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The sustainability of coffee production is a concern for producers around the world. To be sustainable, it is necessary to achieve satisfactory levels of coffee productivity and quality. Pests and diseases cause reduced productivity and can affect the quality of coffee beans. To ensure sustainability, producers need to monitor pests that can lead to substantial crop losses, such as the coffee leaf miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae), which belongs to the Lepidoptera order and the Lyonetiidae family. This research aimed to use machine learning techniques and vegetation indices to remotely identify infestations of the coffee leaf miner in coffee-growing regions. Field assessments of coffee leaf miner infestation were conducted in September 2023. Aerial images were taken using remotely piloted aircraft to determine 13 vegetative indices with RGB (red, green, blue) images. The vegetation indices were calculated using ArcGis 10.8 software. A comprehensive database encompassing details of coffee leaf miner infestation, vegetation indices, and crop data. The dataset was divided into training and testing subsets. A set of four machine learning algorithms was utilized: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). Following hyperparameter tuning, the test subset was employed for model validation. Remarkably, both the SVM and SGD models demonstrated superior performance in estimating coffee leaf miner infestations, with kappa indices of 0.6 and 0.67, respectively. The combined use of vegetation indices and crop data increased the accuracy of coffee leaf miner detection. The RF model performed poorly, while the SVM and SGD models performed better. This situation highlights the challenges of tracking coffee leaf miner infestations in fields with varying ages of coffee plants, different cultivars, and other environmental variables. [ABSTRACT FROM AUTHOR]

Metarhizium (Hypocreales: Clavicipitaceae) has a multifunctional life cycle, establishing as a plant endophyte and acting as entomopathogenic fungi. Metarhizium robertsii and Metarhizium brunneum can be associated with coffee plants and provide enhanced protection against a major pest of coffee, the coffee leaf miner (CLM) (Leucoptera coffeella). This association would be an easily deployable biological control option. Here we tested the potential of inoculating coffee seeds with M. robertsii and M. brunneum collected from the soil of coffee crops in the Cerrado (Brazil) for control of the CLM and the enhancement of plant growth with a commonly used fungicide. We conducted the experiment in a greenhouse and after the seedlings grew, we placed them in a cage with two couples of CLMs. We evaluated the CLM development time, reproduction, and plant growth traits. We observed a longer development time of CLMs when fed on plants inoculated with both isolates. In addition, the CLMs laid fewer eggs compared to those fed on plants without fungal inoculation. Plant growth was promoted when seeds were inoculated with fungi, and the fungicide did not affect any evaluated parameter. Coffee seed inoculation with M. robertsii and M. brunneum appears to provide protection against CLMs and promote growth improvement. [ABSTRACT FROM AUTHOR]

The sustainability of coffee production is a concern for producers around the world. To be sustainable, it is necessary to achieve satisfactory levels of coffee productivity and quality. Pests and diseases cause reduced productivity and can affect the quality of coffee beans. To ensure sustainability, producers need to monitor pests that can lead to substantial crop losses, such as the coffee leaf miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae), which belongs to the Lepidoptera order and the Lyonetiidae family. This research aimed to use machine learning techniques and vegetation indices to remotely identify infestations of the coffee leaf miner in coffee-growing regions. Field assessments of coffee leaf miner infestation were conducted in September 2023. Aerial images were taken using remotely piloted aircraft to determine 13 vegetative indices with RGB (red, green, blue) images. The vegetation indices were calculated using ArcGis 10.8 software. A comprehensive database encompassing details of coffee leaf miner infestation, vegetation indices, and crop data. The dataset was divided into training and testing subsets. A set of four machine learning algorithms was utilized: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). Following hyperparameter tuning, the test subset was employed for model validation. Remarkably, both the SVM and SGD models demonstrated superior performance in estimating coffee leaf miner infestations, with kappa indices of 0.6 and 0.67, respectively. The combined use of vegetation indices and crop data increased the accuracy of coffee leaf miner detection. The RF model performed poorly, while the SVM and SGD models performed better. This situation highlights the challenges of tracking coffee leaf miner infestations in fields with varying ages of coffee plants, different cultivars, and other environmental variables.

The coffee leaf miner (Leucoptera coffeella) is a key pest in coffee-producing regions in Brazil. The objective of this work was to evaluate the potential of machine learning algorithms to identify coffee leaf miner infestation by considering the assessment period and Sentinel-2 satellite images generated on the Google Earth Engine platform. Coffee leaf miner infestation in the field was measured monthly from 2019 to 2023. Images were selected from the Sentinel-2 satellite to determine 13 vegetative indices. The selection of images and calculations of the vegetation indices were carried out using the Google Earth Engine platform. A database was generated with information on coffee leaf miner infestation, vegetation indices, and assessment times. The database was separated into training data and testing data. Nine machine learning algorithms were used, including Linear Discriminant Analysis, Random Forest, Support Vector Machine, k-nearest neighbors, and Logistic Regression, and a principal component analysis was conducted for each algorithm. After optimizing the hyperparameters, the testing data were used to validate the model. The best model to estimate miner infestation was RF, which had an accuracy of 0.86, a kappa index of 0.64, and a precision of 0.87. The developed models were capable of monitoring coffee leaf miner infestation.

The coffee leaf miner (Leucoptera coffeella) is a key pest in coffee-producing regions in Brazil. The objective of this work was to evaluate the potential of machine learning algorithms to identify coffee leaf miner infestation by considering the assessment period and Sentinel-2 satellite images generated on the Google Earth Engine platform. Coffee leaf miner infestation in the field was measured monthly from 2019 to 2023. Images were selected from the Sentinel-2 satellite to determine 13 vegetative indices. The selection of images and calculations of the vegetation indices were carried out using the Google Earth Engine platform. A database was generated with information on coffee leaf miner infestation, vegetation indices, and assessment times. The database was separated into training data and testing data. Nine machine learning algorithms were used, including Linear Discriminant Analysis, Random Forest, Support Vector Machine, k-nearest neighbors, and Logistic Regression, and a principal component analysis was conducted for each algorithm. After optimizing the hyperparameters, the testing data were used to validate the model. The best model to estimate miner infestation was RF, which had an accuracy of 0.86, a kappa index of 0.64, and a precision of 0.87. The developed models were capable of monitoring coffee leaf miner infestation. [ABSTRACT FROM AUTHOR]

Metarhizium (Hypocreales: Clavicipitaceae) has a multifunctional life cycle, establishing as a plant endophyte and acting as entomopathogenic fungi. Metarhizium robertsii and Metarhizium brunneum can be associated with coffee plants and provide enhanced protection against a major pest of coffee, the coffee leaf miner (CLM) (Leucoptera coffeella). This association would be an easily deployable biological control option. Here we tested the potential of inoculating coffee seeds with M. robertsii and M. brunneum collected from the soil of coffee crops in the Cerrado (Brazil) for control of the CLM and the enhancement of plant growth with a commonly used fungicide. We conducted the experiment in a greenhouse and after the seedlings grew, we placed them in a cage with two couples of CLMs. We evaluated the CLM development time, reproduction, and plant growth traits. We observed a longer development time of CLMs when fed on plants inoculated with both isolates. In addition, the CLMs laid fewer eggs compared to those fed on plants without fungal inoculation. Plant growth was promoted when seeds were inoculated with fungi, and the fungicide did not affect any evaluated parameter. Coffee seed inoculation with M. robertsii and M. brunneum appears to provide protection against CLMs and promote growth improvement.

Coffee is a worldwide commodity, and both coffee-producing and coffee-consuming countries have real concerns about environmental problems and economic growth strategies based on the efficient use of resources. Because this crop is a perennial, pests can significantly affect coffee production, causing considerable yield losses and threatening coffee supply and security. The presence of insects and control strategies for coffee pests is becoming a challenge. Environmental sustainability, conservation of biodiversity, and safety of the coffee seed must go hand in hand with the economic sustainability of coffee growers. This is especially important, as there has been an increase in demand for coffee and new consumer interest in differentiated quality coffee. Regular pest control methods based only on the use of synthetic pesticides are no longer effective or sustainable due to the development of insecticide resistance and negative effects on the environment, human health, and biodiversity. Thus, to ensure better control and ecological sustainability, it is crucial to reduce pesticide use by adopting original alternative strategies to maintain pest populations below the economic threshold level and towards reaching the European Green Deal. In this review, we collect information available for sustainable control of the principal coffee pests in Colombia: coffee berry borer (CBB), Hypothenemus hampei; Monalonion velezangeli; coffee root mealybugs; coffee leaf miner: Leucoptera coffeella; and the coffee red spider mite: Oligonychus yothersi. The control strategies include deep knowledge of the biology of insects and the coffee plant, their relationship with weather and habitats, as well as natural controllers. These control strategies do not involve the use of insecticides, are ecologically friendly and novel, and can be applied in other coffee-producing countries. [ABSTRACT FROM AUTHOR]

ABSTRACT This research is related to the evaluation of vegetative growth parameters, initial yield and incidence of pests and diseases of twelve Arabica coffee cultivars submitted to the subtropical humidity environment of Vale do Ribeira Paulista, Brazil. The experiment was performed in the Experimental Campus of the UNESP in Registro, in a randomized block design, with three replications. Stem diameter, number of plagiotropic branches, plant height, number of internodes of plagiotropic branches, length of the first plagiotropic branch, incidence of brown eye spot and phoma leaf spot on coffee leaves, infestation by leaf miner and the yield of cultivars were evaluated. The results obtained allowed to conclude that: i) cultivars Catuaí Amarelo IAC 62, Catuaí Vermelho IAC 99 and Mundo Novo IAC 379-1 were higher than the others, with yield above the national average in 2020 and percentage of flat beans below 10%; ii) all cultivars evaluated showed a similar level of increment of variables that indicate plant growth and reproductive development; iii) the cultivation environment is favorable to infestation by the leaf miner and unfavorable to the incidence of rust; iv) The prevailing climatic conditions in the Vale do Ribeira Paulista is suitable for the production of Arabica coffee.

ABSTRACT The coffee leaf miner is responsible for significant decreases in coffee production. To mitigate the problems caused by this pest control strategies need to be applied. The combined application of chemical and biological insecticides is an alternative that can reduce the population of leaf miner in areas with high infestation and occurrence of resistant individuals. The objective of this study was to evaluate the combined application of Isaria fumosorosea and beta-cyfluthrin in the control of coffee leaf miner. A randomized block design was used, in a 4 x 2 factorial scheme, with four treatments: I. fumosorosea, beta-cyfluthrin, I fumosorosea + beta-cyfluthrin and control, besides two application levels, one and two applications. There was no significant effect on the interaction between treatments. The number of applications did not increase the control percentage of the leaf miner. However, the treatments I. fumosorosea, beta-cyfluthrin, I fumosorosea + beta-cyfluthrin showed a higher control percentage of the leaf miner, compared to the control treatment with both one and two applications. I. fumosorosea, beta-cyfluthrin, and the combination of I fumosorosea + beta-cyfluthrin are effective in controlling the leaf miner.

The coffee leaf miner (Leucoptera coffeella) is a key coffee pest in Brazil that can cause severe defoliation and a negative impact on the productivity. Thus, it is essential to identify initial pest infestation for the sake of appropriate time control to avoid further economic damage to the coffee crops. A fast non-destructive method is an important tool that can be used to monitor the occurrence of the coffee leaf miner. The present work aims to identify the occurrence of coffee leaf miner infestation through a new vegetation index, using multispectral images from the Sentinel-2 satellite and the Google Earth Engine platform. Coffee leaf miner infestation was measured in the field in four cities in the state of Minas Gerais. The largest infestations occurred in September, October, and November but particularly in October 2021, in which the rate of infestation reached 85%, followed by September 2020 with a maximum infestation of 76%. The calculation steps of the vegetation indices and mappings were carried out in the Google Earth Engine cloud processing platform through the development of a script in JavaScript programming language. Combinations of two sensitive bands were selected to detect coffee leaf miner infestation, and from these, the "Coffee-Leaf-Miner Index" was developed, which was compared with other existing vegetation indices in terms of their performance for coffee leaf miner detection. The combination of the NIR–BLUE and NIR–RED bands was more sensitive for the detection of coffee leaf miner infestation; therefore, the NIR, BLUE, and RED bands were selected to develop the new index. The "Coffee-Leaf-Miner Index" presented the best performance among those evaluated, with a coefficient of determination of about 0.87, a root mean square error of 4.92% coffee leaf miner infestation, accuracy of 89.47%, and kappa coefficient of 95.39. The R2 range of other spectral indices which exist in the literature and which were used in this study was from 0.017 to 0.867, and the root mean square error ranged from 4.996 to 13.582% coffee leaf miner infestation. The machine learning method was then adopted using the supervised Random Forest and Support Vector Machine algorithms to recognize patterns of coffee leaf miner infestation in the field, only the Coffee-Leaf-Miner Index was used for the identification test of the coffee leaf miner infestation. The Support Vector Machine with linear Kernel type was applied to establish a discrimination model. The number of trees for the Random Forest classifier was 100. The Support Vector Machine presented a lower performance than the Random Forest algorithm, but the performance of both were above 80% for user and producer precision. Three bands (Blue, Red, NIR) were selected for the creation of the new index, which showed capacity for remote detection of coffee leaf miner infestation on a regional scale. Thus, "Coffee-Leaf-Miner Index" can identify coffee leaf miner infestation thanks to all the complexity involved in detecting pests via orbital remote sensing. [ABSTRACT FROM AUTHOR]

Coffee crop has been growing increasingly in Brazil and, in view of this, technologies are constantly being developed to obtain greater productivity. In this context, the effect of polyethylene mulching as a source of fertilizer on coffee production has been studied. The objective of this work was to evaluate the incidence of leaf miner and red mite in coffee as a function of the use of polyethylene mulching and controlled and conventional release fertilizer. The research was carried out in the experimental area of the Federal University of Uberlândia - Campus Monte Carmelo, Minas Gerais, with the cultivar IPR 100, in a randomized block design with four blocks. The following treatments were tested: T1 - double-faced white/black mulching with conventional fertilizer; T2 - white/black double-sided mulching with controlled release fertilizer; T3 - double-sided silver/black mulching with conventional fertilizer; T4 - silver/black double-sided mulching with controlled release fertilizer; T5 - No mulching with conventional fertilizer; T6 - No mulching with controlled release fertilizer. The evaluations took place fortnightly from July 2020 to December 2020, upon verification of the presence or absence of leaf miner and red mite caterpillars. Population density peaks for leaf miner occurred from July to October 2020, and for red mite in August, due to low relative humidity and precipitation. Thus, it is concluded that the use of double-sided silver/black mulching provided greater attractiveness to the coffee leaf miner. The fertilizer sources did not influence the probability of occurrence of this insect pest. In addition, conventional fertilizers, as well as those of controlled release, and the absence/presence of soil coverings, did not influence the incidence of red mite in coffee trees in the Cerrado Mineiro region. [ABSTRACT FROM AUTHOR]

This research is related to the evaluation of vegetative growth parameters, initial yield and incidence of pests and diseases of twelve Arabica coffee cultivars submitted to the subtropical humidity environment of Vale do Ribeira Paulista, Brazil. The experiment was performed in the Experimental Campus of the UNESP in Registro, in a randomized block design, with three replications. Stem diameter, number of plagiotropic branches, plant height, number of internodes of plagiotropic branches, length of the first plagiotropic branch, incidence of brown eye spot and phoma leaf spot on coffee leaves, infestation by leaf miner and the yield of cultivars were evaluated. The results obtained allowed to conclude that: i) cultivars Catuaí Amarelo IAC 62, Catuaí Vermelho IAC 99 and Mundo Novo IAC 379-1 were higher than the others, with yield above the national average in 2020 and percentage of flat beans below 10%; ii) all cultivars evaluated showed a similar level of increment of variables that indicate plant growth and reproductive development; iii) the cultivation environment is favorable to infestation by the leaf miner and unfavorable to the incidence of leaf rust; iv) The prevailing climatic conditions in the Vale do Ribeira Paulista is suitable for the production of Arabica coffee. [ABSTRACT FROM AUTHOR]

• Pesticide use and the landscape lead to variations in the shape, size, and Fluctuating asymmetry of females of Closterocerus coffeellae wings. • Females of C. coffeella with larger wings show a strongly or moderately aggregated spatial pattern in coffee plantations. • C. coffeellae presents greater potential to biocontrol, and it must be conserved. Closterocerus coffeellae (Hymenoptera: Eulophidae) is a parasitoid of the coffee leaf miner occurring in coffee farms with high selection pressure caused by pesticides and different landscape patterns on the surrounding crops. This work aims to detect morphometric variations and levels of Fluctuating Asymmetry (FA) in the wings of C. coffeellae in coffee farms with a history of insecticide use and different landscape composition in the Planalto region, Bahia, Brazil. Parasitoids originated from six coffee farms in this region. We prepared microscopy slides of the right wings of 30 C. coffeellae females from each population. We determined variations in wing size and shape by geometric morphometry analysis, adding 1 landmark and 9 semi-anatomical landmarks to the wings. The landscape metrics (3 km radius) and levels of spatial dependence for size and AF were calculated using geostatistical analysis. Higher forest cover and edge density in coffee plantations lead to an increase in the wing size of C. coffeellae and reduced wing FA levels. Diversity of the surrounding agricultural land cover in coffee farms and pesticide use reduces wing size and increases levels of FA in coffee farms. The centroid size exhibited strong and moderate aggregation levels across coffee farms. The conservation of C. coffeellae is recommended since the species can be used as a bioproduct against the coffee leaf miner, and as a biocontrol solution for coffee farms. Furthermore, adopting conservative practices of natural vegetation surrounding coffee farms is essential for the parasitoids. It must be included in the coffee leaf miner management, mainly in farms with higher agricultural cropland diversity on surrounding coffee farms and with higher pesticide use. [ABSTRACT FROM AUTHOR]

The coffee leaf miner is responsible for significant decreases in coffee production. To mitigate the problems caused by this pest control strategies need to be applied. The combined application of chemical and biological insecticides is an alternative that can reduce the population of leaf miner in areas with high infestation and occurrence of resistant individuals. The objective of this study was to evaluate the combined application of Isaria fumosorosea and beta-cyfluthrin in the control of coffee leaf miner. A randomized block design was used, in a 4 x 2 factorial scheme, with four treatments: I. fumosorosea, beta-cyfluthrin, I fumosorosea + beta-cyfluthrin and control, besides two application levels, one and two applications. There was no significant effect on the interaction between treatments. The number of applications did not increase the control percentage of the leaf miner. However, the treatments I. fumosorosea, beta-cyfluthrin, I fumosorosea + beta-cyfluthrin showed a higher control percentage of the leaf miner, compared to the control treatment with both one and two applications. I. fumosorosea, beta-cyfluthrin, and the combination of I fumosorosea + beta-cyfluthrin are effective in controlling the leaf miner. [ABSTRACT FROM AUTHOR]

The coffee leaf miner (Leucoptera coffeella) is a key coffee pest in Brazil that can cause severe defoliation and a negative impact on the productivity. Thus, it is essential to identify initial pest infestation for the sake of appropriate time control to avoid further economic damage to the coffee crops. A fast non-destructive method is an important tool that can be used to monitor the occurrence of the coffee leaf miner. The present work aims to identify the occurrence of coffee leaf miner infestation through a new vegetation index, using multispectral images from the Sentinel-2 satellite and the Google Earth Engine platform. Coffee leaf miner infestation was measured in the field in four cities in the state of Minas Gerais. The largest infestations occurred in September, October, and November but particularly in October 2021, in which the rate of infestation reached 85%, followed by September 2020 with a maximum infestation of 76%. The calculation steps of the vegetation indices and mappings were carried out in the Google Earth Engine cloud processing platform through the development of a script in JavaScript programming language. Combinations of two sensitive bands were selected to detect coffee leaf miner infestation, and from these, the “Coffee-Leaf-Miner Index” was developed, which was compared with other existing vegetation indices in terms of their performance for coffee leaf miner detection. The combination of the NIR–BLUE and NIR–RED bands was more sensitive for the detection of coffee leaf miner infestation; therefore, the NIR, BLUE, and RED bands were selected to develop the new index. The “Coffee-Leaf-Miner Index” presented the best performance among those evaluated, with a coefficient of determination of about 0.87, a root mean square error of 4.92% coffee leaf miner infestation, accuracy of 89.47%, and kappa coefficient of 95.39. The R2 range of other spectral indices which exist in the literature and which were used in this study was from 0.017 to 0.867, and the root mean square error ranged from 4.996 to 13.582% coffee leaf miner infestation. The machine learning method was then adopted using the supervised Random Forest and Support Vector Machine algorithms to recognize patterns of coffee leaf miner infestation in the field, only the Coffee-Leaf-Miner Index was used for the identification test of the coffee leaf miner infestation. The Support Vector Machine with linear Kernel type was applied to establish a discrimination model. The number of trees for the Random Forest classifier was 100. The Support Vector Machine presented a lower performance than the Random Forest algorithm, but the performance of both were above 80% for user and producer precision. Three bands (Blue, Red, NIR) were selected for the creation of the new index, which showed capacity for remote detection of coffee leaf miner infestation on a regional scale. Thus, “Coffee-Leaf-Miner Index” can identify coffee leaf miner infestation thanks to all the complexity involved in detecting pests via orbital remote sensing.

ABSTRACT Coffee production faces challenges due to the susceptibility of the crop to insects. Owing to the coffee leaf miner Leucoptera coffeella, which is one of the main insect pests, the technology for the application of phytosanitary measures becomes the main challenge for coffee growers. This study aimed to evaluate the effectiveness of coffee leaf miner control using different methods of application. The study was conducted in an experimental site in the municipality of Monte Carmelo - Minas Gerais. Spray depositions in different positions of the plant canopy and the effective control of coffee leaf miner were evaluated using the following application treatments: a hydropneumatic sprayer with varying spray volumes (200 and 400 L ha-1), two hollow-cone spray nozzles with the presence and absence of agricultural adjuvant, and electrostatic application using a spray volume of 200 L ha-1 with and without spray adjuvant. The addition of an adjuvant to the spray did not influence coffee leaf miner control; in contrast, a spray volume of 200 L ha-1 combined with the application of coarse droplets was efficient in the management of insect pest. The reduction in the rate of the application using coarse droplets increased the operational capacity and reduced spray drift potential.

Leaf miner is a major coffee tree pest that causes severe losses. Most farmers perform chemical control in an attempt to reduce damage to the crop. Thus, the current study aims to analyze spraying efficiency and control effectiveness according to the management plan adopted in a coffee farm in the Cerrado of Minas Gerais. The application used Arbus sprayer, model 2,000, at the pressure of 600 kPa. The droplet spectrum was analyzed using water-sensitive paper. The plant architecture influence on the application quality was evaluated through the droplet spectrum and control efficacy. We conclude that the plant crown height and depth affected application efficiency. Spraying was an effective phytosanitary control of coffee leaf miner, reaching 87.5% control efficiency ratio, considering that infestation was three times higher than that recommended for the applied pesticide at the time of application.

Coffee production faces challenges due to the susceptibility of the crop to insects. Owing to the coffee leaf miner Leucoptera coffeella, which is one of the main insect pests, the technology for the application of phytosanitary measures becomes the main challenge for coffee growers. This study aimed to evaluate the effectiveness of coffee leaf miner control using different methods of application. The study was conducted in an experimental site in the municipality of Monte Carmelo - Minas Gerais. Spray depositions in different positions of the plant canopy and the effective control of coffee leaf miner were evaluated using the following application treatments: a hydropneumatic sprayer with varying spray volumes (200 and 400 L ha-1), two hollow-cone spray nozzles with the presence and absence of agricultural adjuvant, and electrostatic application using a spray volume of 200 L ha-1 with and without spray adjuvant. The addition of an adjuvant to the spray did not influence coffee leaf miner control; in contrast, a spray volume of 200 L ha-1 combined with the application of coarse droplets was efficient in the management of insect pest. The reduction in the rate of the application using coarse droplets increased the operational capacity and reduced spray drift potential. [ABSTRACT FROM AUTHOR]

ABSTRACT The coffee crop requires great spray ability to penetrate into the plant canopy during the application of pesticides. The aim of this study was to evaluate the spray deposition on leaves of coffee plants and the chemical control of the leaf miner provided by the application of different spray nozzles, with and without the use of an auxiliary boom. The deposition on the upper, middle and lower parts of the plants and the losses to the soil were evaluated using a tracer quantified by spectrophotometer. We also evaluated the chemical control of the leaf miner, counting the larvae, after the application of the insecticides cartap and fenpropathrin. The trial was carried out in a randomized block design, with four replications, in a factorial model (2×2+1): with and without the auxiliary boom, three spray nozzles (hollow cone – MAG 02, flat fan – AD 11002 and air induction hollow cone – TVI 8002) and a control. The use of the auxiliary boom increased the deposition in the lower part of the plants but decreased in the middle part, besides the increase in the runoff. The different nozzles do not provided differences in the deposition, showing the technical feasibility in the use of the hollow cone nozzles with coarse droplets and flat fan. All the chemical control decreased the leaf miner population, without differentiation.

Brazilian arabica coffee production is based on a set of highly productive cultivars sexually propagated. All of them are susceptible to coffee leaf miner, Leucoptera coffeella, and most of them are also susceptible to coffee leaf rust, Hemileia vastatrix. Aiming to reduce the selection process of cultivars with multiple resistances to both biotic constraints, the Coffea arabica clones 760, 1059, 1064 and 1215 were evaluated under field conditions. The mean yield of four C. arabica clones was not statistically different from the experimental controls; however, the yield of clone 1059 was 21% higher than the control cultivar Obatã IAC 1669-20. Leaf miner resistance level in all the evaluated clones remained the same as the mother plant’s. However, the same did not occur for resistance to H. vastatrix, due to the emergence of new pathogen races. In summary, it is potentially feasible the adoption of clone 1059 as a new clonal cultivar resistant to coffee leaf miner.

ABSTRACT: The infestation of coffee leaf miner and its predation by wasps were assessed in commercial organic coffee plantings shaded with leucaena or intercropped with pigeon pea. Plantings in northern Paraná were assessed every two weeks from June 2011 to December 2012. The percent of infested leaves, number of mines per leaf and the percent of predation by wasps (indicated by lacerations) were determined. Fewer coffee leaf miners and fewer mines per leaf were found in pigeon pea intercropped plantings (28.4% and 0.3, respectively) than in shaded plantings with leucaena (48.1% and 0.8, respectively). More predation by wasps was found in the shaded (27.2%) than in the intercropped (13.2%) plantings. Suggestions for new studies are presented along with suggestions for managing the leaf miner in coffee plantings.

O objetivo deste trabalho foi avaliar o dano foliar provocado pela ocorrência de bicho-mineiro em cafeeiros da espécie arábica, variedade Catuaí Amarelo 493. O estudo foi realizado em junho de 2016, no Instituto Federal do Espírito Santo - Campus de Alegre, ES, Brasil. Foram coletadas um par de folhas no terço superior, terço médio e terço inferior, em 25 plantas de café, escolhidas de forma aleatória. Essas amostras foram levadas até o Laboratório de Geoprocessamento e foram fotografadas, em seguida, realizadas as seguintes medições: Área total e perímetro das folhas, e área afetada pelo ataque do bicho-mineiro. Em média, as áreas afetadas foram de 5,8 cm² (terço superior), 3,6 cm² (terço médio) e 10,6 cm² (terço inferior). O ataque pelo bicho mineiro corresponde a 23,83% no terço superior, 25,68% no terço médio e 30,68% no inferior das plantas da lavoura.

The objective of this work was to evaluate the toxicity of organic farming‑compatible products to the coffee leaf miner Leucoptera coffeella. Lime sulphur, enriched Bordeaux mixture (Viça Café Plus), and the "supermagro" biofertilizer were first tested in laboratory. The most promising product was tested afterwards under field conditions. In laboratory, different concentrations of each product were applied on L. coffeella eggs and on infested coffee‑mined leaves. Only lime sulphur had ovicidal effects at an acceptable concentration (1.6%) for field applications, but no significant effect on larvae mortality was found. Enriched Bordeaux mixture and the "supermagro" biofertilizer had no effect on L. coffeella eggs and larvae. In the field trial, biweekly or monthly sprayings of lime sulphur at different concentrations caused population decrease after 30 days; however, this effect was not significant after 60 or 90 days.

Aiming to contribute to the development of alternative control methods of the coffee leaf miner, Leucoptera coffeella (Guérin-Mèneville & Perrottet, 1842) (Lepidoptera: Lyonetiidae), a search for plants able to produce active substances against this insect was carried out, with species collected during different periods of time in the Alto Rio Grande region, (Lavras, Minas Gerais, Brazil). Coffee leaves containing L. coffeella mines were joined with 106 extracts from 77 plant species and, after 48 hours, the dead and alive caterpillars were counted. The extracts from Achillea millefolium, Citrus limon, Glechoma hederacea, Malva sylvestris, Mangifera indica, Mentha spicata, Mirabilis jalapa, Musa sapientum, Ocimum basiculum, Petiveria alliaceae, Porophyllum ruderale, Psidium guajava, Rosmarinus officinalis, Roupala montana, Sambucus nigra and Tropaeolum majus showed the highest mortality rates.

The objective of this work was to evaluate the effect of different drip irrigation regimes and planting densities on the incidence of the leaf miner, Leucoptera coffeella, in arabica coffee plants for one year. The experiment was carried out in 2008, in a complete randomized block design, in a split-plot in time arrangement, with four replicates. The treatments consisted of four drip irrigation regimes - soil water balance, irrigations at 20 and 60 kPa soil tensions, and a nonirrigated treatment -, which were distributed at three plant densities: 2, 500, 5, 000, and 10, 000 plants per hectare. The evaluations were made on a monthly basis between January and December 2008. The highest pest occurrence period was from August to November, a season with low-air relative humidity preceded by a drought period. Irrigated coffee plants showed an incidence of intact mines 2.2 times lower than that of nonirrigated plants. Irrigation and increasing of plant density contribute to the reduction of coffee leaf miner occurrence.

The objective of this work was to analyze coffee (Coffea arabica) genotypes resistant to the coffee leaf miner (Leucoptera coffeella) using microsatellite markers. Sixty-six loci were evaluated, of which 63 were obtained from the Brazilian Coffee Expressed Sequence Tag (EST) database. These loci were amplified in bulks of individuals from F5 progenies of 'Siriema' (C. arabica x C. racemosa) resistant and susceptible to the insect, in eight samples of C. racemosa, and in a F6 population of 'Siriema' with 91 individuals segregating for resistance to the leaf miner. Polymorphisms were verified for two simple sequence repeat (SSR) loci in bulks of the susceptible progenies. The two polymorphic alleles were present in around 70% of the susceptible genotypes in F5 and in approximately 90% of the susceptible individuals in F6. However, the polymorphic EST-SSR markers among populations contrasting for resistance to leaf miner were not correlated to the evaluated characteristics. SSR markers show inter- and intraspecific polymorphism in C. arabica and C. racemosa.

Objetivou-se, com o presente trabalho, verificar o efeito do extrato metanólico de Coffea racemosa Lour., espécie silvestre de cafeeiro portadora de resistência a uma das principais pragas da cultura cafeeira, o bicho-mineiro. Inicialmente, o extrato metanólico das folhas de C. racemosa foi pulverizado em mudas de cafeeiro Coffea arabica cv. Catuaí, em casa-de-vegetação, as quais foram expostas a adultos de L. coffeella para o teste de oviposição com chance de escolha. Observou-se que as plantas tratadas foram menos preferidas para oviposição. A seguir, realizou-se o teste de efeito ovicida, no qual mudas de C. arabica contendo ovos do bicho-mineiro foram pulverizadas com o mesmo extrato. Os experimentos foram conduzidos em delineamento inteiramente casualizado com cinco repetições. Verificou-se redução significativa na eclosão de larvas, e segundo análise por microscopia eletrônica de varredura, os ovos não viáveis apresentaram anomalias nas superfícies externas. Entretanto, os adultos provenientes de ovos tratados não foram afetados na percentagem de emergência, razão sexual e oviposição. Os resultados obtidos demonstram que C. racemosa apresenta-se como uma alternativa promissora ao controle do bicho-mineiro, necessitando, no entanto, de novos estudos para isolamento e identificação das substâncias bioativas.The objective of this study was to evaluate the effect of Coffea racemosa Lour. methanolic extract , a wild coffee specie that presents resistance against the coffee leaf-miner, a major pest of coffee crop. The methanol extract of C. racemosa leaves was sprayed on coffee seedlings, Coffea arabica cv. Catuaí, in greenhouse, which were exposed to adults of L. coffeella for oviposition in free choice test. Plants treated were less preferred for oviposition. An ovicidal assay was carried out by spraying the methanol extract on C. arabica seedlings containing L. coffeela eggs on their leaves. The experiments were conducted in a completely randomized design with five replicates. Larvae hatching out from treated eggs were significantly reduced by the extract and non viable eggs presented some anomalies on their external surfaces according to analyses by scanning electron microscopy. However, the adults derived from treated eggs did not have the percentage of emergence, sex ratio and oviposition affected by the treatment. The results show that C. racemosa presents as a promising alternative to control the coffee leaf-miner, being necessary new studies to isolate and caracterize the bioactive substances against this pest.

The coffee production is an economic mainstay for many countries in the world. Brazil is the world's largest producer and exporter of coffee, being responsible for about 25% of the world production. It is well known that coffee plantations are susceptible to more than 850 fungal and insect pests. Among these pests, the most important significant throughout Brazil is the coffee-leaf-miner, [Leucoptera coffeella (Guérin-Mèneville & Perrottet, 1842) (Lepidoptera: Lyonetiidae)]. It is estimated that the loss in yield due to infestation by L. coffeella moths can increase to as much as 80% in areas where the coffee-leaf-miner larvae are not controlled. Though it is effective, the chemical control of the pest substantially increases the cost of production and constitutes a risk to the environment. The knowledge about the spatial-temporal dynamics of coffee-leaf-miner and predatory wasps may provide valuable information about the biological management pest context. One way to investigate the spatial-temporal synchronism of predator and prey is to calculate and compare an index of spatial randomness within a sequence time. This paper advocates using the Morisita's index, coupled with the bootstrap method, in a temporal sequence to characterize the spatial- temporal dynamics of coffee-leaf-miner and predatory wasp (Hymenoptera: Vespidae) in one hectare of an organic coffee plantation. The results showed that coffee-leaf-miner and predatory wasps presented a seasonal behavior with a temporal synchronism. The results also showed that both new and preyed mines were aggregated during the peak population (dry season). There was little evidence for space-time interaction between coffee-leaf-miner and predatory wasps.A produção do café é, economicamente, muito importante para vários países do mundo. O Brasil é o maior produtor e exportador de café do mundo, sendo responsável por, aproximadamente, 25% da produção mundial. Sabe-se que as plantações de café são suscetíveis ao ataque de mais de 850 tipos de fungos e insetos. Entre essas pragas, a mais importante em todo Brasil é o bicho mineiro do cafeeiro [Leucoptera coffeella (Guérin-Mèneville & Perrottet, 1842) (Lepidoptera: Lyonetiidae)]. Estima-se que a perda no rendimento devido à infestação do bicho mineiro pode chegar a 80% em plantações onde não existe controle da praga. Embora o controle químico seja eficaz para combatê-la, ele acarreta um aumento no custo da produção e constitui um risco para o meio ambiente. O conhecimento da dinâmica espacial-temporal do bicho-mineiro e da vespa predadora (Hymenoptera: Vespidae) pode fornecer informações valiosas para o controle biológico da praga. Uma maneira de investigar o sincronismo espacial-temporal entre presa e predador é calcular algum índice para análise de completa aleatoriedade espacial e compará-lo ao longo do tempo. Este artigo sugere o uso do índice de Morisita, juntamente com o método bootstrap, em uma sequência temporal para caracterizar a dinâmica espacial-temporal do bicho-mineiro e da vespa predadora em um hectare de uma plantação orgânica de café. Os resultados revelaram que o bicho-mineiro e a vespa predadora apresentaram um comportamento sazonal com um sincronismo temporal. Além disso, observou-se que minas novas e predadas apresentavam agrupamentos durante a estação mais seca do ano. Existem poucas evidências de interação espaço-temporal entre minas predadas e vespas predadoras.

The objective of this work was to assess the potential impact of climate change on the spatial distribution of coffee nematodes (races of Meloidogyne incognita) and leaf miner (Leucoptera coffeella), using a Geographic Information System. Assessment of the impacts of climate change on pest infestations and disease epidemics in crops is needed as a basis for revising management practices to minimize crop losses as climatic conditions shift. Future scenarios focused on the decades of the 2020's, 2050's, and 2080's (scenarios A2 and B2) were obtained from five General Circulation Models available on Data Distribution Centre from Intergovernmental Panel on Climate Change. Geographic distribution maps were prepared using models to predict the number of generations of the nematodes and leaf miner. Maps obtained in scenario A2 allowed prediction of an increased infestation of the nematode and of the pest, due to greater number of generations per month, than occurred under the climatological normal from 1961-1990. The number of generations also increased in the B2 scenario, but was lower than in the A2 scenario for both organisms.O objetivo deste trabalho foi avaliar os impactos potenciais da mudança climática sobre a distribuição espacial de nematóides (raças de Meloidogyne incognita) e do bicho-mineiro (Leucoptera coffeella) da cultura de café, com uso de Sistema de Informações Geográficas. O conhecimento dos impactos da mudança climática na ocorrência de pragas e doenças é de grande importância para o setor agrícola, pois permite a elaboração de estratégias para minimizar prejuízos futuros. Cenários futuros centrados nas décadas de 2020, 2050 e 2080 (cenários A2 e B2), foram obtidos a partir de cinco Modelos Climáticos Globais, disponibilizados pelo Centro de Distribuição de Dados do Painel Intergovernamental sobre Mudança do Clima. Por meio de modelos para previsão do número de gerações anuais do nematóide e do bicho-mineiro, foram elaborados mapas de distribuição geográfica. Os mapas obtidos no cenário A2 permitem verificar que poderá haver aumento na infestação, tanto do nematóide quanto da praga, pelo maior número de gerações por mês, quando se compara com a normal climatológica de 1961-1990. No cenário B2, o número de gerações também foi maior que no cenário atual, porém nota-se que é inferior ao observando no cenário A2.

The objective of this work was to identify expressed simple sequence repeats (SSR) markers associated to leaf miner resistance in coffee progenies. Identification of SSR markers was accomplished by directed searches on the Brazilian Coffee Expressed Sequence Tags (EST) database. Sequence analysis of 32 selected SSR loci showed that 65% repeats are of tetra-, 21% of tri- and 14% of dinucleotides. Also, expressed SSR are localized frequently in the 5'-UTR of gene transcript. Moreover, most of the genes containing SSR are associated with defense mechanisms. Polymorphisms were analyzed in progenies segregating for resistance to the leaf miner and corresponding to advanced generations of a Coffea arabica x Coffea racemosa hybrid. Frequency of SSR alleles was 2.1 per locus. However, no polymorphism associated with leaf miner resistance was identified. These results suggest that marker-assisted selection in coffee breeding should be performed on the initial cross, in which genetic variability is still significant.O objetivo deste trabalho foi identificar marcadores do tipo microssatélite (SSR) associados à resistência ao bicho-mineiro. A identificação dos marcadores foi feita por meio de buscas dirigidas no banco de seqüências expressas (EST) de café. Foram selecionadas 32 sequências microssatélites expressas em folhas e relacionadas a mecanismos de defesa. Os locos SSR avaliados apresentaram, em sua maioria, repetições mais complexas, com: 65% das repetições de tetranucleotídeos, 21% de trinucleotídeos e 14% de dinucleotídeos. Os locos avaliados estão freqüentemente localizados na região 5'-UTR do gene e envolvidos com mecanismos de defesa. Polimorfismos foram avaliados em progênies avançadas de cruzamentos entre Coffea racemosa x Coffea arabica, segregantes para resistência ao bicho-mineiro. Destes 32 oligonucleotídeos, 29 amplificaram os locos SSR esperados, e o número médio de alelos por loco foi de 2,1. Não foram identificados alelos polimórficos associados à resistência ao bicho-mineiro. Esses resultados sugerem que a seleção assistida por marcadores, em café, deve ser realizada em cruzamentos iniciais, nos quais a variabilidade genética é maior.

Brazilian arabica coffee production is based on a set of highly productive cultivars sexually propagated. All of them are susceptible to coffee leaf miner, Leucoptera coffeella, and most of them are also susceptible to coffee leaf rust, Hemileia vastatrix. Aiming to reduce the selection process of cultivars with multiple resistances to both biotic constraints, the Coffea arabica clones 760, 1059, 1064 and 1215 were evaluated under field conditions. The mean yield of four C. arabica clones was not statistically different from the experimental controls; however, the yield of clone 1059 was 21% higher than the control cultivar Obatã IAC 1669-20. Leaf miner resistance level in all the evaluated clones remained the same as the mother plant's. However, the same did not occur for resistance to H. vastatrix, due to the emergence of new pathogen races. In summary, it is potentially feasible the adoption of clone 1059 as a new clonal cultivar resistant to coffee leaf miner. [ABSTRACT FROM AUTHOR]

Highlights: [•] Indirect plant defence provided by extrafloral nectaries affects neighbour plants. [•] Nectar availability of Inga trees increased parasitism rate of coffee leafminer. [•] Nectar availability of Inga trees decreased coffee damage. [•] Inga trees might be a good strategy to enhance natural pest control in coffee crops. [ABSTRACT FROM AUTHOR]

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The coffee leaf-miner (CLM) ( Leucoptera coffeella Guérin-Mèneville; Lepidoptera: Lyonetiidae), the main pest of coffee plants, occurs widely throughout the Neotropics where it has a significant, negative economic and quantitative impact on coffee production. This study was conducted in a rubber tree/coffee plant interface that was influenced by the trees to a varying degrees depending on the location of the coffee plants, i.e. from beneath the rubber trees, extending through a range of distances from the edge of the tree plantation to end in a coffee monocrop field. The most severe damage inflicted on coffee plants by the CLM (number of mined leaves) from April, which marks the start of the water deficit period, until September 2003 was in the zone close to the rubber trees, whereas the damage inflicted on plants in the monocropped field was comparable to that on coffee plants grown directly beneath the rubber trees, which received about 25–40 % of the available irradiance (I r—available irradiation at a certain position divided by the irradiation received in full sunlight, i.e. in the monocrop). From May until July damage caused by the CLM nearly doubled in each month. In midwinter (July), the damage decreased perceptibly from the tree edge toward the open field. From September onward, with the rising air temperatures CLM damage in the coffee monocrop started to increase. Based on these results, we conclude that coffee plants grown in the full sun incurred the most damage only at the end of winter, with warming air temperatures. Coffee plants grown in shadier locations (25–40 % I r) were less damaged by the CLM, although a higher proportion of their leaves were mined. The rubber trees probably acted as a shelter during the cold autumn and winter seasons, leading to greater CLM damage over a distance outside the rubber tree plantation that was about equal to the height of the trees. Future studies should attempt to relate leaf hydric potential to pest attack in field conditions. More rigorous measurements of shade conditions could improve our understanding of the relationship of this factor to CLM attack. [ABSTRACT FROM AUTHOR]