The 'Paradox of Thrips': Identifying a Critical Level of Investment in Pest Exclusion Activities in Western Australia

 

---

 

1. Abstract

With increasing efficiency in human and freight transport fuelled by the creation of the global market place, pressure is mounting on quarantine administrators to target their resources strategically.  A managed approach to decision-making is therefore becoming an integral part of quarantine management since target species and/or entry pathways must be identified and policed effectively.  Using the example of Melon Thrips in Western Australia, this paper presents an economic framework that allows decision-makers to prioritise exotic pests based on the damage and production cost increases they are capable of imposing on affected industries.  In doing so it identifies a critical level of expected damage associated with the pest that can then be used as a ceiling for incursion response expenditure.

Key words: quarantine, invertebrate pest, Western Australia.

 

2. Introduction

As advancement in human transport and freight technology continues to reduce travel time between trading centres and enable increasing volumes of goods to be moved around the globe, biosecurity is fast becoming a major concern for signatory nations of the World Trade Organisation (WTO) Agreement[1].  The establishment of new and lucrative trade routes provides a myriad of biological organisms the opportunity to colonise areas of the world previously impossible to reach unaided.  The measures available to most trading nations to prevent them from doing are limited in the sense that they must be seen to satisfy the Agreement on Sanitary and Phytosanitary Measures (SPS Agreement).  This situation forces WTO Member governments to play a dangerous game balancing the gains from trade on one hand, and the damage potentially caused by invasive organisms on the other.  The stakes in this game are high.  One wrong decision could lead to a harmful pest outbreak affecting the livelihoods of many agricultural producers, their communities and environment.  One right decision could secure a cheaper, wider variety of agricultural produce for all members of society whilst minimising costly retaliatory actions by trading partners in response to excessive quarantine requirements.

In this sort of environment a system of targeting quarantine effort towards those pests, diseases and entry pathways capable of producing the greatest amount of damage to an economy forms an important tool for policy-makers.  A failure to do so may lead to the paradoxical situation where a healthy desire to rid a region of a damaging pest may in fact cause that region’s economy more harm than the pests itself.  If advanced warning of pest threats can be provided along with an indication of their potential means of entry and the damage to be expected from them, it may be possible to tailor a quarantine system to minimise losses from exotic pest outbreaks cost-effectively.  At the same time, the benefits from trade must be taken into account if quarantine measures are to reflect social preferences with regard to consumption and international trade risk.

With this in mind, this paper puts forward a method of estimating the potential economic losses from an invertebrate plant pest in Western Australia (WA), Melon Thrips (Thrips palmi).  In recognition of the fact that the notion of ‘zero risk’ is fictitious, it is assumed that the probability the entry and establishment of this pest in susceptible crops is positive both with a concerted quarantine effort and without it.  By calculating the difference in the damage to be expected with and without quarantine targeting over a twenty-year period, the model presented enables a critical level of expected damage to be identified for use in determining an appropriate policy response in the event of an outbreak[2].  If the costs of eradicating an outbreak are believed to exceed this critical level, then other options such as containment or living with the pest should be considered.  If the costs of eradication are below the critical level, then a rapid and purposeful campaign should be mounted against the pest without delay to maximise the chances of removal.

 

3. Background

3.1 Melon Thrips and Western Australian agriculture

Melon Thrips (T. palmi) is a small, polyphagous sucking insect that inhibits host plant development by depriving it of nutrients.  It is thought to have originated in Malaysia and western Indonesia, but in the past twenty years T. palmi has spread throughout many tropical areas of the world.  It is now present in south east Asia, Japan, Papua New Guinea and other Pacific Islands, North America, the Caribbean islands, South America and parts of Europe.  The insect is a prolific breeder, and both larvae and adults feed by extracting cell contents from the leaves, stems, flowers and the surface of fruits with mouth parts specifically adapted for sucking (Lewis, 1973).

Until recently T. palmi was only found in Australia in the Northern Territory and, to a lesser extent, northern Queensland (Young and Zhang, 1998; CABI, 1999).  In WA T. palmi remains classified as a pest of quarantine significance, and specific quarantine protocols apply to most fruits, vegetables, plants and flowers crossing the State border in addition to those required for other pests (WAQIS, 1999)[3].  However, the label of ‘quarantine pest’ has recently been called into question following the discovery of T. palmi in the Ord River Irrigation Area (ORIA) of the Kimberley region in the north of the State in September 2001.  In May 2002 a subsequent discovery was made some 20 kilometres from the previous site, indicating that a permanent population had established, rendering eradication a costly option of doubtful technical feasibility.

For the purposes of this investigation it is assumed that this outbreak does indeed constitute a permanent population, and can not be eradicated (at least not cost effectively).  However, it is further assumed that a mutually government-industry funded containment campaign can successfully restrict the movement of T. palmi and prevent it from extending its range beyond the ORIA.  The objective here is to establish the critical or break-even annual value of expected costs required to eradicate any T. palmi outbreak outside the ORIA to prevent it from becoming widely established in the State.  Using this value, decisions of what course of action to instigate against an outbreak outside the ORIA can be made with minimal delay.  If, after an initial survey of the infested area it is believed the pest can be eradicated for less than the critical value, a concentrated eradication campaign should be put in place immediately.  If not, an alternative course of action should be considered since eradication is not a reasonable option for an ‘economically-rational’ decision-making body.

If T. palmi were to become widely established in WA beyond the ORIA it is expected to populate most of the State’s fruit and vegetable growing areas north of Perth, possibly extending into the south west.  Industries affected would include cucurbits, cabbage, Chinese cabbage, lettuce, potato, onion, tomato, citrus, mango, avocado, nurseries and cut flowers.  Each of these industries is used to assess the overall impact of T. palmi spread on the WA economy.  Although the insect is capable of inflicting severe crop damage if its numbers are not adequately controlled, growers of susceptible crops in the Northern Territory and Queensland have successfully managed T. palmi using a variety of methods.  These range from the use of potassium soap sprays and plastic mulch to weed control, crop rotations and wind breaks (Planck, 2001).  So, an increase in the average total cost of production for each crop modelled is expected if the insect becomes established beyond the ORIA in WA.

 

3.2 The theoretical model

The quarantine-significance of T. palmi can be determined by measuring the welfare effects of its introduction on WA producers of host crops[4].  For this, a static, partial equilibrium model is appropriate if the following assumptions are made:

(i) The domestic market for the good is perfectly competitive;

(ii) The domestic price for the product is above the ‘landed’ price of imported product;

(iii) The contribution of WA to the total supply of the good is insufficient to exert influence on the world price, exchange rate or domestic markets for other goods;

(iv) Society has a neutral attitude to risk;

(v) The costs of any quarantine procedures are borne by the exporter and transferred to consumers via the price mechanism (James and Anderson, 1998).

Although T. palmi is not host specific, suppose initially only one commodity is affected.  If so, figure 1 can be used to illustrate the economic consequences of an incursion where the frame on the left represents the ‘on-farm’ impact, and frame on the right the aggregated industry impact.

Profit maximising growers in perfectly competitive markets for host plants will choose to produce a level of output corresponding to the point where price (p) equals the Marginal Cost (MC) of production.  At this point, the differential between total cost and total revenue is maximised.  Assuming the market in which growers operate receives quarantine protection in the form of costly chemical treatments and sampling requirements for imported products, the prevailing domestic market price will be below a closed market equilibrium price, and above a free trade level.  If this were the case, a grower facing a price p_q would choose to produce quantity q₀ and earn a profit of ABCp_q.  It should be noted that output will be positive so long as the price received by the producer remains above the minimum value of the Average Total Cost (ATC) of production.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1: The economic impact of a Thrips palmi incursion

 

If the quarantine protection were to be removed from the market so that produce can move into WA without the need to treat for T. palmi, the price faced by domestic growers would be lowered to p_q*.  This will be greater than a free-trade price as long as quarantine requirements for other pests remain in place.  This being the case, a grower would maximise profits by producing the quantity q₁, where profit is given by the area GHI p_q*.

If all growers in the industry behave in a similar manner, the industry supply schedule produced by the horizontal summation of each producer’s output at different prices would resemble the curve S in the right hand frame of figure 1.  According to the industry demand schedule D_I domestic consumers will demand the quantity Q₁ if quarantine restrictions for T.palmi are in place.  Of this, Q₂ will be supplied by domestic growers, and Q₁ – Q₂ by imports.  In this situation, producer surplus is given by the area ONM, and consumer surplus by WUO.  Under a domestic closed-economy equilibrium scenario (i.e. E_D) producer surplus would be the larger area XE_DM, and consumer surplus the smaller area WE_DX.  Hence, the ‘traditional’ gains from trade resulting from quarantine restricted trade is shown as E_DNU.

The gains would be greater if the quarantine regulations concerning T. palmi were removed, causing the price to fall from p_q to p_q*.  Producer surplus would be reduced to the area STM under the weight of increased volumes of imports (i.e. Q₄ - Q₅), while consumer surplus would increase to the area SVW due to greater volumes of cheaper produce.  Therefore, the traditional gains from trade increase to E_DVT.

The removal of quarantine treatments to minimise the risk of importing T. palmi increases the likelihood of pest entry and establishment through trade.  However, it is important to appreciate that no matter how thorough quarantine treatments, the probability of importing T. palmi with food imports from pest-endemic areas will always be positive.  By making any quantity of imported product available to WA consumers, however small, quarantine authorities are taking a calculated risk that a contaminated batch will not be amongst those imported.  Quarantine requirements merely alter the probability that a contaminated unit or group of units will be present.  If one is present and the pest is able to escape and enter the WA environment in sufficient numbers and in climatic conditions conducive to its survival, an outbreak might occur.

If this series of events were to transpire and T. palmi is fortunate enough to find itself in a position to spread to most fruit and vegetable growing areas as predicted, the effect at the farm level will be rising ATC (and MC).  Although the pest is capable of causing serious damage if left uncontrolled, producers are able to minimise damage by employing additional pest management measures to those already part of standard management practice[5].  It follows that a greater cost is involved in producing each unit of production after the outbreak than before it.  If specific treatments are in place and p_q is the prevailing market price, the increased costs of production would lower grower output from q₀ to q*₀ where producer surplus is DEFp_q.  If the probability of entry and establishment with full quarantine restrictions in place is P_q, then the expected loss of producer surplus at the farm level (ED (P_q)_F) associated with imports can be expressed as[6]:

ED (P_q)_F = P_q × (ABCp_q - DEFp_q)            (1)

Similarly, if the probability of T. palmi entry and establishment in the absence of quarantine treatments is P_q*, the expected loss of producer surplus can be expressed as:

ED (P_q*)_F = P_q* × (GHIp_q* - JKLp_q*)         (2)

At an industry level, the domestic supply curve will contract (from S to S^* in the right frame of figure 1) in the face of added growing costs and yield reductions.  If the industry receives full quarantine protection, domestic producer surplus will decline to the area OUW, representing a loss of MNQP.  So, the expected damage to the collective industry from importing T. palmi-hosts with full quarantine protection (ED_q (Industry)) can be expressed as:

ED_q (Industry) = P_q × MNQP       (3)

Similarly, the expected damage to the industry from allowing imports without quarantine treatments (ED_q* (Industry)) can be written as:

ED_q* (Industry) = P_q* × MTRP     (4)

Using equations (1) and (2) at the farm level and (3) and (4) at the industry level a critical level of expected damage (ED_crit) can be identified which indicates the maximum level of investment appropriate to manage an outbreak of T. palmi.  This is simply the difference in expected damage with and without the pest-specific quarantine regulations (above which costs outweigh on-farm benefits).  So, at the farm level:

ED_crit = [P_q* × (GHIp_q* - JKLp_q*)] - [P_q × (ABCp_q - DEFp_q)] (5)

 

And, at the industry level:

ED_crit = (P_q* × MTRP) - (P_q × MNQP)       (6)

The value representing ED_crit can be used as an important reference point for policy-makers when deciding on an appropriate course of action in response to an outbreak.  For instance, should T.palmi escape from the ORIA and become established in a certain region, a decision on whether or not to embark on a costly eradication campaign becomes straightforward, at least in terms of the static, partial equilibrium trade model.  ED_crit represents the reduction in expected damage by the pest produced by the maintenance of quarantine regulations specific to that pest.  If the likely costs of eradicating an outbreak are below this value, the benefits of eradication will outweigh the costs.  However, if the costs necessary to eradicate the outbreak exceed ED_crit it is more cost-effective to pursue lower cost alternatives such as containment.

Due to a lack of available, sufficiently detailed information concerning the ATC of production under a pest and no-pest scenario, it becomes difficult to use this model in practice.  Of primary concern is the inability to estimate the change in domestic producer surplus induced by the removal of quarantine restrictions.  To overcome this problem it is necessary to assume that producer surplus both in a full and partial quarantine situation (i.e. without T. palmi-specific requirements) remains constant, and to then conduct a sensitivity analysis to discover the likely consequences of this assumption for the model’s results[7].  Assuming a constant domestic producer surplus, equation (6) can be re-written as:

ED_crit = (P_q* - P_q) × MNQP          (7)

If the assumption of T-palmi’s host specificity is now relaxed, the ED_crit for all combined host industries can be estimated by summing the ED_crit calculated for each industry.

 

4. Estimating the on-farm benefits of pest freedom

4.1 Modelling pest spread

Following the successful establishment of T. palmi beyond the ORIA it is expected to spread through susceptible industries utilising both natural and artificial means[8].  Hence, a relevant model of spread must include a random element of sporadic ‘satellite’ spread as well as spread from an initial infestation.         Following the entry and establishment of T. palmi on a given area of susceptible crop its spread to neighbouring host crops is assumed to follow a sigmoid function[9].  The total area of susceptible crops infested at any point in time following an ‘original’ or source outbreak is given by the function:

                  (8)

where;

 

As the area involved in an initial infestation increases, so too does the likelihood of a satellite outbreak some distance from the original site.  Once T. palmi has become established at this new site it is assumed to begin spreading in exactly the same fashion as the original outbreak.  The number of new satellites created in each time interval can therefore be expressed as a function of the total area of range occupied[10].  The model used here follows a logistic law as given by:

 

                  (9)

 

where;

 

The variable m_i is assumed constant, thus discounting the possibility of different establishment rates caused by outbreak size variations, crop susceptibility, and so forth (Moody and Mack, 1988).

4.2 Probabilities of entry and establishment

The probabilities of entry and establishment outside the ORIA under a comprehensive and laps quarantine regime are estimated using the semi-quantitative risk categorisation methodology outlined in AFFA (2001), presented in table 1.

 

 

Table 1: Semi-quantifiable risk categorisation methodology

Likelihood	Descriptive definition	Probability range
High	Very likely to occur	0.7 - 1.0
Moderate	Occurs with even probability	0.3 - 0.7
Low	Unlikely to occur	0.05 - 0.3
Very Low	Very unlikely to occur	0.001 - 0.05
Extremely Low	Extremely unlikely to occur	0.000001 - 0.001
Negligible	Almost certainly will not occur	0 - 0.000001

Source: AFFA (2001).

The probabilities of pest entry and establishment are considered separately.  The presence of quarantine protocols only affects the probability of T. palmi entering WA on contaminated fruit, or by other means.  Given the pest is small and capable of surviving on many plant species the likelihood of entry with appropriate measures in place is considered to be Very Low.  As table 1 indicates, this means a probability of entry of between 0.001 and 0.05, which can be specified as a uniform distribution for modelling purposes.  Since the pest is established in neighbouring Northern Territory, the likelihood of it entering in the absence of appropriate quarantine measures is assumed to be High.  Again, this is specified as a uniform distribution with a minimum value of 0.7 and a maximum of 1.0.[11]

Once T. palmi has entered WA its polyphagous characteristics are assumed to allow it to become established with relative ease.  But, the sheer size of WA (relative to the area of suitable cultivated crops) is expected to restrict colonisation potential.  Hence, establishment capabilities after introduction are assumed to be Moderate.  The probability of establishment is therefore represented by a uniform distribution with a minimum value of 0.3 and a maximum of 0.7.

4.3 Average Total Cost Increments – Producer Surplus Lost

The losses to individual growers resulting from T. palmi becoming established in WA can be expected to comprise of the following:

Management Costs

Production cost increases are to be expected to result from the need for additional chemical and oil sprays several times per season.  Assume insecticides used are methidathion (around $10/L applied at 75-125ml/ha) or imidacloprid (around $300/L applied at 25ml/ha), both of which are registered for thrips control in selected crops in WA (DAWA, 2001).  Given that T. palmi has shown resistance to most chemical insecticides (many of which actually promote its abundance by destroying natural predators), alternative approaches may be required for effective control (CPC, 1999).  One such approach might involve the use of potassium soap (e.g. NATRASOAP, around $130/20L applied at 1L/ha), which acts by suffocating insects rather than poisoning them (Young and Zhang 1998; Young and Zhang, 2000).  Assume additional sprays are necessary between 1 and 3 times per year, which can be modelled using a uniform distribution with a minimum value of 1 and a maximum value of 3.  In addition, vehicle, equipment and labour costs of $35/ha per application are assumed[12].

Loss of Marketable Product

Despite incorporating a spraying program into normal management practise, a small yield loss to T. palmi is still to be expected.  A pert distribution with a minimum value of 0 per cent, a mean of 2.5 per cent and a maximum of 5 per cent per year was used to represent yield losses in the calculations to follow.

4.4 Export Market Losses

It is conceivable that both interstate and international export losses may result from T. palmi becoming widely established in WA.

Interstate Trade

Volume statistics on interstate are currently unavailable.  However, as a rough guide approximately 75 per cent of melon sales from the ORIA are transported east.  The main quarantine protocols of concern are those of South Australia (SA) and Tasmania. (Chris Robinson - DAWA, pers comm, 09/05/2002).

International Trade

Collectively the host commodities modelled earn over $22 million international export revenue annually, some of which may be threatened by the presence of T. palmi requiring additional quarantine treatments.  However, the pest is present in many prominent destinations for WA produce, such as Hong Kong, Singapore, Malaysia and Brunei (AGWEST Trade and Development, 2001).  This makes the imposition of bans on WA exports due to a perceived threat of T. palmi unlikely, and indeed illegal under the terms and conditions of the WTO Agreement.  Currently around 10 per cent of melons from the ORIA are exported internationally, but the extent to which prices are affected by WA’s T. palmi-status is unclear (Chris Robinson - DAWA, pers comm, 09/05/2002).  A variable estimate is assumed using a uniform distribution with a minimum value of 5 per cent and a maximum value of 15 per cent.

4.5 Size and growth of affected industries in susceptible areas

Data indicating the gross value of WA industries susceptible to T. palmi was sourced from the ABS (1998), and supplemented by Cirillo (2001). Relevant figures are contained in table 2.

 

Table 2: Gross Value of Production (GVP) of WA industries potentially affected by general spread of T. palmi

Host crop	GVP (5 year average, 1998-2002)
	WA (excluding ORIA)	ORIA	WA (total)
Cucumber	$       1,705,900	$            25,500	$       1,731,400
Capsicum & Chilli	$       3,729,300	$              9,500	$       3,738,800
Pumpkin	$       3,065,900	$       5,238,000	$       8,303,900
Zucchini	$          490,800	$            98,100	$          588,900
Rock Melon	$       1,662,200	$     10,464,200	$     12,126,400
Water Melon	$       1,986,200	$       4,035,800	$       6,022,000
Nurseries	$     34,408,300	$          428,700	$     34,837,000
Cut Flowers	$     27,869,700	$            19,000	$     27,888,700
Beans	$       1,698,100	$          192,200	$       1,890,300
Cabbage	$       2,188,200	$              4,600	$       2,192,800
Chinese cabbage	$       3,040,400	$              1,000	$       3,041,400
Lettuce	$       7,464,300	$              6,300	$       7,470,600
Onion	$       8,701,900	$            24,100	$       8,726,000
Potato	$     35,366,900	$              3,800	$     35,370,700
Tomato	$       8,462,400	$            19,600	$       8,482,000
Orange	$       2,109,400	$                 100	$       2,109,500
Lemon and Lime	$          706,500	$              1,200	$          707,700
Mandarin	$       1,759,600	$                    -	$       1,759,600
Mango	$       2,094,700	$       1,884,300	$       3,979,000
Avocado	$       2,910,700	$                 400	$       2,911,100
TOTAL	$   151,421,300	$     22,456,500	$   173,877,800

 

5. Results and sensitivity analysis

The impact of T. palmi outside the ORIA was simulated over a 20 year period.  Ten thousand iterations of the model were performed using the Latin Hypercube sampling technique to extract values from each variable specified as a distribution.  Due to the uncertainty surrounding several model parameters it is prudent to run an extensive sensitivity analysis to establish how significantly results change in response to changes in these parameters.  This is particularly useful in that it highlights those variables exerting the greatest influence over the expected benefits of exclusion over time.

5.1 Estimated annual benefits to host industries from containment in the ORIA

Results of an economic assessment of T. palmi isolation to the ORIA indicate that the net present value of the on-farm benefits of excluding the insect from the remainder of WA are between ‑$0.8 million and $1.7 million per annum.  Gross benefits estimated for each industry appear in table 3.  This reveals that the cut flower industry will be the highest beneficiary of T. palmi exclusion (mean of $110,500 per year over twenty years), followed by the Chinese cabbage industry ($53,800 per year), the potato industry ($53,600), the nursery industry ($51,100 per year), the lettuce industry ($40,700) and the onion industry ($35,100 per year).

When industry benefits are represented as a proportion of their Gross Value of Production (GVP) the results are quite different.  As figure 2 graphically demonstrates, when benefits are expressed in these terms the Chinese cabbage industry (0.8 per cent of industry GVP) appears to be the largest beneficiary followed by the rockmelon (0.3 per cent), lettuce (0.2 per cent), onion (0.2 per cent) and cut flower (0.2 per cent) industries.  However, it must be pointed out that these results should not be interpreted in terms of ‘capacity to absorb costs’ since GVP contains no information on production costs.

Table 3: Results

Industry	Annual industry benefits of exclusion over a 20 year period
	Minimum	Mean	Maximum
Capsicums & chilies	-$    67,900	$      6,400	$     74,000
Cucumbers	-$    27,900	$      3,100	$     41,500
Rockmelons	-$  100,100	$    12,800	$   135,500
Watermelons	-$    39,500	$      4,500	$     48,300
Beans (French & runner)	-$    27,900	$      2,900	$     32,100
Cabbages	-$    56,800	$      7,800	$     75,000
Pumpkins	-$    54,700	$      6,400	$     59,000
Oranges	-$    41,800	$      5,900	$     51,500
Lemons and limes	-$    20,900	$      2,300	$     25,200
Mandarins	-$    42,400	$      4,900	$     50,200
Zucchini	-$    11,500	$      1,100	$     12,600
Tomatoes	-$  139,600	$    16,100	$   179,500
Mangoes	-$    54,200	$      6,200	$     72,800
Cut flowers	-$  663,500	$  110,500	$   833,100
Nurseries	-$  344,900	$    51,100	$   465,200
Lettuces	-$  206,600	$    40,700	$   280,000
Potatoes	-$  268,000	$    53,600	$   404,200
Onions	-$  109,500	$    35,100	$   188,900
Avocados	-$    53,000	$      5,400	$     79,600
Chinese cabbage	-$  304,500	$    53,800	$   337,200
TOTAL	-$  831,000	$  430,500	$1,656,400

 

 

 

 

Figure 2: Annual industry benefit from Thrips palmi containment (over 20-years) as a percentage of GVP.

 

 

5.2 Sensitivity analysis

The sensitivity of ED_crit to each key variable is shown in table 4.  Here, the percentage change in the

variable concerned is indicated along with the resultant change (D%) in total benefits.

 

Table 4: Sensitivity of ED_crit to changes in key assumptions

Assumption (most likely value)	Value tested	D%	Expected average annual on-farm cost to affected industries	D%
Probability of entry with exclusion policy (very low)	Extremely low	- 98.0%*	$450,300	+ 4.6%
	Low	+ 586.0%*	$361,300	- 16.1%
Probability of entry without exclusion policy (high)	Moderate	- 41.2%*	$249,200	- 42.1%
	Low	+ 79.4%*	$100,600	+ 76.6%
Probability of establishment (moderate)	Low	- 65.0%*	$159,600	- 62.9%
	High	+ 70.0%*	$720,200	+ 67.3%
Discount rate (7.00%)	3.5%	- 50.0%	$532,100	+ 23.6%
	10.5%	+ 50.0%	$351,100	- 18.4%
Satellite infestation parameter, m (1.0 ´ 10-3)	1.0 ´ 10-4	- 90.0%	$423,700	- 1.6%
	1.0 ´ 10-2	+ 900.0%	$479,400	+ 11.4%
Average total cost increment (excl. yield loss) ($90/ha/yr - 2 sprays, ie. distribution  mid point)	$45/ha/yr - 1 spray	- 50.0%	$423,300	- 1.7%
	$135/ha/yr – 3 sprays	+ 50.0%	$436,000	+ 1.3%
Percentage of yield loss despite control effort (2.5% - distribution mean)	0.0%	- 100.0%*	$269,500	- 37.4%
	5.0%	+ 100.0%*	$688,500	+ 59.9%
Proportion of host industries potentially affected (» 75%**)	50.0%	- 25.0%	$367,600	- 14.6%
	100.0%	+ 25.0%	$485,600	+ 12.8%
Export revenue losses attributable to loss of pest-freedom status (10% - distribution mid point)	5.0%	- 50.0%*	$384,700	- 10.6%
	15.0%	+ 50.0%*	$543,900	+ 26.3%
Change in producer surplus induced by removal of quarantine restrictions (0%)	- 20.0%	na	$433,800	+ 0.8%
	- 50.0%	na	$436,800	+ 1.5%

^*  Indicates mid point or mean of distribution used to calculate D%.

^** Represents mean across all regions.  Actual areas used were taken from ABS (1998).

 

The value the model returns for ED_crit is highly sensitive to changes in the ‘probability of entry without exclusion policy’ (PEn_without) and ‘probability of establishment’ (PEs), but not so the ‘probability of entry with exclusion policy’ (PEn_with).  Intuitively, these three variables might be expected to produce similar sensitivities, but since PEn_with is extremely small in nominal terms it tends to exert a smaller influence over ED_crit.  As implied by table 1 the mid point of the very low risk category distribution is 2.55 ´ 10^-2, so even changes as large as ‑96.0 or +586.0 per cent are not significant in relative terms.  In contrast the mid point of the high risk category distribution used to represent PEn_without is 0.85, and that of the moderate distribution representing PEs is 0.50.  Changes in these parameters are therefore more influential on ED_crit.

Changes in the ‘discount rate’ also produced significant alterations in ED_crit, although not to the same extent.  This is entirely expected since the analysis was carried out over a 20‑year period, so future benefits are increasingly eroded by successive increments in the opportunity cost of quarantine resources.  This includes the benefits of exclusion attributable to the growth of industries affected.

Since the T. palmi host crops used in the model are generally characterised by high value, intensive plantings it is to be expected that changes in the ‘percentage of yield loss despite control effort’ will have a relatively large impact on ED_crit.  This variable is used in acknowledgement of the fact that no control measures taken against the pest can be realistically assumed as 100 per cent effective.  It is reasonable to expect a certain amount of crop loss even though appropriate control measures may have been taken to counter the effects of T. palmi.  However, determining this figure for each individual crop is difficult since it requires knowledge of the pest’s behaviour in new environments and the susceptibility of each crop to pest damage.

Uncertainty also surrounds the capacity of T. palmi to spread to all horticultural regions in WA if it were to move beyond the ORIA.  In recognition of this uncertainty the ‘proportion of host industries potentially affected‘ was included in the sensitivity analysis,.  The most likely values for this variable for each of the modelled host industries was assumed to be the proportion of the industry outside of the south west region of the State.  Conditions in the south west may be prohibitively cold, leaving roughly 75 per cent of susceptible crop areas expected to be affected by T. palmi.  This is a speculative assumption, and sensitivities extended to the possibility that all growing regions would be affected.  The south west is a very prominent region in WA in terms of its horticultural output, and this is reflected in the relatively high sensitivity of ED_crit to changes in this variable, particularly upwards.

All other variables tested for sensitivity produced minor changes in ED_crit.  The ‘satellite infestation parameter’ was anticipated to produce small changes in the results since the analysis was only carried out over a 20-year period and the probability of satellite occurrence was assumed to be small.  Had the time frame been larger this variable would be expected to exert a much stronger influence over the results.  The ‘average total cost increment (excl. yield loss)’ variable too produced a low sensitivity due to the additional costs the pest would impose upon affected growers are relatively small in comparison to the value of their crops.  Hence, the ‘percentage of yield loss despite control effort’ exhibits a higher sensitivity.  The ‘export revenue loss attributable to loss of pest-freedom status’ was also insignificant in determining ED_crit.  This is a subjective variable in the sense that its estimation involves making assumptions about the behaviour of trading partners to the altering pest status of WA and their own individual pest status.

6. Discussion

The findings of section 5 give an indication of the potential benefits a general system of pest assessment can deliver to those empowered with quarantine policy formulation.  The methodology demonstrated here provides quarantine policy-makers with a high level of information concerning the repercussions of their decisions.  Perhaps the two most critical pieces of information concern the strategic importance of the pest to the State economy, and maximum amount of resources that should be expended to combat an outbreak of the pest concerned.

The techniques used to calculate ED_crit are directly applicable to every exotic pest species.  If assessments could be completed for each species of concern to primary stakeholders it would be possible to compare and rank each pest according to the economic benefits of excluding them from the State.  Each pest’s individual ED_crit provides an indication of the ability to use quarantine as a front-line method of preventing it from entering and becoming established amongst suitable hosts in WA.  Hence, those with a high ED_crit value are of highest strategic significance to policy-makers, and vice versa.  If quarantine resources are targeted towards those species of strategic significance then the State could be said to be fully embracing the concept of ‘managed risk’.  Pest risks are assessed, quantified, and resources targeted to those areas where it is believed the highest economic benefits (in terms of damage or cost increments averted) can be produced.

In addition to indicating economic importance, eliciting values of ED_crit specific to pest species also identifies a critical level of expected damage to be used as a point of reference in the event of an outbreak.  For instance, in the case of T. palmi results indicate that the critical level of expected damage associated with the pest is in the order of $430,500 per annum.  If an incursion outside the ORIA were to occur this represents the maximum amount of resources that can be spent annually to combat the pest before the costs of counter measures outweigh the benefits of exclusion.  The use of such a measure guards against over-exuberance in the desire to rid an area of a pest causing abatement expenditures to exceed the benefits of area-freedom.  The removal of a pest does not necessarily produce a net social welfare gain.

When an outbreak is detected it is vital that surveys be conducted to determine the full extent of the outbreak.  This information can then be used to estimate an annual cost of eradicating the outbreak (C_e) and the probability that eradication can be completed successfully over time (P_e).  These estimates can then be compared to the value of ED_crit corresponding to the pest to determine the appropriate course of action to take in response to the incursion.  If P_e ´ C_e < ED_crit, then an eradication campaign should be embarked upon without delay to maximise the chances of success.  On the other hand, if P_e ´ C_e > ED_crit an alternative strategy should be sought in which it is accepted the objective is ‘damage minimisation’ as opposed to eradication.

It is conceded that such a rigid interpretation of economic modelling results poses a problem as far as non-market goods are concerned.  Invariably decisions of this nature require supplementary information where pest species pose additional threats to native biodiversity, and/or whose introduction could force members of rural communities to seek employment in other economic sectors.  Moreover, the ‘visibility’ of these types of effects has the potential to create political imperatives that take precedence over all other information.  This makes for an interesting state of affairs with politicians, environmental scientists, sociologists and economists all having input into the decision-making process.  Quarantine analysis therefore represents a unique opportunity for interdisciplinary co-operation to deliver socially desirable outcomes.

7. Conclusions

The model presented in this analysis is capable of providing quarantine decision-makers with a high level of information concerning the repercussions of their decisions.  Not only does it disclose the level of damage to be expected by a pest, thereby indicating its strategic importance, but it also identifies a critical level of expected damage to be used as a point of reference in the event of an outbreak.  When applied to the case of T. palmi in WA results indicate that the critical level of expected damage associated with the pest is in the order of $430,500 per annum.  Hence, if an incursion outside the ORIA were to occur this represents the maximum expenditure limit of an eradication campaign before the costs begin to outweigh the benefits of exclusion.

If decisions on the course of action to be embarked upon in response to an outbreak can be made swiftly the probability of successfully eradicating outbreaks greatly improves.  If widely employed to analyse the potential impact of large numbers of pests, it is possible that the model presented in this paper could form the basis of a system of quarantine prioritisation.  While this is technically feasible, it is important to recognise supplementary information in the analytical process.  Non-price information can and should exert an influence over decision-makers, so some system of including qualitative information is necessary.  Nevertheless, the economic model of pest impact presented here may serve as an important building block on which future research effort can be devoted.  If an effective system of pest prioritisation and response determination is forthcoming it has the potential to greatly improve WA’s efficiency and accountability with regard to quarantine policy and administration.

 8. References

ABS (1998). Agstats Database – 1992/93 to 1996/97. Australian Bureau of Statistics, Canberra.

AFFA (2001). Guidelines for Import Risk Analysis. Agriculture, Fisheries and Forestry Australia/Biosecurity Australia, Canberra: Australian Government Publishing Service.

AGWEST Trade & Development (2001). AGTRADE Database. Department of Agriculture – Western Australia, Perth.

CABI (1999). Crop Protection Compendium – Global Module. Cayman Islands: CAB International.

Chiang, A.C. (1984). Fundamental Methods of Mathematical Economics, 3rd Ed. Singapore: McGraw-Hill.

Cirillo, L. (2001). The Australian Horticultural Statistics Handbook. Sydney: Horticulture Australia.

DAWA (2001). Farm Weekly ‘Farm Budget Guide’ 2001. Perth, Australia: Department of Agriculture Western Australia.

James, S. and Anderson, K. (1998). On the Need for More Economic Assessment of Quarantine Policies. Australian Journal of Agricultural and Resource Economics 42, 425-444.

Lewis, T. (1973). Thrips, Their Biology, Ecology and Economic Importance. London: Academic Press.

Moody, M.E. and Mack, R.N. (1988). Controlling the Spread of Plant Invasions: The Importance of Nascent Foci. Journal of Applied Ecology 25, 1009-1021.

Planck, J. (2001). Melon Thrips: A Quarantine Pest of Some Fruit and Vegetables. Brisbane: Department of Primary Industries.

Waage, J., Mumford, J. and Fraser, R. (2001). Biosecurity, Unpublished Seminar Paper. Department of Agricultural Science, Imperial College at Wye, November.

WAQIS (1999). Interstate Quarantine WA: Operations Manual. South Perth: Western Australian Quarantine and Inspection Service.

Young, G.R. and Zhang, L. (1998). Control of the Melon Thrips, Thrips palmi. Darwin, Australia: Primary Industry and Fisheries Northern Territory

Young, G.R. and Zhang, L. (2000). IPM of Melon Thrips, Thrips palmi Karny (Thysanoptera:Thripidae), on Eggplant in the Top End of the Northern Territory Darwin, Australia: Primary Industry and Fisheries Northern Territory.