Jhersyka da Silva Paes

Pesticide resistance in agricultural pests has become a growing concern, as many species have developed resistance to most commercially available insecticides. Phthorimaea absoluta Meyrick, the tomato leaf miner, is one of the most destructive pests of tomato crops, capable of causing severe damage and even complete yield loss. This species has shown high levels of resistance to various insecticides, complicating effective pest management. Chlorfenapyr is a pro-insecticide that disrupts mitochondrial ATP production, ultimately leading to insect death. This study evaluated the lethal, sublethal, and histopathological effects of a chlorfenapyr-based insecticide on P. absoluta larvae through oral exposure. Lethal concentrations were determined using a concentration–mortality bioassay, and the LC ₅₀ value (3.72 ppm) was applied to assess histopathological alterations in the midgut. Chlorfenapyr-based insecticide exhibited high toxicity against P. absoluta . Histopathological and histochemical analyses revealed midgut epithelial damage, morphological alterations associated with cell degeneration, as well as the elimination of mycetocytes, which may play roles in digestion and xenobiotic detoxification. These findings provide one of the first histopathological characterizations of pesticide effects in this species and in microlepidoptera more broadly, reinforcing the potential use of chlorfenapyr within integrated pest management strategies.

For pest control to be sustainable, the methods applied must be efficient and have a low environmental impact. Pest control failures bring economic and environmental problems. Phthorimaea absoluta is the main pest in tomato crops worldwide. Benzoylureas, diamides, and pyrethroids are among the insecticides with the highest reports of pest control failures, and Brazil is the country where this has been most observed. Machine learning models are suitable for predicting biological events. Thus, this study aimed to determine the risks of failures in the control of P. absoluta by insecticides in Brazilian biomes using the MaxEnt machine learning algorithm. The risks of pest control failures by benzoylureas and pyrethroids were higher in tomato crops located in the Cerrado and Atlantic Forest biomes, and annual precipitation was the critical variable associated with these failures. The risks of control failures by diamides were higher in crops located in the Caatinga, Cerrado, and Atlantic Forest, and temperature seasonality was the critical variable associated with these failures. In conclusion, the models determined in the study are robust to predict the regions with higher risks of P. absoluta control failures by insecticides, and they indicated the environmental variables associated with these risks.

Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a primary pest in essential crops for food security, such as tomato, potato, and soybean. Soybean (Glycine max) (L) (Merr) is the leading source of animal protein in the world. B. tabaci remains a relevant pest in soybean crops; on average, one whitefly per sample yields 30 kg. ha⁻¹ of losses. Determining the seasonal dynamic of B. tabaci is helpful in controlling the pest in time, avoiding losses. This investigation aims to model the seasonal dynamics of B. tabaci in soybean crops through neural networks. This research tracked whitefly density, climatic elements and soybean age in 100 soybean fields in the Brazilian Cerrado to build seasonal dynamic models along 2 years. Features were selected according to correlation analysis and biological meaning. ANNs structures were investigated to forecast whitefly through the years and the model presenting the highest Pearson correlation and lowest root mean square error were chosen. Feature importance was analyzed to examine these attributes’ effect on B. tabaci. Then, the model was validated by comparing whitefly observed and fit data densities during the study. The ANN selected has five entries (soybean age, average temperature, rainfall, wind speed, and atmosphere pressure) and four neurons in the hidden shell. Average temperature and wind speed are key features in the model presenting the most elevated relative importance index to predict whitefly adult population. Therefore, this study highlighted artificial intelligence’s power in modelling a key pest’s seasonal dynamic upon a range of attributes seven days in advance.

The cashew tree (Anacardium occidentale) is a tropical plant that yields the world's most consumed nut. Anacampsis phytomiella (Lepidoptera: Gelechiidae) is the primary pest that infests cashew nuts, leading to losses of up to 80%. The comprehension of the spatiotemporal dynamics of pests enables enhanced planning of sampling and control methods. Artificial neural networks (ANNs) are computational models in machine learning with high predictive capability. Thus, the objective of this study was to determine a seasonal dynamic model of A. phytomiella using ANNs. Over 3 years, the pest attack intensity and climatic elements were monitored in two cashew orchards. A total of 1716 ANN models were determined. The model predictors included the time of plant fruiting, average air temperature, dew point, atmospheric pressure and rainfall. The temperature, atmospheric pressure and time of plant fruiting had a positive effect on pest infestation, while the opposite occurred with rainfall. The pest infestation curve in relation to the dew point exhibited a point of maximum. The model successfully predicted the intensity of A. phytomiella infestation across different years, plant fruiting stages and pest densities. Therefore, the ANN model determined in this study is promising for predicting the intensity of A. phytomiella infestation in cashew orchards.

Biological control is a growing branch in Brazil, and with it comes the need to obtain efficient enemies in the control of pests. The use of entomopathogens has expanded and with it comes the need to investigate their selectivity to non-target organisms. The present work was conducted in a pinecone (Annona squamosa L.) orchard with the objective of evaluating in which locations of the plant (lower, middle or upper third) and the effects of the entomopathogens: Beauveria bassiana, Metharhizium anisoplie and Bacillus thuringiensis on the predatory mite. Sixteen weekly applications were made between March and August 2017 with a constant pressure knapsack sprayer, nozzle type JA-2 with the jet directed at the entire plant. The treatments consisted of the use of three entomopathogenic biological products: Dipel, Beuaveria bassiana JCO and Metarhizium anisopliae JCO applied alone and in combination. Eight treatments were performed, as follows: T1-Spraying with M. anisopliae + phfos (Quimifol) + adhesive spreader (Agral), T2-Spraying with B. bassiana + phfos (Quimifol) + adhesive spreader (Agral), T3-Spraying with B. thruringiensis + phfos (Quimifol) + adhesive spreader (Agral), T4-Spraying with M. anisopliae + B. basisiana + phfos (Quimifol) + adhesive spreader (Agral), T5- Spraying with M. anisopliae + B. thruringiensis + phfos (Quimifol) + adhesive spreader (Agral), T6- Spraying with B. bassiana + B. thruringiensis + phfos (Quimifol) + adhesive spreader (Agral); T7- Spraying with M. anisopliae + B. bassiana + B. thruringiensis + phfos (Quimifol) + adhesive spreader (Agral) e T8-Testify, spraying with phfos (Quimifol) + adhesive spreader (Agral). The results indicate a preference of the Neoseiulus sp. mite for the middle third of the plant canopy. The results obtained indicated that the entomopathogens influenced the population of the Neoseiulus sp. mite until the sixth week after treatment, where the combination of Metarhizium anisopliae + Beauveria bassiana + Bacillus thuringiensis was the one that most negatively affected the population of the predatory mite Neoseiulus sp.

Coffee is considered one of the most important commercial commodities globally, and in 2020, it moved to a global market of USD 102.02 billion. However, the attack of pests in coffee production can cause significant economic losses. Leucoptera coffeella is a critical pest in coffee-producing countries, with productivity losses reaching 87%. The knowledge of the spatial distribution patterns of L. coffeella is essential to developing an efficient sampling and control plan. Moreover, it allows us to target for control specific locations/seasons where L. coffeella occurrence is at its highest density before reaching the economic injury level. Therefore, our objective in this study was to determine the spatial distribution of L. coffeella in coffee crops through geostatistical analysis. Data on the population density of L. coffeella were collected over four years on a farm with 18 center pivots located in the Brazilian Cerrado. The presence of L. coffeella was recorded in all 18 pivots during the entire time of the study (2016 to 2020). The highest densities were from July to November. These high densities of L. coffeella positively correlated with maximum air temperatures and wind speed. It was also verified to negatively correlate with minimum air temperatures and rainfall. The surrounding vegetation does not affect the pest densities. The pest hotspots appeared in different pivots and different locations inside pivots. Furthermore, L. coffeella showed an aggregated distribution pattern. For three years, the colonization started at the edge of the crop. The sampling should be performed equidistant as the pest is distributed equally in all directions. The information found in this study provides valuable information to initiate timely management and control methods in coffee crops with a high incidence of L. coffeella, thus reducing production costs and the harmful effects of pesticide use.