fruit
1. Introduction
In recent decades trade in tree fruit has expanded, and with that growth increasing
challenges have arisen related to pest and disease outbreaks in tree fruit (Calvin, Krissoff
and Foster 2008; Yue and Beghin 2009). Pest and disease events lead to immediate direct
losses for fruit growers in the form of reduced fruit quantity and/or quality, decreased
bearing acres in orchards, and plus the costs of control and mitigation. Short-run and
long-run losses can be influenced by fruit quarantines or government policies and
regulations (Zhao, Wahl and Marsh 2007). Policymakers are often left to allocate
resources in efforts to prepare for or respond to outbreaks but have limited insight into
economically efficient means by which to proceed.
Pest and disease outbreaks also disrupt international trade of tree fruit. The Sanitary
and Phytosanitary Measures (SPS) Agreement and the Technical Barriers to Trade (TBT)
Agreement allow countries to set standards to protect plant and human health.
International shipments are accepted only under specific SPS requirements written into
the agreements between importing and exporting countries. Varying perceptions about
the risks of pest and disease events and dynamic political outcomes lead to different
magnitudes and time periods of trade sanctions between countries. Importing countries
often overestimate the risk of pest and disease transmission and impose high standards in
her technical barriers (Yue, Beghin and Jensen 2006).
Previous literature on pest and disease outbreak in tree fruit focuses on U.S. apple
exports to Japan for fire blight issue (Calvin and Krissoff 1998) , U.S. apple exports to
Japan subject to codling moth protocol (Calvin, Krissoff and Foster 2008), U.S. imports
3
of avocados from Mexico and pest infestations (Disdier, Fontagné and Mimouni 2008),
potential for Australia imports of New Zealand apples (Yue and Beghin 2009), and
maximum residue levels (MRL) of pesticides on trade of apples and pears (Drogué and
DeMaria 2012). We analyze heterogeneous impacts (consumption, production, value
added, and trade) from vertical market distortions and economic impact, including
welfare changes, and intertemporal effects, due to hypothetical pest and disease outbreaks.
A model with vertical supply chain for tree fruit is specified. The tree fruit supply
chain stretches from growers and input suppliers downstream to fresh packers and
processors and then to wholesalers distributing products to domestic and international
consumers. Wohlegnant and others have demonstrated the importance of vertical market
recognition in modeling commodity markets. Accounting for both fresh and processed
markets and associated economic agents along the supply chain allows from more
complete and accurate economic assessment (Paarlberg, Seitzinger, Lee et al. 2008).
Moreover, the impacts of pest and disease outbreaks can be felt along the supply chain
from changes in input mix, output quality and quantity, and bearing acreage to fresh and
processed market utilization and by final consumers in domestic and foreign markets.
An intertemporal model with dynamic supply is specified for several important reasons.
First, trees are durable goods and are appropriately modeled in a capital theoretic
framework (Labys 1973). For instance the long life cycle of perennial trees tend to make
suppliers less responsive to market prices in the short term. However, sudden shocks to
the production system can cause wide fluctuations in fruit market in the long run (Zhao,
Wahl and Marsh 2007). Second, pests and diseases may be durable over time as well with
short term and the long term impacts (Marsh, Huffaker and Long 2000). Mitigation and
4
control costs, as well as trade barriers may depend on the intertemporal management of
the pest or disease. Third, bearing acreages of tree fruit are reflected in a dynamic process
(French, Gordon and Minami 1985). More specifically, pest and disease outbreaks may
cause direct damage by reducing yield and decrease in bearing acreage or lead to
government intervention that removes affected bearing acreage. Either case induces
intertemporal changes in benefits and costs. Finally, importers of tree fruit tend to
impose sanctions quickly but remove sanctions more slowly. The time length to remove
sanctions is often dependent upon the political nature and dynamics between the
importing and exporting countries.
Economic surplus analysis has been used to estimate costs of pest and disease
outbreaks in both plants and animals (Petry, Paarlberg and Lee 1999; Cembali, Folwell,
Wandschneider et al. 2003; Paarlberg, Lee and Seitzinger 2003; Paarlberg, Seitzinger and
Lee 2007; Zhao, Wahl and Marsh 2007; Paarlberg et al. 2008). This approach not only
quantifies production and consumption but also provides measures of welfare changes.
The current article provides important information for both policy makers and the fruit
industry by quantifying consumer and producer surplus. First, understanding the
distribution of benefits and costs is essential for guiding resource allocations. Pest and
disease events are public good problems complete with externalities. Thus we can expect
private control efforts at lower than efficient levels. Recognition of this problem can be
seen when public organizations as well as private growers invest to mitigate pest and
disease events. Quantified outcomes can help to prioritize and plan for, respond to,
mitigate, and clean up after such events. Second, our analysis can provide an economic
assessment (changes in producer and consumer surplus) of trade distortions from such
5
outbreaks, and provide valuable knowledge for resolving trade disputes. Third, we
identify regional impact from the pest and disease outbreak. Regional economic
assessments can provide better information for mitigation and policy formulation.
The objectives of this study are twofold: 1) to develop an intertemporal model of tree
fruit demand and supply with international trade to estimate the economic consequences
of a disease or pest introduction or outbreak (henceforth termed outbreak) in tree fruit; 2)
to construct an empirical model that calculates the economic consequences of a disease
and/or pest outbreak in selected tree fruit (pears) and that measures consumer and
producer welfare effects from such outbreaks. The conceptual model is general enough to
apply to other tree fruit (e.g., apples, cherries, plums or peaches and nectarines) with
recalibration of the model.
The study will proceed in the following manner. First we provide background
information on tree fruit for both fresh and processing use. Second, we present a model
specification for selected tree fruit demand and supply at the farm level and the retail
level, with an international trade component. Third, we report data and simulation
methods. Finally, we discuss the results and conclude the paper.
2. Background
The United States ranks as the world’s second largest pear producer. According to the
2011 National Agricultural Statistics Service, the largest pear producing states in terms of
bearing acreages in the United States are, in descending order, Washington (48%),
Californian (26%), and Oregon (24%). The top three states essentially comprise 98% of
the entire U.S. pear supply. Yield per acre and bearing acreage together determine total
production. Yield per acre has an upward trend from 4.2 tons per acre to 16.52 tons per
6
acre with variation in some years related to orchard management and technology change,
weather, and pest and disease outbreaks. Bearing acreages fluctuate but have declined
over last 80 years. The total production of pears has been relatively stable over the past
three decades, with declining total bearing acreage approximately balanced off by
increasing yield per acre. Once tree fruit is harvested, it is allocated to either the fresh or
processed market. Production composition of U.S. pears has averaged 54% for fresh
market and 46% for processed market from 853,700 tons in 1980 to 802,000 tons in 2010
with pears increasingly allocated to the fresh market, which reached 65% in 2010. Pears
satisfying quality standards go to the fresh market which commands higher prices, while
contractually committed and residual fruit go to the processed market with lower prices.
Domestic markets for fresh and processed pears have been changing over time. Per
capita consumption of fresh and processed pears exhibits a declining trend from 6.25
pounds in 2001 to 4.86 pounds in 2010. The declining trend is especially strong for
processed pears. For example, per capita U.S. consumption in 2010 for fresh and
processed pears was 2.9 pounds and 1.96 pounds (fresh equivalent weight), compared to
2001 with 3.25 pounds and 3 pounds, respectively (USDA-ERS, 2012).
International markets for both fresh and processed pears play a more important role in
pear trade. Rising export prices coupled with more pears allocated to fresh use and
decreasing domestic consumption have led to speculation that U.S. growers may be
relying more on foreign exports to move their fresh pears. The United States is a net
exporter of fresh pears, and exported 188,346 tons in 2011, a 12% increase compared to
2010. According to USDA-FAS, U.S. fresh pears reach almost 100 countries and the
largest markets for U.S. fresh pears are Canada, Mexico, Brazil, and Russia, in
7
descending order. While the United States is a major supplier of pears in the domestic
market, pears from Argentina, Chile, South Korea, and other nations also enter the
market. Previous research shows that the majority of imported pears enter the U.S.
market during the off season from domestic production (Cook 2002; Zhang, Marsh and
Schotzko 2007).
Historically the United States had been a net exporter of processed pears, and it
became a net importer after 2000. Canada and Mexico are two of the largest foreign
importers for processed pears, accounting for approximately 80 percent of U.S. total
exports since 2007. In 2011, the United States imported processed pears from 23
countries. Approximated 94% of those were from China, who is the largest producer over
the world. All of those importing countries except Canada impose either tariff barriers or
non-tariff barriers on U.S. pears (Newhouse and Hamilton 2010). They set a tariff or
equivalent tariff rate between 5% and 60.3% for U.S. pears. Non-tariff barriers are in the
form of SPS and other restrictions. According to SPS agreement, a country may impose
bans, quarantine measures, or other trade restrictions on products from a trading partner
that bear potential risks posed by exotic pests and diseases to human, animal, and plant
life or health. For example, Argentina, Australia, China, Indonesia, Japan, South Africa,
South Korea, Taiwan, and Vietnam impose sanctions on pears imported from the United
States.
3. A Conceptual Model of Tree Fruit
An intertemporal economic model is presented for a single tree fruit crop. Tree fruit
supply is dynamic accounting for the durable nature of trees. The model is structured to
allow economic assessment of shocks (e.g., pest and disease outbreaks) at different levels
8
along the supply chain (production, value added, and trade) and of trade policy for
differentiated end uses. It measures consumer and producer welfare effects over time
from shocks such as, but not limited to, pest and disease outbreaks. Unlike Zhao, Wahl,
and Marsh we do not explicitly integrate a pest or disease spread model but rather treat
outbreaks as shocks to the economic system as in Paarlberg et al (2008).
Overview of the Model
The model is comprised of three components, supply, demand, and international trade.
On the supply side a dynamic supply response model is necessary to capture the perennial
nature of tree fruit. After planting, a fruit tree takes several years to mature and then it is
commercially productive for several decades. Total supply per period is defined as
bearing acreage times yield per acre. On the demand side, demand is aggregated by
population from the individual demand model. Aggregate per capita demand models
provide parameters that can be used to estimate gross and net benefits to the industry
from shocks. Imports and exports are linked through market clearing conditions that clear
prices at the wholesale level with several available trade policy instruments.
In our model of domestic and international supply and demand for fruit, we
differentiate between fresh and processed fruit. Each season fruit from growers to
consumers passes through four vertical markets: the farm level, a packing or processing
level, a wholesale level and a retail level. Fresh and processed fruit allocation is
determined by producer prices and total production. Fresh fruit markets exhibit higher
prices and therefore after the fresh demand has been satisfied the remaining fruit goes to
the processed market at lower prices. At the wholesale level, fruit is allocated to domestic
or international markets. Supply and demand are linked through market clearing
9
conditions that clear wholesale prices each time period. Farm level and retail level prices
are identified through marketing margins.
A hypothetical fire blight outbreak can be decomposed into several separate potential
shocks (Paarlberg et al. 2008). Supply shocks can result from reducing bearing acreages
and decreasing yields. Trade sanction shock results from total loss of fruit exports either a
United States trade embargo or importers decrease or even refuse to import fruit from the
United States. Consumer shocks result from potential adverse consumer reaction to
contaminated tree fruit products. These exogenous shocks’ are reflected by changes in the
new equilibrium prices of the model and induce upstream dynamic supply responses. The
estimated dynamic supply response along with partial equilibrium solution each year
allows us to compare the simulation results with and without outbreak events over time.
To empirically estimate the model we construct an equilibrium displacement model for
from the theoretical model. The logarithmic differential version is advantageous because
the differential version is driven by elasticities. The logarithmic differential version can
also be applied to observed historical data, and base data can be updated as new values
are available.
Supply, Demand and International Trade
Primary Farm Level Supply
The production of perennial fruit involves planting, yield, and removal. A model needs
to provide a structural base for estimating response relationship that encompasses these
dimensions (French, Gordon and Minami 1985). Several studies have considered age
distribution effects in providing separate estimates of new planting and removal
equations. See French and Matthews (1971) for asparagus, French, Gordon and Minami
10
(1985) for peaches, Akiyama and Trivedi (1987) for tea, Elnagheeb and Florkowski
(1993) for pecans, and Zhao, Wahl and Marsh (2007) for apples.
Compared with annual crops, perennial crops involve not only removal, but also
replacement of plants. Growers plants new trees and remove existing acreage to
maximize profits over a time horizon. The acreage in individual age categories evolves
depending upon existing acreage, new planting, and removals. Consequently a perennial
model must consider lags between input and output, and effects of populations of bearing
plants on production.
The bearing acreage equation for fruit trees is given by
1 0
1 ,j j j
t t t t tK K RM K NP
(1)
The total acreage of trees in year t is0
uj
t
j
K
, where j
tK is the acreages of age j trees at
year t. Let tNP denote new plantings in year t, j
tRM denote removals of age j trees at year
t. Assuming pear trees start to bear fruit at age 3 and maximum life time of a tree is ,
then tB is the bearing acreage
3
j
t t
j
B K
(2)
which is summation over acreages of different ages of trees that bear significant quantity
of commercial crop at period t . The change in bearing acreage is then
0
1 2
3
j
t t t t t
j
B B B NP RM
(3)
Yield per acre of mature fruit trees reflects the influence of management and
technological changes. For example, the rootstock structure for pears changes over time.
11
Current growers prefer to plant dwarf and semi-dwarf pear trees instead of standard trees.
The rootstock structure changes yield per acre of pear trees in the long run. The effects of
technological changes are measured by a trend variable (a function of time or as a
dummy variable). To capture these characteristics, we assume the expected yield of
mature fruit can be written as a trend model of the following form: , ,1n
r t n r typ g yp
where ,r t nyp
is the projected yield per mature bearing acre in year t+n at region r, which
is equal to the value in the base year ,r typ scaled by a growth rate g.
Primary farm level supply for pears can be obtained by multiplying the bearing acreage
and yield by each region
, 1 , 1 , 1 , , 1 , ,( ) (1 )r t r t r t r t r t r t r t rFS B yp B S B yp g (4)
where ,r tFS is the total supply at region r at year t, ,r tB is bearing acreages at region r at
year t,,r typ is yield per acre at region r at year t,
,r tB is the change in bearing acreages
at region r,, 1r tS
is exogenous shock due to pest or disease outbreak on bearing acreages.
Allocation either to fresh or processed market is determined by producer prices, with
observed fresh market prices higher than the processed market prices. Gao and O'Rourke
(1992) analyzed demand for Pacific Coast pears which separately identified Bartlett and
winter pears in fresh and processed uses. They point out that production would affect
pear price and the optimal allocation of pears to the fresh or processing markets. Farm
level demand for i at region r is given as
, , , ,( , , )F F
i r i r f r p rFD FP p p FS (5)
12
where ,i f p indicate fresh and processed use; r = 1, …, R denote the regions; ,i rFD is
the quantity demanded at the farm level for use i at region r, ,
F
i rP is the farm level price
for use i at region r.
At processed fruit market, the price maybe set before the crop production season. For
example, the price for Bartlett processing pears grown in the Pacific Northwest has been
set for the next three years (2012 to 2014) at 2011 through negotiations between
Washington Oregon Canning Pear Association and processors (Warner 2012). In practice,
allocation of fruit to fresh markets will be considered as a function of total production
and price for the fresh fruit. The allocation of fruit to the processed market will be the
total production minus the quantity distributed to fresh markets.
, , ,( , )F
f r f r f rFD FP p FS (6)
, ,p r f rFD FS FD (7)
Retail-level demand and supply
Individual demand model
Let q denote a n-vector of commodities consumed with a n-vector of corresponding
prices p and income I. We define z to be other shift or conditioning variables. Then the
consumer’s maximization problem is
max{ ( ; ) ' }x
u q z p x I
where ( ; )u q z reflects the individual’s utility function with appropriate properties1. Then
the individual consumer’s demand for a vector of commodities can be represented by
1 See Deaton and Muellbauer (1990) for background details related to demand systems
13
( , ; )q f p I z . This implies that demand for fresh and processed fruit should be a
function of the price of the fresh and processed fruit, their substitutes or complements,
and other shift variables, such as a demand shift from consumer shocks due to a pest or
disease outbreak.
Under the assumption that consumers have a homothetic utility function, the individual
demand functions represented above can be linearly aggregated,
1
pop
k
Q q
, by region in
the United States to construct aggregate quantity demanded by region.
The per capita demand function for i at region r is given by
, , , , ,( , , , , )D D R R
i r i r f r p r o r rq f p p p I z
(8)
where ,
D
i rq is the per capita consumption of the retail fruit i demanded at region r ;
, , R R
f r p rp and p are retail level prices for fresh and processed pears at region r ; ,o rp is a
vector of prices for all other substitutes or complement, if any, for the fresh and
processed fruit; rI is the per capita income at region r; z is a demand shift for consumer
shock because of a pest or disease outbreak. Possible alternative substitute fruit varieties
for pears include apples and oranges (Arnade and Pick 2000). In their empirical analysis,
orange prices in their demand model for pears showed statistical insignificance and
apples prices were statistically significant.
Reduced consumption of fruit can come from aversion to pesticides and lower fruit
quality. Fresh fruit has come under particularly close scrutiny because of the chemicals
used to control pests or plant diseases. Health risks associated with pesticide residues on
produce have been of concern for many years. We assume that fewer consumers would
14
purchase fruit if pest or disease occurs. We introduce parameters to capture the consumer
shocks for fresh and processed fruit in the model structure.
Total consumption of fresh or processed fruit in each region of the United States
depends on per capita consumption multiplied by the scalar population, rpop , and a
scalar ,i r , ,0 1i r ,of parameters that indicate the share of the population unafraid of
a health risk associated with the fruit disease at region r.
National retail level demand is given by
, , ,
1 1
R RD
i i r r i r i r
r r
QD QD pop q
(9)
And regional retail level demand is given by
, , ,
D
i r r i r i rQD pop q (10)
where,i rQD denotes total consumption for i at region r and rpop is the population at
region r.
The total production supplies are intended for both export and domestic markets. Retail
level supply for domestic consumers includes domestic utilized production plus imported
fruit and excludes those fruit demanded by international market (export).
International Trade
Trade is linked to U.S. market prices, trade policy, and disease outbreaks. We introduce
two parameters to capture specific trade interventions and severity of trade restrictions.
Trade is determined by the U.S. domestic price along with the specific trade intervention.
Exports depend on prices and trade interventions and, in some cases, on the disease
15
outbreak. Of central interest are the effects of exogenous shocks in the international
market on the prices of a country’s domestic farm outputs.
As pest and disease outbreaks can disrupt trade, parameter ie , ranging from 0 to 1, is
used to indicate the severity of trade restrictions imposed by foreign importers. Let ,i rE be
the quantity demanded by international market from region r then
, , ,( )Ex W SPS
i r i i r i r iE f p c e (11)
where ( )Ex
if is the excess supply function for fruit i, ,
W
i rp is wholesale level prices for fruit
i at region r, and ,
SPS
i rc is the cost imposed by the SPS conditions. Peterson, Grant, Roberts
and Karov (2013) investigate the trade impact of different pest-mitigation measures using
a product-line gravity equation for fresh fruit and vegetables and they find that
phytosanitary treatments generally reduce trade.
The import demand for foreign fruit products, if the imported fruit and domestically
produced fruit are homogeneous, is also a function of the domestic price. Define
parameter im , ranging from 0 to 1, is used to indicate the severity of trade restrictions.
Let iM is the quantity of the imported fruit at region r, then
Im
, ,( )W
i r i i r i iM f p tm m (12)
Where Im ( )if is the excess demand function for fruit i, itm be a vector of trade
interventions.
National retail level supply is given by
, , ,
1
R
i i r i r i r
r
QS FS M E
(13)
16
And regional retail-level supply is given by
, , , ,i r i r i r i rQS FS M E (14)
where,i rQS is quantity of the retail-level supply for fruit at region r,
,i rFS is the domestic
produced fruit at region r,,i rM is the quantity imported by region r from the international
market, and,i rE is quantity demanded by the international market from region r.
Intermediaries
Pears realize value added from growers to consumers through packers and processors.
Wholesalers play an important role in connecting farm level and retail level markets. It is
reasonable to use a marketing margin to connect the vertical markets as long as evidence
shows that no market power exists that will affect our analysis.
Wann and Sexton (1992) showed that producer prices in the U.S. pear market are not
significantly different from marginal costs. We assume that the market for selling
producer fruit is competitive and that producer prices equal producer marginal costs. This
assumption of competitive markets at the producer level is supported by the fact that
there are 1,640 pear growers in Oregon and Washington and none of the pear growers are
large (Carman, Klonsky, Beaujard et al. 2004; Winfree, McCluskey, Mittelhammer et al.
2004).
Carman et al. (2004) reported that there are approximately 70 handlers of winter pears
in Oregon and Washington, none of which are large operations. Meanwhile, the largest
individual shippers each handle 6 to 8% of all pears. Winfree et al. (2004) pointed out
that more than 80% of the sales and marketing of pears is handled by fewer than ten sales
desks or packers in the Northwest D’Anjou pear industry but oligopoly power is not an
17
issue. This is because the packers do not actually buy the pears from the growers. Rather,
they provide marketing services. They also found that the Northwest D’Anjou pear
industry has had some degree of oligopoly power when the new crop first enters the
market and when flows of shipments from imports and/or other pear varieties are low.
Arnade and Pick (2000) pointed out that estimating oligopoly power using annual data
may generate results that suggest a competitive market when in fact seasonally varying
oligopoly power may exist.
The relationship between grower received price and wholesale price is given by
, ,
F W F
i r i i rp p MM (15)
where ,
F
i rp is the growers’ price for i at region r , and ,
F
i rMM is the markup price because of
marketing services between the farm level market and the wholesale level market.
The relationship between retail received price and wholesale received price is given by
, ,
R W R
i r i i rp p MM (16)
where ,
R
i rp is the retail level price for i at region r , and ,
R
i rMM is the markup price because
of marketing services from wholesaler to consumers.
Market Closure
Assuming the market clears at the national level, equation (14) can be rewritten as
,
1
R
i i i r i
r
QD E FS M
(17)
where iQD is the quantity demand at domestic retail market for i. Using the markup price
equation (16) and equation (17), the partial equilibrium wholesale price can be solved
18
from the marketing clearing conditions. See Appendix B for differential transformation of
the conceptual model into an equilibrium displacement framework.
4. Data
Data for each individual state were collected from the Noncitrus Fruits and Nuts
Summaries (USDA-NASS). State level bearing acreages, yield per acre, and grower
prices received were collected from USDA-NASS. Trade volume and price data for fresh
and processed pears were taken from the Foreign Agricultural Service’s Global
Agricultural Trade system (USDA-FAS). Per capita consumption of fruit was collected
from Food Availability Data System(USDA-ERS). Wholesale price for fresh pears were
downloaded from the Agricultural Marketing Service (USDA-AMS). Retail level price
for fresh pears was collected from the Bureau of Labor Statistics (USDL). Regional
population data were from Bureau of Economic Analysis, U.S. Department of Commerce.
We deflated all the prices into 2010 dollar values and used a 4% discount rate to calculate
net present values. Parameters such as elasticities are either from previous literature or
calculated by authors (Table 1).
Fire blight in Pears
Fire blight, caused by the bacterium Erwinia amylovora, is a common and devastating
bacterial disease of apples and pears, as well as other species in the Rosaceae family2.
This disease can rapidly move internally in host plant tissues. Unlike many other plant
diseases, fire blight is destructive to the current year’s crops, and it can cause permanent
damage to an orchard (Group 2005; Ellis 2008).
2 See Bonn and van der Zwet (2000) for more details about distribution and economic importance of fire
blight.
19
An epidemic outbreak of fire blight in a given geographic area has the potential to
destroy the economic viability of apple and pear production in that region. The outbreak
causes yield reductions, and it may indirectly put the processing industry at risk. An
example is the processing pear industry in Ontario. A fire blight epidemic also has the
potential of substantially increase a grower’s production cost. An infection requires
diligent and regular dormant pruning, removal of diseased branches, and spraying with an
EPA-labeled antibiotic during flowering, to reduce losses throughout the orchard. A
blight prevention program results in higher cost on the spraying equipment, labor, and the
applied chemicals. Labor already is the single largest expense to an orchard operation,
averaging about 35-40% of total expenses. A significant fire blight outbreak, and the
inability to access efficacious control tools, may result in a grower, or even a local
industry, never being able to recover from the outbreak.
Prevention and control are the two basic ways of reducing costs of an invasive pest
species (Dunley, Kupferman and Willett 2001). Optimal resource allocation between
prevention and control has been studied (Shogren 2000). The only practical control
strategy in case of an outbreak is eradication. The efforts made to eradicate pest or
disease depends on the characteristic of the pest, along with spatial factors, such as pest
population size, rate of spread, and the capacity of a regulatory authority to reduce this
rate of spread.
5. Scenarios and Results
We examine shocks from nine fire blight outbreak scenarios in the U.S. pear industry
(Table 2) and report summary results compared with a baseline scenario (Table 3, 4).
Three possible cases are considered:1) supply shocks; 2) supply and international trade
20
shocks; 3) international trade shocks. International trade shocks include SPS cost, trade
contraction, and trade expansion. For each set of scenarios, we differentiate three levels
of severity of outbreak for each case: High, Medium, and Low. The high (low) level
represents a worst (best) case outbreak event with more (less) severe trade distortion and
adverse consumer reaction. The medium level outbreak comes in between the high and
the low levels.
The model was calibrated using above discussed parameters and validated to trade
conditions for fresh and processed pears in 2002, especially for processed pears. Since
2002, China and Thailand, taking over South Africa and Australia, became main
importing countries for processed pears. Hence, the results are conditional on the trade
pattern changes. The nine scenarios were simulated and compared to the baseline
outcomes. Changes in producer and consumer welfare changes are calculated. The
baseline (scenario 1) is simulated without shocks from fire blight starting in 2002 and
continuing through two life cycles of pear trees (60 years). The long run equilibrium is
identified by pear tree planting and removal decisions (change in bearing acreage),
wholesale prices, consumption, as well as imports and exports for fresh and processed
pears. Baseline outcomes calibrated started 2002 captures variations of data from 2002 to
2010.
Scenarios 2-4: Supply Shock
Figure 1 displays the baseline grower price path and price changes from supply shock,
where a hypothetical fireblight outbreak occurs in 2015 for three scenarios: High
(Scenario 4 - 6000 acre decrease in bearing acres); Medium (Scenario 3- 4000 acre
decrease in bearing acres); and Low (Scenario 2- 2000 acre decrease in bearing acres).
21
The grower price is calculated as an average price of farm level fresh and processed pear
prices weighted by utilization production. The grower price exhibits approximately a 10
to 15 year cyclical pattern that alternates the baseline price path and eventually converges
to the long run equilibrium level. The upward trend in grower prices is driven by demand
result from assumed population growth.
As the grower prices rise over time, import demand for fresh and processed pears
increase and export demand by foreign importing countries decreases. Imports and
exports fluctuate along the baseline over time. The larger the shock from fire blight
outbreak, the farther the outbreak scenario deviates from the baseline.
Domestic consumer benefits associated with the outbreak are calculated as change in
consumer surplus relative to the baseline, reflecting the effects of both price changes and
shifts in the demand. Figure 2 shows the time path of changes in consumer surplus for
fresh pears3. The first graph in figure 2 shows the change in consumer surplus for 3
scenarios’ supply shock. Changes in consumer surplus exhibit similar patterns to grower
prices. Consumers are worse off (better off) if prices fluctuate above (below) the baseline
as a result of supply shock. Consumer losses are larger with higher shocks.
Producer and sectors benefits and/or losses associated with the outbreak are computed
as well. Change in quasi-return to capital and management to producer and sectors, which
include packers and processors, for the nine scenarios compared to baseline is illustrated
in figure 3 and figure 4. Changes in quasi-return to capital and management normally
exhibit opposite patterns to grower prices. Producers are better off (worse off) if prices
fluctuate above (below) the baseline as a result of shocks. Producers can be worse off
even if prices fluctuate above the baseline when there are severe supply shock, because
3 Figure for changes in consumer surplus for processed pears are very similar and thus, omitted.
22
production drop dominate increased prices and together they drive down quasi-return to
capital and management to growers and sectors (Scenario 4). Severe shock results in
larger loses. The positive change in revenue eventually causes an increase in the time
path of bearing acreages. Because of lag between investment decision and the realization
of that decision in the pear industry, the effect on bearing acreages is not observed until
the pear trees reach reproductive maturity. The increase in bearing acres results in
increased production, driving down prices and revenue and dissipating the benefits for
producers.
Scenarios 5-7: Trade Sanction and Contraction
Scenarios 5-7 consider importing countries impose SPS intervention and contract trade
volume at three levels in 2015 (Table 2). Unlike fluctuation along baseline in Scenarios
2-4, grower prices in scenarios 5-7 exhibit smaller than the baseline. This is because trade
shocks reduce exports. Declines in export flows are rerouted to domestic supply driving
down price. Unlike supply shock has long run impact on the production; the trade shocks
have short run impact on production. The High scenario has the largest impact. Severe
trade shocks changes lead to higher declines in export flows and therefore higher level of
domestic supply, which results in lower grower price than the baseline.
Although grower prices in scenarios 5-7 are smaller than baseline over time, grower
prices still have an increase trend; therefore, fresh and processed pears imports increase
and higher than those in baseline while exports decrease and smaller than those in
baseline. The larger the shocks, the farther imports and exports deviate from the baseline.
Domestic consumer benefits associated with trade shocks. The second graph in figure
2 shows the change in consumer surplus for fresh pears. Consumers are better off as
23
prices are lower than the baseline as a result of SPS cost and trade contraction. Consumer
benefits are larger with higher level shocks. Change in domestic consumer surplus in
scenarios 5-7 is quantitatively smaller over time compared to outbreak scenarios (2-4)
(Figure2). Change in domestic consumer surplus is positive as grower prices below the
baseline over time.
Producer and sectors losses associated with the trade shocks. The second graph in
figure 3 and 4 show the change in quasi-return to capital and management to producer
and packers. Producers, packers, and processors are worse off as prices are below the
baseline as a result of trade shocks. Severe shock results in larger loses.
Scenarios 9-10: Trade Sanction and Expansion
Scenarios 8-10 include trade shocks from SPS intervention and trade expansion
(Table2). Grower prices in scenarios 8-10 show an upward trend over time but have a
different pattern relative to scenarios 2-4. Unlike fluctuation along baseline in Scenarios
2-4 and below baseline in scenarios 5-7, grower prices in scenarios 8-10 illustrate higher
than the baseline. This is because trade expansion stimulates exports dominate the SPS
cost reduce exports. Rise in export flows drive up price.
Different from scenarios 2-7, imports for fresh and processed pears increase over time
but they are above the baseline (Recall that in scenarios 2-4 import fluctuates above and
below the baseline and in scenarios 5-7 import below the baseline). Exports for fresh and
processed pears have a downward trend and are higher compared to scenarios 5- 7.
Increases in exports of both fresh and processed pears arise because the effect from trade
expansion and meanwhile increase imports.
24
Domestic consumer losses associated with trade expansion. The third graph in figure 2
shows the change in consumer surplus for fresh pears. Consumers are better off right
after the imposed SPS interventions; however, the impact from the SPS is short run. The
domestic consumers are worse off as prices are higher than baseline as a result of trade
expansion. Change in consumer surplus for processed pears has the same pattern of fresh
pears. Consumer losses are larger with higher level trade expansion. Change in domestic
consumer surplus is negative as grower prices below the baseline.
Producer and sectors benefit associated with the trade expansion. The third graph in
figure 3 and 4 show the change in quasi-return to capital and management to producer
and packers. Producers, packers, and processors are better off as prices are below the
baseline as a result of trade expansion. Higher level trade expansion results in larger
gains along the supply chain from growers to sectors.
Welfare Analysis
Table 3 reports the change in welfare for the nine scenarios compared to the baseline.
The reported values are cumulative net present value in 2014 (in 2010 dollar). The
change in net surplus (consumer, producer, and sector) is negative, indicating the United
States is worse off when there are fire blight outbreaks (Scenario 2-4). We notice that the
change in total surplus loss approximately between $363.48 million to $975.72 million.
The change in total surplus increases with higher level of outbreak. This is because
higher fire blight outbreak causes long run production fluctuation and price deviate
farther from baseline. Also, processed pear market accounts for major total surplus loss.
This is because pears go to fresh market with higher price and remaining go to processed
market. Severe outbreak decrease total production and pears go to processed market
25
decrease even more drive up processed pears price along with fewer processed pears
available with higher outbreak together lead to increasing total surplus loss in processed
market. Change in quasi-return to capital and management transition from positive to
negative value are observed when there are high level outbreak for fresh pears and
medium level outbreak for processed pears. Low outbreak (Scenario 2) has the positive
magnitude of change in producer surplus (growers and sectors) with $26.18 million,
followed by the medium outbreak (scenario 3) of $5.49 million, and then the High
outbreak (Scenario 4) with loss of $65.58 million.
Scenarios 5-7 represent hypothetical SPS intervention and trade contraction in 2015
impact on welfare change (Table 3). The first observation is that the change in total
surplus increases with increased trade contraction. This is because higher trade
contraction leads to lower price along with more pears available benefit domestic
consumers. The second observation is that change in quasi-return to capital and
management to growers and sectors are negative indicating that supply chain from
growers to packers/processors suffer losses from trade contraction.
Scenarios 8-10 represent hypothetical SPS intervention and trade expansion in 2015
impact on welfare change (Table 3). Change in total surplus is negative and increases
with increased trade expansion. This is because higher trade expansion leads to drive up
demand for US pears and hence price. Meanwhile, change in quasi-return to capital and
management to growers and sectors are positive indicating that supply chain from
growers to packers/processors suffers benefit from trade expansion.
The patterns of change in consumer surplus and producer surplus differ under cases
with supply shocks and trade shocks. For each set of shocks, consumer surplus loss
26
increases as fire blight outbreak is worse. Consumer surplus loss (gain) increases as trade
expansion (contraction) level is higher. However, producer surplus (growers and sectors)
differs in different set of shocks. Under supply shocks (Scenario 2-4), producer transits
from gains to losses with increase fire blight outbreak level and processing sector seems
bear more burden than fresh sector. This is consistent with historical event that the
processing pear industry in Ontario never is able to recover from outbreak. Under trade
shocks (scenario5-10), producer loss increase with higher trade contraction level, but
producer gain and the gains increase with higher trade expansion level.
Discussion
To summarize the effects of the pest and disease outbreak over the 60-year simulation,
we report average effects over 60 years for quantity variables and for those variables with
dollar value unit we report cumulative net present value in 2010 of the effects after
hypothetical outbreak in 2015. The values at the end of 60 year compared to baseline
show us how the pear industry performance in order to recover from supply shock and
trade shock in the long run (Table 4). Through comparing the simulation results with
baseline, welfare changes associated with fire blight outbreak shocks are quantified
(Table 3).
Compared to the baseline, fire blight outbreaks in scenarios 2-4 decrease the average
number of bearing acreage and production over the 60 years. These decreases in
production are associated generally with decrease in domestic consumption and decreases
in exports and imports for both fresh and processed pears. The more severe the fire blight
outbreak, the higher the impacts are on production, consumption, export, and import. For
example, Scenario 4 has the largest impact from the fire blight outbreak, followed by
27
Scenario 3, and then Scenario 2. Those mean values illustrate average effects and they
average out the negative impacts in some years and positive impacts in others over 60
years. For instance, average exports of fresh pears in scenario 2 are lower than those in
baseline over time, while exports in scenario 2 is more than those in baseline in 2062. In
2062, the bearing acreages are higher than those in baseline. The increase in production
put downward pressure on grower price and therefore exports are larger than baseline.
Trade sanction through SPS measure and contraction in trade with scenarios 5-7
decrease the bearing acreages and production over the 60 years compared to baseline, but
the magnitude is much smaller than supply shock. These impacts are related with a
decrease in exports, imports, and an increase in domestic processed and fresh pear
consumption. Given the small decrease in total production, decrease in exports outweighs
the decrease in imports result in an increase in consumption for both fresh and processed
pears.
Trade expansion and trade sanction together in scenarios 8-10 increase the bearing
acreages and production over the 60 years compared to baseline, but the change is
relatively small. These impacts are related with an increase in exports, imports, and a
decrease in domestic processed and fresh pear consumption. Given the small increase in
total production, increase in exports outweighs the decrease in imports result in a
decrease in consumption for fresh pears. Even if increase in imports outweighs the
decrease in exports for processed pears, the net import of processed pears contributes
small part for total consumption. Trade expansion put upward pressure on prices and thus
fewer pears go to processing sector result in total consumption reduction in processed
pears.
28
We find that pear industry would suffer more losses with trade sanction than without
trade sanction imposed (scenarios 2-4) given a fire blight outbreak level. These impacts
are associated with higher change in production, trade flows, and consumption for both
fresh and processed pears. We find change in production with supply shock and trade
shock is higher than scenarios 2-4. Grower prices from supply and trade sanction shocks
have similar patterns in the first decade with scenarios 2-4 and then exhibit below the
baseline. The larger the shocks, the farther imports and exports deviate from the baseline.
This is in part because trade sanctions reduce exports in the beginning. Declines in export
flows are rerouted to domestic supply driving down price. Higher bearing acreages
changes lead to higher output changes, which results in lower grower price. The lower
price causes more pears exported and fewer pears imported. The increase in exports
drives down the total consumption of fresh and processed pears. At the same outbreak
level, producers (growers and sectors) suffer more loss with trade sanction than without
trade sanction. We estimate that producer losses $572 million more under each outbreak
level with trade sanction than sceanrios2-4. In 2062, although bearing acreages and
production are lower than the baseline, impacts of trade sanctions imposed by importing
countries affects the price in the long run such that grower price is still below baseline.
However, consumer transits from loss to benefit if trade sanction were impose after an
outbreak. This indicates that the pear industry bears all of surplus loss. It could impact the
productivity and competiveness of the pear industry in the long run.
The simulated outcomes from different scenarios imply that it takes several decades
for the pear industry to track back to baseline equilibrium when there is pest or disease
outbreak (Figure 3). And it may take even longer for the pear industry to adjust when
29
there are shocks from international market. The time to return to equilibrium could be
adjusted by government intervention or industry innovations to recover to original
production level. For example, the government could subsidize growers to recover the
impacted acreage or the industry improves technology to increase the yield per acre.
We find that total surplus could be two to four times less if there is effective control
after fire blight outbreak. We draw this conclusion by considering pear industry have a
hypothetical fire blight outbreak in 2015 by decline the yield instead of decreasing
equivalent bearing acreages and it takes 3 years for the pear industry to recover to
baseline scenario. For example, supply shock (scenario 2-4) would cause $59.54 million
to $290 million total surplus loss when the hypothetical fire blight outbreak occurs in
2015 and it takes 3 years to recover. Fire blight outbreak with trade sanction imposed
would even more burden in pear industry. Moreover, efficient control of fire blight
outbreak leads to less fluctuation in production, trade flow, and consumption.
6. Conclusions
This article presents an intertemporal economic model to estimate economic impacts of a
single tree fruit pest or disease outbreak. The model is designed to examine the economic
consequences of pest and disease outbreak on demand and supply with international trade.
Equilibrium displacement model is adopted to conduct a dynamic simulation of the single
tree fruit industry under scenarios with and without outbreak (baseline scenario). The
framework can be applied to other tree fruit with recalibration of the model. We report
the comparison results of the stream of simulated outcomes and the consequences for
measures of economic welfare of producers and consumers.
30
Our article bridges an important gap in the literature in two ways. We use econometric
method to estimate dynamic supply response for perennial crops to reflect the production
decision response to updated price from solving the partial equilibrium model. This
method overcomes the lack of price information for updating production decisions of
perennial crops. Second, the model embodies vertical supply chains so that associate
shocks at each market level are considered. Not only production, value added, trade flows,
and consumption are estimated in the model, welfare changes for producers (growers and
sectors) and consumers are quantified with associated shocks.
We apply the model to a hypothetical fire blight outbreak in the U.S. pear industry.
The model considers three effects from the hypothetical fire blight outbreak in 2015 for
the pear industry and calibrated the results from 2002 to 2062. The three effects include
supply shock, trade sanction, and trade expansion with 9 scenarios. Higher level outbreak
causes dramatically variation in pear production, pear price, trade flows, and welfare
changes. It has the highest impact for the years after the outbreak and the impact
decreases with time passes by. The total surplus loss increases with increases of outbreak
level. At the same outbreak level, scenario with trade sanction rise higher change in
production, trade flows, and consumption for both fresh and processed pears. With severe
supply shocks and multiple shocks, it takes longer for the pear industry to recover from
fire blight outbreak. Under scenarios of trade contraction or expansion, the change in the
total surplus has much smaller magnitude than the cases with fire blight outbreak.
The results show that a hypothetical fire blight outbreak causes total surplus loss or
gain but pear industry suffer more loss when trade shocks are imposed. Supply shock
result in total surplus loss among $134.45 million and $975.72 million (scenarios 2- 4).
31
Consumers bear loss when there are only supply shocks while growers and sectors transit
from gain to bear loss with higher outbreak level. Meanwhile, consumers for processed
pears bear most of the consumer surplus loss and processed pear industry from grower to
processor bears most of the loss with severe fire blight outbreak. Supply shock with trade
sanction results in total surplus gain. But pear industry along supply chain bears
approximately $550 million to $634 million loss.
Unlike supply shocks have a long run impact in pear industry; shocks from
international market have short run impact and have relative smaller quantitative impact
on the welfare change. Consumers gain when there is trade contraction and SPS measure,
while producers bear loss (scenarios 5- 7). Consumers benefit from $194.29 million to
$562.84 million, while producers (growers and sectors) bear loss from $48.43 million to
$135.19 million. Trade expansion and SPS measure has opposite impact where
consumers bear loss while producers gain (scenarios 8- 10). Producers (growers and
sectors) gain from $37.96 million to $123.99 million, while consumers bear loss from
$172.41 million to $537.23 million.
The results imply that multiple shocks increase the pear industry recovery time and
impose more burdens on pear industry. Even if the total surplus for the outbreak is
smaller under scenarios of outbreak and trade sanction (scenarios 5-8) than under
scenarios without trade sanction (scenarios 2-4), pear industry suffers loss 2 to 5 times
more with multiple shocks. Due to perennial property, it would take about 20 years for
the pear industry to recover from the impact of the fire blight outbreak. The shocks from
international market would make the pear industry to take another 5 years to recover.
32
We also find that efficient control of fire blight outbreak would shorten recovery time
and also decrease the total surplus loss by two to four times. If resources are put into
control the fire blight outbreak and the effective control would leads to the pear industry
to recover to the normal level within three to five years. Meanwhile, the total surplus
(consumer and producer surplus) decrease 2 to 4 times compared to those scenarios
examined accordingly.
33
References
Global Agricultural Trade System (GATS), Foreign Agricultural Service,United States
Department of Agriculture. 2012, from http://www.fas.usda.gov/gats/
Economic Research Service, United States Department of Agriculture. "Food Availability
Data System." 2012, from http://www.ers.usda.gov/data-products/food-
consumption-and-nutrient-intakes.aspx.
Bureau of Labor Statistics, United States Department of Labor. "Inflation & Prices."
2012, from http://data.bls.gov/pdq/querytool.jsp?survey=cu.
Akiyama and Trivedi 1987. "Vintage production approach to perennial crop supply: An
application to tea in major producing countries." Journal of Econometrics 36(1–2):
133-161.
Arnade and Pick 2000. "Seasonal oligopoly power: the case of the US fresh fruit market."
Applied Economics 32(8): 969-977.
Bonn and van der Zwet 2000. Distribution and Economic Importance of Fire Blight. Fire
Blight: The Disease and its Causative Agent, Erwinia amylovora. J. L. Vanneste.
Wallingford, UK, CABI: 37-49.
Calvin and Krissoff 1998. "Technical Barriers To Trade: A Case Study Of Phytosanitary
Barriers And U.S. - Japanese Apple Trade." Journal of Agricultural and Resource
Economics 23(02): 351-366.
Calvin, Krissoff and Foster 2008. "Measuring the Costs and Trade Effects of
Phytosanitary Protocols: A U.S.–Japanese Apple Example." Review of
Agricultural Economics 30(1): 120-135.
Calvin, Krissoff and Foster 2008. "Measuring the Costs and Trade Effects of
Phytosanitary Protocols: A U.S.–Japanese Apple Example." Applied Economic
Perspectives and Policy 30(1): 120-135.
Carman, Klonsky, Beaujard, et al., Eds. 2004. Marketing Order Impact on the Organic
Sector: Almonds, Kiwifruit and Winter Pears. Giannini Foundation Research
Report, no.346.
34
Cembali, Folwell, Wandschneider, et al. 2003. "Economic implications of a virus
prevention program in deciduous tree fruits in the US." Crop Protection 22(10):
1149-1156.
Cook 2002. "Update on the U.S. Pear Industry." Department of Agricultural and
Resource Economics, UC Davis.
Disdier, Fontagné and Mimouni 2008. "The Impact of Regulations on Agricultural Trade:
Evidence from the SPS and TBT Agreements." American Journal of Agricultural
Economics 90(2): 336-350.
Drogué and DeMaria 2012. "Pesticide residues and trade, the apple of discord?" Food
Policy 37(6): 641-649.
Dunley, Kupferman and Willett 2001. Pear Pest Management in the Pacific Northwest.
WSU Tree Fruit Reseach & Extension Center Postharvest Information Network.
Ellis 2008. Fire Blight of Apples and Pears. FACT SHEET Agriculture and Natural
Resources, The Ohio State University Extension.
Elnagheeb and Florkowski 1993. "Modeling Perennial Crop Supply: An Illustration from
the Pecan Industry." Journal of Agricultural and Applied Economics 25(01): 187-
196.
French, Gordon and Minami 1985. "Planting and Removal Relationships for Perennial
Crops: An Application to Cling Peaches." American Journal of Agricultural
Economics 67(2): 215-223.
French and Matthews 1971. "A Supply Response Model for Perennial Crops." American
Journal of Agricultural Economics 53(3): 478-490.
Gao and O'Rourke 1992. "Demand Analysis of the Pacific Coast Pear Industry."
Agribusiness 8(4): 323-333.
Group 2005. Fire Blight of Apple and Pear in Canada: Economic Importance and a
Strategy for Sustainable Management of the Disease, Canadian Horticultural
Concil's Apple Working Group.
35
Labys 1973. Dynamic commodity models: specification, estimation, and simulation.
Lexington, Massachusettes, Lexington Books.
Marsh, Huffaker and Long 2000. "Optimal Control of Vector-Virus-Plant Interactions:
The Case of Potato Leafroll Virus Net Necrosis." American Journal of
Agricultural Economics 82(3): 556-569.
Newhouse and Hamilton 2010. The Washington State Report on Foreign Trade Barriers
to Food and Agricultural Exports. W. S. D. o. Agriculture. Olympia.
Paarlberg, Lee and Seitzinger 2003. "Measuring Welfare Effects of an FMD Outbreak in
the United States." Journal of Agricultural and Applied Economics 35: 53-65.
Paarlberg, Seitzinger and Lee 2007. "Economic Impacts of Regionalization of a Highly
Pathogenic Avian Influenza Outbreak in the United States." Journal of
Agricultural and Applied Economics 39: 325-333.
Paarlberg, Seitzinger, Lee, et al. 2008. "Economic Impacts of Foreign Animal Disease."
ERR-57. U.S. Dept. of Agriculture, Econ. Res. Serv.
Peterson, Grant, Roberts, et al. 2013. "Evaluating the Trade Restrictiveness of
Phytosanitary Measures on U.S. Fresh Fruit and Vegetable Imports." American
Journal of Agricultural Economics.
Petry, Paarlberg and Lee 1999. "PRRS and the North American Swine Trade: A Trade
Barrier Analysis." Journal of Agricultural and Applied Economics 31: 425-436.
Shogren 2000. Risk Reduction Strategies against the 'Expolosive Invader'. The
Economics of Biological Invasions. C. Perrings, M. Williamson and S.
Dalmazzone. Cheltenham, UK, Edward Elgar Publishing, Inc.
United States Department of Agriculture. from http://www.ams.usda.gov/AMSv1.0/.
Wann and Sexton 1992. "Imperfect Competition in Multiproduct Food Industries with
Application to Pear Processing." American Journal of Agricultural Economics
74(4): 980-990.
Warner 2012. "Canning pear price set for Pacific Northwest." Good Fruit Grower(July).
36
Winfree, McCluskey, Mittelhammer, et al. 2004. "Seasonal Oligopoly Power in the
D'Anjou Pear Industry." Journal of Food Distribution Research 35(02): 56-65.
Yue and Beghin 2009. "Tariff Equivalent and Forgone Trade Effects of Prohibitive
Technical Barriers to Trade." American Journal of Agricultural Economics 91(4):
930-941.
Yue, Beghin and Jensen 2006. "Tariff Equivalent of Technical Barriers to Trade with
Imperfect Substitution and Trade Costs." American Journal of Agricultural
Economics 88(4): 947-960.
Zhang, Marsh and Schotzko 2007. "Evaluation of the Effectiveness of Pear Advertising
and Promotion: Final Report."
Zhao, Wahl and Marsh 2007. "Economic Effects of Mitigating Apple Maggot Spread."
Canadian Journal of Agricultural Economics 55(4): 499-514.
37
Figure 1 Economic Impact of Fire Blight Outbreak on Grower Prices, 2002-2062
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051 2055 2059
$/p
ou
nd
baseline Low Impact Medium Impact High Impact
38
Figure 2 Economic impacts of fire blight outbreak and trade on change in consumer
surplus for fresh pear, 2002-2062
39
Figure 3 Economic impacts of fire blight outbreak and trade on change in quasi-return to
capital and management to producers for fresh pears, 2002-2062
40
Figure 4 Economic impacts of fire blight outbreak and trade on change in quasi-return to
capital and management to packers for fresh pears, 2002-2062
41
Table 1 Parameter Description and Values
parameter Description Value F
f Fresh pear farm price elasticity 1c
f Fresh pear retail price elasticity -1.104a
p Processed pear retail price elasticity -2 b
Cross price elasticity for fresh pear 0c
Cross price elasticity for processed pear 0c
md
f Fresh pear import elasticity 1c
md
p Processed pear import elasticity 1c
es
f Fresh pear export elasticity -1.001c
es
p Processed pear export elasticity -1.001c
Elasticty of total quantity increase in supply to fresh market for a rise in total production 0.6 d
US population growth rate 0.09d
g Yield per acre growth rate 0.01d
Utilized production adjustment value 0.98d
Rate of returns to capital and management for pear grower 0.15e
Rate of returns to capital and management for pear packer 0.08f
Rate of returns to capital and management for pear processor 0.22g
Sources: a Price and Mittelhammer, 1979;
b Andersen and Sexton, 2001;
c assumed values;
d estimated values;
e authors estimated from Galinato and Gallardo, 2010;
f authors estimated from Galinato and Gallardo, 2012;
g?
42
Table 2 Hypothetical Fire Blight Outbreak Scenarios in U.S. Pear Industry, 2015
Bearing Acreage
Change Export Shocks Export Shocks
Scenario SPS
Intervention
Trade
Contraction
SPS
Intervention
Trade
Expansion
(2) -2000
(3) -4000
(4) -6000
(5)
0.03 -0.03
(6)
0.03 -0.06
(7)
0.03 -0.09
(8)
0.03 0.03
(9)
0.03 0.06
(10)
0.03 0.09
43
Table 3 Welfare Analysis for Each Scenario, NPV in 2014. Unit: $ Million
Change in Consumer Surplus Change in Quasi-return to growers Change in Quasi-return to Sectors Net Surplus
Scenario Fresh Processed Total Fresh Processed Total Fresh Processed Total
(2) -90.14 -299.52 -389.66 8.28 1.62 9.90 6.66 9.62 16.28 -363.48
(3) -115.97 -589.67 -705.65 8.57 -8.29 0.28 7.45 -2.23 5.21 -700.16
(4) -90.59 -819.55 -910.14 -2.39 -28.81 -31.20 1.15 -35.54 - 34.38 -975.72
(5) 120.01 74.28 194.29 -18.50 -5.39 -23.89 -11.52 -13.02 - 24.54 145.86
(6) 233.39 144.96 378.35 -35.68 -9.84 -45.52 -22.19 -24.05 - 46.25 286.58
(7) 347.15 215.68 562.84 -52.86 -14.34 -67.19 -32.86 -35.14 - 68.00 427.64
(8) -105.75 -66.66 -172.41 15.87 3.38 19.25 9.87 8.84 18.71 -134.45
(9) -218.08 -136.99 -355.07 33.06 7.70 40.76 20.58 19.68 40.26 -274.05
(10) -330.09 -207.15 -537.23 50.25 11.98 62.23 31.30 30.46 61.76 -413.24
44
Table 4 Simulation Results for Fire Blight Outbreak Scenarios, 2002-2062
Baseline 2 3 4 5 6 7 8 9 10
Induced Changes in Average of Annual Values, 2003-2062
Bearing Acreages (acres) 44868 -143.33 -295.71 -458.00 -6.01 -11.76 -17.57 5.30 10.87 16.38
Production (million lbs) 1909 -5.12 -10.66 -16.66 -0.24 -0.47 -0.70 0.21 0.43 0.65
U.S. Consumption -Fresh pears (million lbs) 1080 6.88 16.44 26.60 1.46 3.05 4.63 -1.75 -3.38 -5.01
U.S. Consumption -Processed pears (million lbs) 847 -10.19 -23.82 -38.40 0.87 1.84 2.80 -1.11 -2.11 -3.13
Exports-Fresh pears (million lbs) 250 -1.44 -3.61 -6.93 -3.65 -7.70 -11.73 4.49 8.59 12.70
Exports-Processed Pears (million lbs) 9 -0.18 -0.43 -0.73 -0.39 -0.58 -0.76 -0.03 0.15 0.34
Imports -Fresh Pears (million lbs) 235 -0.52 -2.07 -4.40 -1.26 -2.47 -3.69 1.17 2.38 3.59
Imports -Processed Pears (million lbs) 43 0.72 1.32 1.61 -0.22 -0.44 -0.66 0.22 0.44 0.66
Induced Changes in Final Values, 2062
Bearing Acreages (acres) 31164 31.90 50.90 56.77 -2.64 -3.83 -5.21 -0.83 -0.20 0.25
Production (million lbs) 1824 1.87 2.98 3.32 -0.15 -0.22 -0.30 -0.05 -0.01 0.01
U.S. Consumption -Fresh pears (million lbs) 1257 11.55 27.35 44.15 0.81 1.78 2.75 -1.23 -2.28 -3.37
U.S. Consumption -Processed pears (million lbs) 900 -11.80 -29.59 -49.74 0.42 1.11 1.76 -1.03 -1.80 -2.58
Exports-Fresh pears (million lbs) 118 0.43 -0.04 -1.74 -2.67 -5.84 -8.97 3.76 7.03 10.33
Exports-Processed Pears (million lbs) 5 -0.10 -0.28 -0.51 -0.29 -0.44 -0.59 0.00 0.16 0.31
Imports -Fresh Pears (million lbs) 379 -2.88 -7.59 -13.60 -1.32 -2.61 -3.92 1.25 2.53 3.80
Imports -Processed Pears (million lbs) 79 1.09 2.05 2.44 -0.26 -0.55 -0.82 0.30 0.59 0.87
45
Appendix A: Econometric Model for the Primary Farm Level Supply
We use a reduced form of change in bearing acreage to estimate the supply response for
pear production, with an econometric model for change in bearing acreage:
0 1 1 2 3 2 4 2 5
e
t t t t tB B p B Y t u
Where 1t t tB B B , which is the change of pear-bearing acreage from year 1t to year
t ;
2 2
1
0 0
3 3
t i t ie i i
p p
p
, which is the change of a three-year moving average of real
producer pear price;
5
2
02
5
t i
it
B
B
,which is average bearing acreage during the
previous five years at year 2t ;
5
2
02
5
t i
it
y
Y
, which is average yield per acre during
the previous five years at year 2t , t is time trend starting from 1 at year 1920. tu is the
error term.
Table B.1 Parameter Estimates for Supply Response Model
Variable Parameter Estimate Standard Error t Value Pr > |t|
Intercept 14937 2429.63372 6.15 <.0001
1tB 0.32289 0.08853 3.65 0.0005
ep 6.38583 3.12902 2.04 0.0445
2tB -0.15549 0.02494 -6.23 <.0001
2tY -243.322 117.09314 -2.08 0.0409
t -28.5886 16.69686 -1.71 0.0907
46
The estimation result shows that the adjusted R-square is 72.22%, which indicates that
the model explains 72.22% of total variation. The DW statistic is 2.062 (approximately 2),
implying that there is no autocorrelation in error terms.
Change in bearing acreage at year t explains the summation of new plantings at year
2t , and removals at year t . Average yield per acre during the previous five years at
year 2t has a statistically significant negative effect on the change in bearing acreage.
A stable increase in yield per acre reduces growers’ incentive to expand orchards or it
accelerates the growers’ incentive to remove old trees and replant. Average bearing
acreage during the previous five years at year 2t (2tB ) also has a statistically
significant impact on the change in bearing acreage. It is a good indicator for old trees
that need to be removed in the next few years. If the proportion of old trees is higher, then
the change in bearing acreage would decrease, caused by removal. Change in bearing
acreage at a previous period has positive effect on the change in bearing at the present
period.
A change in the three-year moving average of real producer pear price (ep ) is
statistically significant and positively related to the change in bearing acreage. This can
be explained as the growers’ expectation for the future pear market. If growers expect the
price to go up, then they would expect more output through additional plantings and
delayed removals.
A time trend as a proxy for orchards’ operation and management culture has a negative
effect on bearing acreage. The removal of old trees may be postponed for better operation
and management skills, therefore decreasing the change in bearing acreage.
47
The degree of the damage caused by pest and disease will depend on different factors,
such as varieties and the orchard’s pest and disease control ability. A hypothetical
outbreak of pest or disease in the fruit industry is used to illustrate the economic impact
of these outcomes. Different scenarios of potential economic losses due to pest or disease
outbreaks can be applied to test the impact. The results allow us to estimate the effects of
pest and disease shocks on bearing acreages along with yield, allowing us to predict total
production.
48
Appendix B: Differential Transformation of the Conceptual Model
A numerical solution of the partial equilibrium model is facilitated by a total logarithmic
differential version of the equations presented in the preceding part. The logarithmic
differential version is advantagous because the differential version is driven by
elasticities, which are easier to obtain than specific functional forms. The logarithmic
differential version can also be applied to observed historical data and base data can be
updated as new values are available.
The farm-level demand for fresh fruit is given by equation (6). Total logarithmic
differentiate equation (6) gives:
,
,
, , ,ln ln ln
f r
f rF F
f r f r f r
FP
FDd FD d p d FS
FSFS
(18)
where ,
,
,
ln
ln
f rF
f r F
f r
d FD
d p is the own-price elasticity of farm-level demand for fresh fruit,
,
,
f r
f r
FP
FD
FSFS
is to measure the proportion of the total production change that will cause a
proportional change in allocation to the fresh market.
Using a total logarithmic differentiatial equation , ,p r f rFD FS FD (7), we get
,
, ,
, ,
ln ln lnf r
p r f r
p r p r
FDFSd FD d FS d FD
FD FD (19)
Total logarithmic differentiation of market margin equations (15) (16) gives:
49
, ,
, ,ln ln ln
F F
i r i rW F F
i i r i rW W
i i
p MMd p d p d MM
p p (20)
,
, ,
, ,
ln ln ln
RWi rR W Ri
i r i i rR R
i r i r
MMpd p d p d MM
p p
(21)
The final demand in conceptual functional form is given by equation (10), which is
per-capita consumption of fruit (equation (8)) multiplied by population. Logarithmically
differentiating equation (10) regional consumption gives :
, , , , ,
, , , ,
ln ln ln ln
ln ln ln
D
i r r f r f r p r p r
o r o r I r r i r
d Q d pop d p d p
d p d I d
(22)
where ,
,
,
ln
ln
D
i r
f r
f r
d Q
d p is the elasticity of retail demand for fruit i with respect to retail
fresh fruit price, ,
,
,
ln
ln
D
i r
p r
p r
d Q
d p is the elasticity of retail demand for fruit i with respect to
retail processed fruit price, ,
,
,
ln
ln
D
i r
o r
o r
d Q
d p is the elasticity of retail demand for fruit i with
respect to retail other fruit price,,
,
ln
ln
D
i r
I r
r
d Q
d I is the income elasticity.
National demand in logarithmic differentiatial form is given as:
, , , , ,
, , , ,1
ln ln lnln
ln ln ln
DRi r r f r f r p r p r
io r o r I r r i rr i
Q d pop d p d pd QD
d p d I dQD
(23)
Taking total logarithmic differentiation of the exported fruit equation (11) gives:
1
ln ln lnEx W SPS W W SPS
i i i i i i i id E p c p d p dc d e
(24)
50
Fruit import demand is given by equation (12). Taking logarithmic differentiation of
equation (12), we get
1
Imln ln lnW W
i i i i i i i id M p tm p d p dtm d m
(25)
wheremd
i is the own-price elasticity of imported fruit i.
The conceptual retail-level supply functional form is given in equation (13). Total
logarithmic differentiation of the function and substituting equation (24), (25) into it
gives equation (26):
1, , Im
,
1,1
ln ln ln
ln
ln ln
i r i r W W
i r i i i i i i iR
i ii
i r Ex W SPS W W SPSr
i i i i i i i
i
FS Md FS p tm p d p dtm d m
QS QSd QS
Ep c p d p dc d e
QS
(26)
In empirical analysis, the total logarithmic differential equation is given by equation
(27)
1, Im
,
1
1
ln ln ln ln
ln ln
Ri r W Wi
i i r i i i i i i i
r i i
Ex W SPS W W SPSii i i i i i i
i
FS Md QS d FS p tm p d p dtm d m
QS QS
Ep c p d p dc d e
QS
(27)
Retail-level market clearing conditions are given by equation (17). Combining
equation (24) and (27), we get the total logarithmic differentiation form:
, , , , ,
, , , ,1
1, Im
,
1
1
ln ln ln
ln ln ln
ln [ ln ln ]
ln ln
DRi r r f r f r p r p r
o r o r I r r i rr iR
i r W W Wii r i i i i
r i i
Ex W SPS W Wii i i i i
i
Q d pop d p d p
d p d I dQD
FS Md FS p tx p d p dtx d m
QS QS
Ep c p d p dte d e
QS
(28)
51
We can solve the single fruit equilibrium to derive retail-level fresh and process price
based on the retail-level market clearing conditions.
