Open access peer-reviewed chapter

Resistance to neurotoxic insecticides in populations of the coffee leafminer Leucoptera coffeella

Written By

Daianna P. Costa, Flávio L. Fernandes, Flávia M. Alves, Ézio M. da Silva and Liliane E. Visôtto

Submitted: March 19th, 2015 Reviewed: September 9th, 2015 Published: March 2nd, 2016

DOI: 10.5772/61466

Chapter metrics overview

1,802 Chapter Downloads

View Full Metrics


Coffee leafminer Leucoptera coffeella is an important pest on coffee. The continued use of chemicals can result in loss of efficacy and selection of leafminer-resistant populations. We aimed to identify L. coffeella populations resistant to old and new neurotoxic insecticides in regions of Brazil. We collected seven populations of L. coffeella in Brazil. Low levels of resistance were observed for the insecticides chlorantraniliprole (1.02-3.23 times), abamectin (1.19-4.80 times), and deltamethrin (1.05-5.35 times). High resistance levels were observed for profenofos (65.3-522 times) and chlorpyrifos (4.53-18.63 times). We conclude that Brazilian L. coffeella populations showed greater resistance to organophosphate insecticides. Furthermore, resistance may be associated with the distance between the coffee-producing regions.


  • Anthranilamide
  • Coffea spp
  • Lepidoptera
  • lethal time
  • organophosphate

1. Introduction

The coffee leafminer Leucoptera coffeella (Guérin-Méneville, 1842) (Lepidoptera: Lyonetiidae) is originally from Africa and has become a pest species of great significance in many countries producing coffee (Coffea arabica and Coffea canephora) [1,2]. The extremely variable life cycle of this species and their damage to coffee crops make them a pest with a high destruction capacity [3-5]. Insecticides provide the most efficient method of controlling this pest, with more than 30 different active pesticides registered for use against this L. coffeella in Brazil [6]. Despite the existence of several active ingredients, the overuse of pesticides by farmers has led to the insects becoming resistant [7].

The first documented case of resistance was in 1914, in San Jose Scale (Quadraspidiotus perniciosus) (Comstock, 1881) (Hemiptera: Diaspididae) exposed to repeated doses of sulfur powder [8]. Reports of insect resistance began to increase in the 1940s as insecticides and miticides emerged. There are over 7740 reported cases of resistance, involving 331 compounds and more than 540 species of insects and mite pests [9]. From 1914 to 2007, the vast majority of cases of resistance occurred in Lepidoptera, with 1799 confirmed cases.

Lepidopteran species such as Alabama argillacea (Lepidoptera: Noctuidae) [10], Plutella xylostella (Lepidoptera: Plutellidae) [11], and Tuta absoluta (Lepidoptera: Gelechiidae) [12] have shown resistance to several groups of insecticides. These authors studied insect populations from different locations, using different groups of insecticides with varying mechanisms of action. Studies with L. coffeella, however, have focused only on the organophosphate group with no studies on other chemical groups [13,14]. As such, studies of different populations and various insecticide groups are needed.

Among the insecticides used, most are neurotoxins, and it is this group that presents the most problems of insect resistance [9]. These neurotoxic insecticides (e.g., organophosphates and pyrethroids) cause rapid death of susceptible insects, and abamectin, neonicotinoids, and diamides are slower in causing death of insects [15].

It is therefore possible to detect resistance to a particular active ingredient by comparing the time of death of each population to different neurotoxic insecticides. Similar experiments have been done with other insects, such as the mosquito Culex tarsalis (Diptera: Culicidae) [16]. Slower deaths may indicate the population is beginning to become resistant. Delayed mortality could be compared to the effect of sublethal doses, which put the insects in a state of stress and reduce their metabolism before death [17]. One way to detect resistance using the lethal time of death (LT) is to collect geographically distant populations to obtain more precise information and compare populations across regions since the resistance is relative. Thus, based on the mechanism of action of each insecticide group, it is possible to compare resistance by measuring how quickly the insecticides act on a population.

There are two studies focusing on the detection of insecticide resistance among populations of L. coffeella and just with organophosphate insecticides. Our proposal is to study different groups and regions. This study aimed to recognize populations of L. coffeella in different regions of Brazil that were resistant to neurotoxic insecticides by comparing the lethal time.


2. Materials and methods

2.1. Insect populations

This study was conducted at the Laboratory of Integrated Pests Management at Universidade Federal de Viçosa, Rio Paranaíba Campus (UFV-CRP). We selected six municipalities with coffee cultivation of the species C. arabica and C. canephora, located in producing regions of the Brazilian states of Minas Gerais, Espírito Santo, São Paulo, and Pernambuco. These areas were selected because they are the largest coffee producing regions in Brazil. In these regions, we collected leaves from the middle third of plants randomly selected in commercial crops during the 2012-2013 crop season, with active mines (live caterpillars) of L. coffeella. These crops were georeferenced with the help of a portable Garmin E-trex Summit Hc GPS (Figure 1).

Figure 1.

Location and characterization of Leucoptera coffeella collection in coffee-producing regions. Dark spots represent coffee-producing regions. White spherical symbols within the dark spots represent collection sites of leafminer populations.

The leaves collected in each region were transported to the laboratory in separate plastic bags for visual selection of mines that did not present any harm (e.g., open or with signs of parasitism/predation). Selected mined leaves were combined for insect rearing in a greenhouse (20 × 10 m). These leaves were placed in vials with water (25 mL) inside wooden cages covered with organza. The larvae were fed seedlings coffee of Catuaí cultivar grown in a greenhouse without insecticide application. Only larvae with at least one generation in the laboratory were used in bioassays to prevent the expression of insecticide tolerance due to differing environmental conditions at the different sampling sites (i.e., differences without any genetic basis).

2.2. Insecticides

Six neurotoxic insecticides were selected for bioassays of L. coffeella resistance to the concentrated active ingredients abamectin 18 g l-1 EC (emulsifiable concentrate) (Syngenta, São Paulo, Brazil), chlorantraniliprole 350 g l-1 WG (water-dispersible granules) (DuPont, Paulínia, Brazil), chlorpyrifos 480 g l-1 EC (Fersol, Mairinque, Brazil), deltamethrin 25 g l-1 EC (Bayer SA, São Paulo, Brazil), profenofos 550 g l-1 EC (Syngenta, São Paulo, Brazil), and thiamethoxam 250 g l-1 WG (Syngenta, São Paulo, Brazil) (Table 1).

Insecticide Population LT50 a (CI95%)b n RT50 c χ2 d(df)e Pf
Abamectin Rio Paranaíba-MG 13.29 (11.29–15.34) 40 1.74 2.87 (3) 0.59
Abaeté dos Mendes-MG 14.70 (12.59–16.53) 40 1.92 6.95 (3) 0.07
Carmo do Paranaíba-MG 36.75 (33.63–40.97) 40 4.80 4.05 (3) 0.26
Santa Teresa-ES 9.11 (6.03–11.52) 40 1.19 2.89 (3) 0.59
Guaranhuns-PE 17.85 (15.87–19.71) 40 2.33 4.97 (3) 0.17
Franca-SP 12.41 (10.99–15.12) 40 1.62 3.42 (3) 1.12
Guaraciaba-MG 7.65 (6.85–10.11) 40 1.00 5.63 (3) 3.11
Chlorpyrifos Rio Paranaíba-MG 8.16 (7.02–9.20) 40 8.08 7.35 (4) 0.12
Abaeté dos Mendes-MG 17.18 (15.68–18.75) 40 17.01 9.07 (4) 0.06
Carmo do Paranaíba-MG 16.39 (15.12–17.76) 40 16.23 1.66 (3) 0.65
Santa Teresa-ES 4.58 (3.62–5.54) 40 4.53 7.56 (5) 0.18
Guaranhuns-PE 8.59 (6.70–10.21) 40 8.50 2.39 (3) 0.50
Franca-SP 18.82 (17.54–20.15) 40 18.63 8.20 (4) 0.08
Guaraciaba-MG 1.01 (0.35–2.07) 40 1.00 6.32 (7) 0.06
Chlorantraniliprole Rio Paranaíba-MG 27.70 (24.70–31.56) 40 1.98 3.66 (3) 0.30
Abaeté dos Mendes-MG 26.30 (22.15–34.79) 40 1.88 1.57 (2) 0.54
Carmo do Paranaíba-MG Santa Teresa-ES 14.01 (11.87–16.47) 40 1.00 7.51 (5) 0.18
Santa Teresa-ES 31.53 (28.44–35.74) 40 2.25 5.50 (3) 0.14
Guaranhuns-PE 18.82 (17.54–20.15) 40 3.23 8.20 (4) 0.08
Franca-SP 14.28 (11.00–18.23) 40 1.02 6.30 (5) 1.22
Guaraciaba-MG 8.59 (6.70–10.21) 40 1.88 2.39 (3) 0.50
Deltamethrin Rio Paranaíba-MG 31.12 (27.59–36.20) 40 5.35 4.96 (4) 0.17
Abaeté dos Mendes-MG 25.73 (23.34–28.56) 40 4.42 3.83 (3) 0.28
Carmo do Paranaíba-MG 28.18 (24.46–34.29) 40 4.84 2.22 (3) 0.53
Santa Teresa-ES 5.82 (4.23–7.65) 40 1.00 5.99 (4) 0.07
Guaranhuns-PE 20.38 (17.53–23.23) 40 3.50 6.04 (3) 0.11
Franca-SP 18.82 (17.54–20.15) 40 3.23 8.20 (4) 0.08
Guaraciaba-MG 6.11 (5.03–7.84) 40 1.05 5.81 (4) 0.06
Profenofos Rio Paranaíba-MG 15.66 (13.96–17.17) 40 522 0.85 (5) 0.66
Abaeté dos Mendes-MG 12.25 (11.10–13.19) 40 408 1.35 (6) 0.51
Carmo do Paranaíba-MG 6.96 (4.28–9.00) 40 232 3.85 (5) 0.28
Santa Teresa-ES 1.96 (0.08–3.00) 40 65.3 3.71 (3) 2.32
Guaranhuns-PE 10.96 (8.50–11.00) 40 365 1.36 (4) 0.44
Franca-SP 12.96 (8.24–14.53) 40 432 4.12 (3) 0.21
Guaraciaba-MG 0.03 (0.01–0.50) 40 1.00 1.58 (3) 0.23
Thiamethoxam Rio Paranaíba-MG 37.29 (33.32–43.21) 40 4.41 2.54 (3) 0.53
Abaeté dos Mendes-MG 23.10 (21.11–25.27) 40 2.73 0.43 (3) 0.93
Carmo do Paranaíba-MG 89.93 (61.70–180.00) 40 10.61 6.54 (4) 0.16
Santa Teresa-ES 10.49 (9.13–11.78) 40 1.24 8.65 (4) 0.07
Guaranhuns-PE 13.57 (12.07–14.87) 40 1.61 7.69 (3) 0.06
Franca-SP 8.45 (7.07–10.95) 40 1.00 5.66 (3) 1.05
Guaraciaba-MG 9.36 (7.01–10.34) 40 1.11 6.71 (3) 0.06

Table 1.

Time and mortality curves (LT50) of Brazilian populations of Leucoptera coffeella under the effect of seven insecticides at the recommended doses.

aLT50 = time (h) lethal to kill 50% of the population.

bCI = confidence interval of 95%.

cRT50 = ratio of lethal time to kill 50% of the population.

dχ2 = chi-square.

edf = degrees of freedom.

fP = probability.

The registered label rates of the respective active ingredients in Brazil were 0.18 mg mL-1 (0.026 mg a.i. mL-1) for abamectin, 0.072 mg mL-1 and 0.078 mg a.i. mL-1 for chlorantraniliprole, 0.05 mg mL-1 (4.800 mg a.i. mL-1) for chlorpyrifos, 0.032 mg mL-1 (0.013 mg a.i. mL-1) for deltamethrin, 0.4 mg mL-1 (1.100 mg a.i. mL-1) for profenofos, and 0.024 mg mL-1 (2.000 mg a.i. mL-1) for thiamethoxam.

2.3. Time-mortality bioassay

For time-mortality analysis, circular discs (diameter 90 mm) of filter paper were dipped into the insecticide solutions diluted in distilled water, using the recommended doses to control L. coffeella. The control used embedded disks with distilled water. The discs containing the insecticides and the water were fixed on a clothesline to dry in the shade and then placed separately into Petri dishes (9.0 × 1.5 cm). Ten larvae of L. coffeella reared in the lab were transferred to each Petri dish using a fine-tipped brush. The Petri dishes with the larvae were kept in the BOD incubator (model SP-500) at 25°C ± 1°C until the time of evaluation. The experiments were conducted in a completely randomized design with four replications.

Preliminary tests using only discs soaked in water were carried out to observe caterpillar mortality over a 48-h period. This was necessary to estimate the maximum evaluation time after bioassay assembly that causes 20% lower mortality in the control [18]. Thus, to have a mortality range from 0% to 100%, evaluations were made at 2, 6, 12, 16, 24, 32 and 48 h (treatments) after bioassay assembly. The time intervals were assessed in independent experimental units, to avoid pseudoreplicates. We considered insects dead when they did not move after being touched with the fine-tipped brush.

2.4. Spatial dependence of insecticide resistance

To determine the spatial dependence of L. coffeella insecticide resistance, the semivariance statistical model of LT50 values to L. coffeella populations for each insecticide and the distance between sampling locations of each population were used. The distance between the sampling sites of each insect population was determined using geographic coordinates with a global positioning system (GPS 12, Garmin International, Olathe, KS). The semivariograms were estimated from the semivariance data of the LTs50 of each population for each insecticide and used as dependent variables in regression analysis, with the distance between the sampling sites as an independent variable. The first inflection point of the semivariogram curve represents the maximum distance of interference between the populations of L. coffeella in relation to susceptibility to a given insecticide.


3. Results and discussion

Resistance to neurotoxic insecticides varied generally among the different populations of L. coffeella in Brazil. RT50 varied from 1.02 to 522. Low resistance levels were observed for chlorantraniliprole insecticides (1.02-3.23 times), abamectin (1.19-4.80 times), and deltamethrin (1.05-5.35 times).

On the other hand, intermediate resistance was observed for thiamethoxam (1.11-10.61 times) and chlorpyrifos (4.53-18.63 times), while resistance was high for profenofos (65.3-522 times) (Table 1). Higher levels of organophosphate resistance were observed in Minas Gerais (Abaeté dos Mendes, Rio Paranaíba and Carmo do Paranaíba), Pernambuco (Guaranhuns), and São Paulo (Franca).

The RT50 values are supported by the LT50 values, which were variable among populations and insecticides. The population from Carmo do Paranaíba-MG was noteworthy as it took 89.93 h for 50% of the population to die after contact with the insecticide thiamethoxam. The organophosphate and pyrethroid insecticides had lower lethal times. Chlorantraniliprole showed lower LT50 of 8.59 h.

Two canonical axes were significant among the five canonical axes identified, showing linear associations between LT50 of the insecticides with the geographical regions of the population origins of L. coffeella, which showed that the four canonical axes were significant, with the first three axes explaining 90% of the total variance data (Table 1 and Figure 2). The highest absolute values of the canonical coefficients show which insecticides most contributed to the standard deviation of resistance among the different localities. For the first canonical axis of greater importance in the analysis, the insecticides chlorpyrifos, profenofos, and deltamethrin showed positive correlations and higher values of coefficients and thus higher contributions to the differences between the resistant populations (Table 2). Profenofos and deltamethrin, with a positive relationship, contributed to the pattern of divergence on the second axis.

The opposite relationship was observed for assistance with the chlorpyrifos insecticide on the third and fourth axes. On the fifth and sixth axes, a positive relationship was observed between the profenofos insecticide and the standard deviation. It is important to highlight that the new insecticide chlorantraniliprole did not contribute to the resistance of populations (Table 2). Graphs of this analysis done with the first two axes explained 92% of the total variance of the data to show the grouping between locations (Table 2 and Figure 2).

The weight of organophosphate (profenofos and chlorpyrifos) and pyrethroid (deltamethrin) insecticides on the first two axes enhanced the resistance process since they are among the main groups with examples of insect resistance (quotation). Two grouping patterns were observed, with one group for the populations of L. coffeella Rio Parnaíba-MG, Carmo do Paranaíba-MG, and Abaeté dos Mendes-MG and a second group for the populations of Santa Teresa-ES and Guaraciaba-MG, but these patterns did not occur in the other populations (Figure 2).

Variables/mortality Canonical axes
1 2 3 4 5 6
Abamectin –0.21 –0.10 –0.21 –0.46 –0.38 –0.05
Chlorpyrifos 0.77 –0.65 –0.33 –0.45 –0.10 –0.35
Chlorantraniliprole –0.10 0.00 –0.21 0.00 0.04 0.00
Deltamethrin 0.51 0.68 0.17 0.05 0.39 0.55
Profenofos 0.64 –0.59 0.38 0.55 0.64 0.64
Thiamethoxam 0.40 0.42 0.11 0.19 0.40 0.38
F 31.02 25.51 20.36 15.02 13.55 9.11
dfx a 68; 181 54; 181 46; 181 32; 181 20; 181 16; 181
R 2 x b 0.90 0.89 0.78 0.66 0.61 0.53

Table 2.

Canonical axes and coefficients (grouped in the canonical structure) of mortalities of Leucoptera coffeella caused by six neurotoxic insecticides.

a dfx = degrees of freedom (numerator/denominator).

bR2xb = canonical correlation square.

Figure 2.

Ordination diagram showing discrimination of insecticide resistance between Brazilian populations of Leucoptera coffeella. Spherical gray symbols are centroids of treatments and represent the average of canonical variable classes. Large circles indicates treatment groups with no significant difference between them (approximate F test, P < 0. 05), based on the Mahalanobis distance (D2) between averages.

The semivariogram models related to the LT50 values of L. coffeella with the distance between the sampling sites obtained for only two insecticides, the organophosphates chlorpyrifos and the pirimiphos. The first inflection points for the models were lengths of 169 and 1,956 km for the insecticides chlorpyrifos and pirimiphos (Figure 3). Therefore, these were the maximum distances between the interference resistance levels of the L. coffeella sampling sites.

Figure 3.

Semivariogram of the LT50 of chlorpyrifos, profenofos, and deltamethrin according to the distance between sampled points from populations of Leucoptera coffeella. The first inflection point of the semivariogram curve, represented by a down ward-pointing arrow, represents the maximum distance of interference of the resistance to the insecticides.

Our study reported high variations in the resistance ratio (RT50) of the organophosphates profenofos (522 times) and chlorpyrifos (19 times) compared to the susceptible population of L. coffeella. This large variation represented by RT50 indicates that populations show differences in susceptibility and greater or lesser sensitivity to the enzyme acetylcholinesterase since variations were observed between populations that died the fastest and those that died more slowly.

This shows that this group of insecticides is extremely important in managing resistance because of its intense use, with this group being highly toxic and presenting higher neurotoxic action [19]. Many studies on resistance to the organophosphate insecticide group showed high variation in the mortality of the resistant population compared to other lepidopteran populations [20,21]. Extensive insecticide use in coffee crops and high death speed are among the main factors of resistance [22]. Fragoso et al. [13] observed up to 22 applications of organophosphate insecticides, detecting high levels of resistance when larvae were kept exposed to the discriminating concentration. These concentrations were higher than those tested for profenofos and chlorpyrifos in our study.

On the other hand, chlorantraniliprole, abamectin, and deltamethrin insecticides showed low levels of RT50 variation. The result with the chlorantraniliprole insecticide was as expected since this insecticide has only recently been commercialized [23-25] and has a highly efficient molecule since low doses of this insecticide (31.5 g a.i. ha-1) cause high mortality to L. coffeella; moreover, it is selective for wasps [26].

Selectivity is an important factor in managing resistance in pest insects [27]. Many studies with basic lines of susceptibility have been done with chlorantraniliprole insecticide and Lepidoptera, and the observations are that populations show susceptibility with low variation in mortality [28,29]. The insecticide abamectin is not considered old and has been effective in controlling this pest insect, with no flaws detected in its control of L. coffeella as of yet. Despite the abamectin insecticide not being among those at risk of resistance in L. coffeella, this insecticide has not been studied. However, many arthropod pests have been classified as being at risk for resistance to this group. Among them are Leptinotarsa decemlineata (Say) [30], Musca domestica [31], P. xylostella [32], Frankliniella occidentalis [33], and Tetranychus urticae [34]. Abamectin resistance has been observed in populations of F. occidentalis [35] and Liriomyza trifolii [36]. Deltamethrin had surprising results, with low discrepancy between the resistant and the susceptible populations (5 times) compared to their insecticides such as thiamethoxam (10 times) that are less used in coffee plantation. In recent years, however, the number of pyrethroid applications in coffee production has been greatly reduced. Despite the low resistance to pyrethroids, however, the variation has been observed in Brazil for the moth P. xylostella [37] as well as with other pyrethroids (cypermethrin, β-cypermethrin, deltamethrin, and esfenvalerate) in Pakistan, India, China, and Korea [38,39]. Although deltamethrin has affected fewer Brazilian populations of L. coffeella, a difference of 5 times is cause for concern since it should have been more effective.

Insects usually have a resistance mechanism that confers nerve insensitivity, known as knockdown resistance (Kdr), as first reported in M. domestica (L.) (Diptera: Muscidae) [40]. This type of resistance is found in other agricultural pests based on patterns of cross-resistance and the absence of compound synergism that inhibits the activity of cytochrome P450 and esterase enzymes [41].

The insecticide thiamethoxam has been frequently used and can be applied as a spray or via the soil [42]. There are no studies of lepidopteran resistance to this insecticide. Control failures were observed depending on the time of application, however, for example [43] observed effectiveness of 4.1%, 50.6%, 62.1%, and 69.0%.

The grouping of populations from Rio Paranaíba, Carmo do Paranaíba, and Abaeté (Group I) and Santa Teresa with Guaraciaba (Group II), coupled with the significant response of the effect of distance on the LT50 of the chlorpyrifos, profenofos, and deltamethrin insecticides, showed that resistance was affected by the collection distance of these populations since more closely connected populations had similar resistance responses.

Studies have shown a strong relationship between collection distance and resistance patterns [44,10,45,12]. All of these studies showed significant association of resistance with distance, and nearby populations tended to show more similar responses, as is the case for P. xylostella (L.) (Lepidoptera: Plutellidae). Chen et al. [46] studied the resistance of pyrethroids to Culex pipiens (Diptera: Culicidae) and found different frequencies of resistance at different locations, ranging from 21.4% to 79.8%. Moreover, this type of response may be associated with the large dispersal capacity of adult L. coffeella and the sampling characteristics.

Adults of L. coffeella disperse easily between coffee crops and have different densities in different environments [47-49]. Moreover, there is a geographic corridor between the largest-producing Brazilian states (Figure 1). Isaaks and Srivastava [50] also found that in order to detect differences among geostatistical studies of spatial distribution, it was necessary to collect both near and distant samples.

We conclude that Brazilian populations of L. coffeella showed greater resistance to organophosphates. Furthermore, resistance may be associated with the distance between the producing regions, and local selection favored by dispersal seem important for insecticide resistance evolution among Brazilian populations of L. coffeella and should be considered in designing pest management programs. The insecticides that do not show mortality to L. coffeella should be sprayed in such conditions, and a higher variety of insecticides (out of the cross-resistance and multiple-resistance spectra) should be used in rotation to reduce the danger of evolution of resistance.



The funding and fellowships provided by the following Brazilian agencies were greatly appreciated: CAPES Foundation from the Brazilian Ministry of Education, National Council for Scientific and Tecnological Development (CNPq), and Minas Gerais State Foundation for Research Aid (FAPEMIG).


  1. 1. Lomeli-Flores RJ, Barrera JF, Bernal JS. Impacts of weather, shade cover and elevation on coffee leafminer Leucoptera coffeella (Lepidoptera: Lyonetiidae) population dynamics and natural enemies. Crop Protection. 2010; 29: 1039-1048. DOI: 10.1016/j.cropro.2010.03.007
  2. 2. Righi CA, Campoe OC, Bernardes MS, Lunz AMP, Piedade SMS, Pereira CR. Influence of rubber trees on leaf-miner damage to coffee plants in an agroforestry system. Agroforestry Systems. 2013; 87: 1351-1362. DOI: 10.1007/s10457-013-9642-9.
  3. 3. Ramiro DA, Guerreiro-Filho O, Queiroz-Voltan RB, Matthiesen SC. Caracterização anatômica de folhas de cafeeiros resistentes e suscetíveis ao bicho-mineiro. Bragantia. 2004; 63: 363-372, DOI: 10.1590/S0006-87052004000300006
  4. 4. Vega FE, Posada F, Infante F. Coffee insect: ecology and control. In: Pimentel D, editor. Encyclopedia of Pest Management. 1st ed. London: Taylor & Francis; 2007. p. 1-4. DOI: 10.1081/E-EPM-120042132
  5. 5. Michereff MFF, Michereff-Filho M, Vilela EF. Comportamento de acasalamento do bicho-mineiro-do-cafeeiro, Leucoptera coffeella (Guérin-Mèneville) (Lepidoptera: Lyonetiidae). Neotropical Entomology. 2007; 36: 376-382. DOI: 10.1590/S1519-566X2007000300005
  6. 6. MAPA-Ministério da Agricultura, Pecuária e Abastecimento [Internet]. 2014. Available from: Accessed: 2014-12-22.
  7. 7. Fragoso DB, Guedes RNC, Rezende ST. Glutathione S-transferase detoxification as a potential pyrethroid resistance mechanism in the weevil, Sithophilus zeamais. Entomologia Experimentalis et Applicata. 2003; 109: 21-29. DOI: 10.1046/j.1570-7458.2003.00085.x
  8. 8. Messing R, Croft BA. Insecticide in pest management. In: Metcalf RL, Luckman WH, editors. Introduction to Insect Pest Management. 1st ed. New York: John Wiley & Sons; 1994. p. 217-277.
  9. 9. Whalon M, Mota-Sanchez D, Hollingworth RM. Global Pesticide Resistance in Arthropods. 7th ed. London: CABI; 2009. 192 p. DOI: 10.1079/9781845933531.0000
  10. 10. Silva TBM, Siqueira HAA, Oliveira AC, Torres, JB, Oliveira JV, Montarroyos PAV, Farias M. Insecticide resistance in Brazilian populations of the cotton leaf worm, Alabama argillacea. Crop Protection. 2011; 30: 1156-1161. DOI: 10.1016/j.cropro.2011.05.022
  11. 11. Zago HB, Siqueira HAA, Pereira EJG, Picanço MC, Barros R. Resistance and behavioral response of Plutella xylostella (Lepidoptera: Plutellidae) populations to Bacillus thuringiensis formulations. Pest Management Science. 2014; 70: 488-495. DOI: 10.1002/ps.3600
  12. 12. Gontijo PC, Picanço MC, Pereira EJG, Martins JC, Chediak M, Guedes RNC. Spatial and temporal variation in the control failure likelihood of the tomato leafminer, Tuta absoluta. Annals of Applied Biology. 2012; 162: 50-59. DOI: 10.1111/aab.12000
  13. 13. Fragoso DB, Guedes RNC, Picanço MC, Zambolim L. Insecticide use and organophosphate resistance in the coffee leafminer Leucoptera coffeella (Lepidoptera: Lyonetidae). Bulletin of Entomological Research. 2002; 92: 203-212. DOI: 10.1079/BER2002156
  14. 14. Ribeiro BM, Guedes RNC, Oliveira EE, Santos JP. Insecticide resistance and synergism in Brazilian populations of Sitophilus zeamais (Coleoptera: Curculionidae). Journal of Stored Products Research. 2003; 39: 21-31. DOI: 10.1016/S0022-474X(02)00014-0
  15. 15. Nauen R. Insecticide mode of action: return of the ryanodine receptor. Pest Management Science. 2006; 62: 690-692. DOI: 10.1002/ps.1254
  16. 16. Strong AC, Kondratieff BC, Doyle MS, Black WC. Resistance to permethrin in Culex tarsalis in northeastern Colorado. Journal of the American Mosquito Control Association. 2008; 24: 281-288. DOI: 10.2987/5593.1
  17. 17. Piiroinen S, Lyytinen A, Lindström L. Stress for success? Temperature stress of preceding generations modifies the response to insecticide stress in an invasive pest insect. Evolutionary Applications. 2013; 6: 313-323. DOI: 10.1111/eva.12001
  18. 18. Szendrei Z, Grafius E, Byrne A, Ziegler A. Resistance to neonicotinoid insecticides in field populations of the Colorado potato beetle (Coleoptera: Chrysomelidae). Pest Management Science. 2012; 68: 941-946. DOI: 10.1002/ps.3258
  19. 19. Baron RL. Delayed neurotoxicity and other consequences of organophosphate esters. Annual Review of Entomology. 1981; 26: 29-48. DOI: 10.1146/annurev.en.26.010181.000333
  20. 20. Zibaee A, Sendi JJ, Ghadamyari M, Alinia F, Etebari K. Diazinon resistance in different selected strains of Chilo suppressalis (Lepidoptera: Crambidae) in northern Iran. Journal of Economic Entomology. 2009; 102: 1189-1196. DOI: 10.1603/029.102.0343
  21. 21. Saeed Q, Saleem MA, Ahmad M. Toxicity of some commonly used synthetic insecticides against Spodoptera exigua (Fab) (Lepidoptera: Noctuidae). Pakistan Journal of Zoology. 2012; 44: 1197-1201.
  22. 22. Alves PMP, Lima JOG, Oliveira LM. Monitoramento da resistência do bicho-mineiro-do-café, Leucoptera coffeella (Lepidoptera: Lyonetiidae), a inseticidas em Minas Gerais. Anais da Sociedade Entomológica do Brasil. 1992; 21: 77-91.
  23. 23. Jeanguenat A. The story of a new insecticidal chemistry class: the diamides. Pest Management Science. 2013; 69: 7-14. DOI: 10.1002/ps.3406
  24. 24. Sattelle DB, Cordova D, Cheek TR. Insect ryanodine receptors: molecular targets for novel pest control chemicals. Invertebrate Neuroscience. 2008; 8: 107-119. DOI: 10.1007/s10158-008-0076-4
  25. 25. Lahm GP, Cordova D, Barry JD. New and selective ryanodine receptor activators for insect control. Bioorganic & Medicinal Chemistry. 2009; 17: 4127-4133. DOI: doi:10.1016/j.bmc.2009.01.018
  26. 26. Fernandes FL, Silva PR, Gorri JER, Pucci LF, Silva IW. Selectivity of old and new insecticides and behaviour of Vespidae predators in coffee crop. Sociobiology. 2014; 60: 471-476. DOI: 10.13102/sociobiology.v60i4.471-476
  27. 27. Casida JE, Durkin KA. Neuroactive insecticides: targets, selectivity, resistance, and secondary effects. Annual Review of Entomology. 2013; 58: 99-117. DOI: 10.1146/annurev-ento-120811-153645
  28. 28. Silva EJ, Siqueira HAA, Silva TBM, Campos RM. Baseline susceptibility to chlorantraniliprole of Brazilian populations of Plutella xylostella. Crop Protection. 2012; 35: 97-101. DOI: 10.1016/j.cropro.2012.01.013
  29. 29. Gao C, Yao R, Zhang Z, Wu M, Zhang S, Su J. Susceptibility baseline and chlorantraniliprole resistance monitoring in Chilo suppressalis (Lepidoptera: Pyralidae). Journal of Economic Entomology 106: 2190-2194. DOI: 10.1603/EC13058
  30. 30. Argentine JA, Clark JM. Selection for abamectin resistance in Colorado potato beetle (Coleoptera: Chrysonelidae). Pesticide Science. 1990; 28: 17-24. DOI: 10.1002/ps.2780280104
  31. 31. Scott JG, Roush RT, Liu N. Selection of high-level abamectin resistance from field-collected house flies, Musca domestica. Experientia. 1991; 47: 288-291. DOI: 10.1007/BF01958163
  32. 32. Liang P, Gao X, Zheng B. Genetic basis of resistance and studies on cross-resistance in a population of diamondback moth, Plutella xylostella (Lepidoptera: Plutellidae). Pest Management Science. 2003; 59: 1232-1236. DOI: 10.1002/ps.760
  33. 33. Chen X, Yuan L, Du Y, Zhang Y, Wang J. Cross-resistance and biochemical mechanisms of abamectin resistance in the western flower thrips, Frankliniella occidentalis. Pesticide Biochemistry and Physiology. 2011; 101: 34-38. DOI: 10.1016/j.pestbp.2011.07.001
  34. 34. Kwon DH, Seong GM, Kang TJ, Lee SH. Multiple resistance mechanisms to abamectin in the two spotted spider mite. Journal of Asia-Pacific Entomology. 2010; 13: 229-232. DOI: 10.1016/j.aspen.2010.02.002
  35. 35. Immaraju JA, Paine TD, Bethke JA, Robb KL, Newman JP. Western flower thrips (Thysanoptera: Thripidae) resistance to insecticides in coastal California greenhouse. Journal of Economic Entomology. 1992; 85: 9-14. DOI: 10.1093/jee/85.1.9
  36. 36. Ferguson SJ. Development and stability of insecticide resistance in the leafminer Liriomyza trifolii (Diptera: Agromyzidae) to cyromazine, abamectin and spinosad. Journal of Economic Entomology. 2004; 97: 112-119. DOI: 10.1093/jee/97.1.112
  37. 37. Oliveira AC, Siqueira HA, Oliveira JV, Silva JE, Michereff-Filho M. Resistance of Brazilian diamondback moth populations to insecticides. Scientia Agricola. 2011; 68: 154-159. DOI: 10.1590/S0103-90162011000200004
  38. 38. Kwon DH, Choi BR, Park HM, Lee SH, Miyata T, Clark JM, Lee SH. Knockdown resistance allele frequency in field populations of Plutella xylostella in Korea. Pesticide Biochemistry and Physiology. 2004; 80: 21-30. DOI: 10.1016/j.pestbp.2004.06.001
  39. 39. Balasubramani V, Sayyed AH, Crickmore N. Genetic characterization of resistance to deltamethrin in Plutella xylostella (Lepidoptera: Plutellidae) from India. Journal of Economic Entomology. 2008; 101: 1911-1918. DOI: 10.1603/0022-0493-101.6.1911
  40. 40. Busvine JR. Mechanism of resistance to insecticide in houseflies. Nature. 1951; 168: 193-195. DOI: 10.1038/168193a0
  41. 41. Soderlund DM. Molecular mechanisms of insecticide resistance. In: Sjut V, editor, Molecular Mechanisms of Resistance to Agrochemicals. Berlin: Springer; 1997. p. 21-56. DOI: 10.1007/978-1-4684-6429-0_4
  42. 42. Martins LD, Rodrigues WN, Tomaz MA, De Souza AF, De Jesus Junior WC. Função de crescimento vegetativo de mudas de cafeeiro conilon a níveis de ciproconazol+tiametoxam e nitrogênio. Revista de Ciências Agrárias. 2012; 35: 173-183.
  43. 43. Jones AK, Raymond-Delpech V, Thany SH, Gauthier M, Sattelle DB. The nicotinic acetylcholine receptor gene family of the honey bee, Apis mellifera. Genome Research. 2006; 16: 1422-1430. DOI: 10.1101/gr.4549206
  44. 44. Scott JG, Alefantis TG, Kaufman PE, Rutz DA. Insecticide resistance in house flies from caged-layer poultry facilities. Pest Management Science. 2000; 56: 147-153. DOI: 10.1007/s00436-009-1425-x
  45. 45. Shah R, Worner SP, Chapman RB. Determination of the influence of dispersion pattern of pesticide-resistant individuals on the reliability of resistance estimates using different sampling plans. Bulletin of Entomological Research. 2012; 102: 531-538. DOI: 10.1017/S0007485312000065
  46. 46. Chen L, Zhong D, Zhang D, Shi L, Zhou G, Gong M, Zhou H, Sun Y, Ma L, He J, Hong S, Zhou D, Xiong C, Yan G. Molecular ecology of pyrethroid knockdown resistance in Culex pipiens pallens mosquitoes. PloS One. 2010; 5: 1-9. DOI: 10.1371/journal.pone.0011681
  47. 47. Bacca T, Lima ER, Picanço MC, Guedes RN, Viana JHM. Optimum spacing of pheromone traps for monitoring the coffee leafminer Leucoptera coffeella. Entomoloia Experimentalis et Applicata. 2006; 119: 39-45. DOI: 10.1111/j.1570-7458.2006.00389.x
  48. 48. Bacca T, Lima ER, Picanço MC, Guedes RN, Viana JHM. Sampling plan for the coffee leafminer Leucoptera coffeella with sex pheromone traps. Journal of Applied Entomology. 2008; 132: 430-438. DOI: 10.1111/j.1439-0418.2007.01264.x
  49. 49. Fernandes FL, Mantovani, EC, Neto HB, Nunes VV. Effects of irrigation, environmental variability and predatory wasp on Leucoptera coffeella (Guerin-Meneville) (Lepidoptera: Lyonetiidae), in coffee plants. Neotropical Entomology. 2009. 38: 410-417. DOI: 10.1590/S1519-566X2009000300018
  50. 50. Isaaks EH, Srivastava RM. An Introduction to Applied Geostatistics. 1st ed. Oxford: University Press; 1989. 592 p.

Written By

Daianna P. Costa, Flávio L. Fernandes, Flávia M. Alves, Ézio M. da Silva and Liliane E. Visôtto

Submitted: March 19th, 2015 Reviewed: September 9th, 2015 Published: March 2nd, 2016