G. Criterios para selección de especies de estudio

more commonly referred to as the spray or action threshold and the economic injury level as the economic threshold (Blackshaw, 1995). In practical terms there are different types of economic threshold depending upon their flexibility and how they have been determined (Table 3.3; Poston et al., 1983; Morse and Buhler,

1997). At one end of the scale are the sub- jective ETs (nominal and simple thresh- olds), which are more or less fixed figures representing an average of the pest density at which the cost of control is warranted, and at the other end of the scale are objec- tive ETs, which are based on comprehen- sive research (Morse and Buhler, 1997). The objective ETs are based on estimated ETs and are flexible over time whereas the subjective ones are typically derived by experience and are often no more than ‘rules of thumb’ or ‘guestimates’. In prac- tice, it is the subjective ETs that predomi- nate (Pedigo, 1996) and amongst these the ‘action threshold’ is very common.

Action thresholds have been calculated for a number of insect species, e.g.

Amblyomma americanum on cattle (Barnard et al., 1986), Aeneolamia varia in sugar cane

(Norton and Evans, 1974), Keiferia lycopersi-

cella in tomato, Acyrthosiphon pisum on

green peas (Yencho et al., 1986), Tipula species in barley (Norton, 1976; Blackshaw, 1994), Epiphayas postvittana in top fruit (Valentine et al., 1996) and aphids in cereals (Elliott et al., 1990). Composite thresholds have been calculated for Helicoverpa

armigera, Earias vittelli and Pectinophora gossypiella in cotton (Keerthisinghe, 1982)

and for Trichoplusia ni, Plutella xylostella and Pieris rapae in cabbage (Kirby and Slosser, 1984).

Considerable effort has gone into eco- nomic thresholds (e.g. Table 3.4 for pests of soybean) but this may have reached its limit for some crop pest complexes. Such a large number of variables makes it extremely dif- ficult to obtain economic thresholds that are generally applicable. Hence, although the economic threshold concept serves as a basis for decision making in insect pest management, the determination of such thresholds has proved to be one of the weakest components in management pro- grammes, with the result that very few research based thresholds have been devel- oped (Poston et al., 1983). Ultimately some situations are just too complex or, irrespec- tive of the suitability of the data or how good the understanding of damage func- tions, some pest-crop complexes are just inherently uncertain (Table 3.5). Thresholds represent only one way of assisting decision making and should not be seen as a univer- sal solution (Mumford and Knight, 1997). Table 3.3. Types of thresholds employed in crop protection (from Morse and Buhler, 1997; Poston et al., 1983).

Type of threshold Description

Nominal Based on field experience and logic (e.g. subjective ET) Values remain static

Simple Calculated from crude quantification of the ‘average’ pest-host relationship (e.g. subjective ET) in terms of pest damage potential, crop market value, control costs,

and potential crop yield

Generally inflexible to change over time

Comprehensive Based on interdisciplinary research incorporating the total production (e.g. objective ET) system on a given farm and including factors such as multiple pest

and crop stress effects Very flexible to change over time

Fixed ET: set at a fixed percentage of the EIL

Descriptive ET: includes projections of pest population growth based on simulation models

Dichotomous ET: based on samples taken over time and classifying the population as economic or non-economic as a result of analysing the sample data

Table 3.4. Economic injury levels (EILs) for selected foliage- and pod-feeding arthropods on soybean at growth stages R3 to R5. Values based on average recommendations for the major soybean growing regions of the USA, which may differ for different growing regions of the world (Sinclair et al., 1997).

Pest complex Representative species Economic injury level Foliage-feeding arthropods at growth stages R3 to R5

Lepidopterous Anticarsia gemmatalis 20–25 larvae (>1.2 cm)/row m + 15% defoliation

Epinotia aporema 30% of growing tips attacked1 Helicoverpa zea 8–10 larvae/m row

Plathypena scabra 25–30 larvae (>1 cm)/row m + 15% defoliation

Pseudoplusia includens 20–25 larvae (>1.2 cm)/row m + 15% defoliation

Coleopterous2 _{Cerotoma spp. 20 beetles/row m + 15% defoliation} Epilachna varivestis 15–20 adults + larvae (<0.5 cm)/row

m + 15% defoliation Pod-feeding arthropods at growth stages R5 to R7

Coleopterous Cerotoma spp. 20–25 beetles/row m = 8–12% pod injury

Hemipterous Various species3 _{2–3 large bugs/row m} Lepidopterous Helicoverpa zea 2–3 larvae (>1.5 cm)/m row

Tortricid and Pyralid spp. EILs not defined 1_{EIL defined for conditions in southern Brazil.}

2_{Other Coleopterous species that could also follow these EILs are: Aulacophora sp., Colaspis brunnea,} Diabrotica spp. and various species of Meloidae.

3_{Includes: Acrosternum hilare and Nezara viridula.}

3.10 Discussion

Yield loss assessment data are fundamental to insect pest management, since they are the means by which an insect is judged a pest. Yield is also the ultimate criterion by which the efficacy of control measures is assessed, and they form the basis for deci- sion making in insect pest management programmes. Despite these reasons, there are surprisingly few yield loss studies car- ried out, and this is true for both developed and developing countries (Reed, 1983). To a large extent the reasons for this are asso- ciated with the difficulties involved in car- rying out yield loss experiments. Even the simplest approach to crop loss assess- ments, paired-plot comparisons, are

fraught with difficulties. Economic ento- mologists apply insecticides knowing that they provide the largest return if they are applied to dense, well fertilized, high yielding cultivars. Hence, many paired-plot comparisons are carried out under such conditions which can result in massive yield differences, thereby giving high esti- mates of insect pest losses (Reed, 1983). Given the difficulties of yield loss assess- ment it is not surprising that yield losses are assumed when large pest infestations occur, or when losses are so obvious that it is considered inappropriate to waste effort quantifying losses when control measures are desperately needed. Even when thresh- olds have been obtained, they may not be used correctly (Wratten et al., 1990), only

as a guide (Blackshaw, 1994), they may be inefficient (Waibel, 1987) or uneconomic (Szmedra et al., 1990). Hence, there appear to be few incentives for carrying out exper- iments to obtain yield loss data and calcu- late economic thresholds.

It could be that a change of emphasis is required. The methodologies exist, it is just the complex interactions of so many vari- ables over space and time that seem to make the loss assessment approach so unworthwhile. Perhaps longer term experi- ments with pests in specific ecological zones are necessary to resolve the problem (Judenko, 1972). In the same way that research institutes and experimental sta-

tions are prepared to run long term moni- toring devices such as suction or light traps (not without substantial investment of time and resources in some instances), so they could run long term field evaluations of crop losses to pests. National and regional research institutes would be obvious loca- tions for such work. These studies should be carried out in tandem with those pro- viding information for crop physiological models (Sections 3.5.1; 3.5.2) and the sim- ulation models (e.g. Allen, 1981). This combined approach should ensure that yield loss assessment receives adequate attention and provides the much needed baseline information for IPM programmes. Table 3.5. Conditions that make threshold prediction more or less uncertain (Mumford and Knight, 1997).

Threshold uncertainty Threshold uncertainty

is reduced is increased

Prices Fixed Market values

Type of damage Direct (i.e. feeding on the part of plant harvested) Indirect Crop growth Consistent (irrigation) Variable (rainfed)

Pest attack Short duration Long duration

Slow reproduction Fast reproduction

Tied to crop stage Independent of crop stage

Endemic pests Immigrant pests

Later season Earlier season

Weather conditions Controlled (greenhouse) Temperate areas Constant (irrigated crops, arid areas)

Scouting Cheap Expensive

Easy to detect damage or presence Cryptic stages or damage Control application No pesticide resistance Variable resistance

Not affected by weather Affected by weather (adjuvants/stickers, or in dry climate)

Natural enemies Very high or very low Variable natural control Natural control, stable source nearby

4.1 Introduction

Chemical insecticides have been consid- ered an essential component of insect pest control since the early 1950s when organochlorine insecticides were first widely introduced. Since that time, how- ever, the problems associated with insecti- cide misuse and the advent of more ecologically sound IPM approaches have raised doubts about the wholesale use of insecticides as a sole means of pest control. Increasingly the use of chemical insecti- cides has been considered in terms of judi- cial applications within the context of a more sustainable IPM approach. Despite this, however, the chemical insecticide market is estimated to be worth US$8 bil- lion annually which ably demonstrates the value placed on insecticides by farmers and other purchasers worldwide. While it may be appropriate to decry insecticides for their poor environmental and safety record, insecticide use remains a corner- stone of pest management and is likely to continue as such for many years to come.

The correct and rational use of insecti- cides is a complex process that draws on a thorough knowledge of:

•

insect population dynamics and the

impact of chemical use;

•

the active ingredients of the insecticide,

its mode of action and formulation;

•

delivery of the chemical, its application

and pick-up at the target site;

•

ease of use, safety and economics;

•

toxicological and ecotoxicological impact

and insecticide resistance.

In this chapter, all of these factors are con- sidered in order to provide an overview of the benefits, difficulties and problems asso- ciated with chemical insecticide use.

4.2 Objectives and Strategies

In document El Salvador, C.A. Manual de Inventarios de la Biodiversidad (página 76-89)