BACKGROUND In precision integrated pest management, management tactics are implemented only where and when needed, by identifying the sites where the pest population has reached economic thresholds. Tomato, Solanum lycopersicum (Linn.), is a vegetable cultivated worldwide, but its production is reduced by insect pests such as the whitefly, Bemisia tabaci (Genn.). To improve management, there is a need to understand B. tabaci spatial dynamics in tomato fields, which will elucidate colonization patterns and may improve management of this pest. Thus, this study was conducted to assess the spatial autocorrelation, distribution, and colonization patterns of B. tabaci in 19 commercial tomato fields through the growing season. RESULTS A total of 69 isotropic variograms were fit for B. tabaci. The insect distribution was aggregated with a strong level of spatial dependence. Ranges of spatial dependence varied from 0.53 to 19.05 m and 0.5 to 20 m for adults and nymphs, respectively. Overall, densities of adults and nymphs were higher and reached the economic threshold mainly at the field edges. CONCLUSION Our results suggest a colonization pattern for B. tabaci starting at the edges and spreading inwards in to the tomato fields. This study can improve B. tabaci management in tomato fields, especially scouting and decision‐making to treat fields. Scouting for this pest should be directed to the field edges, with sample points at least 20 m apart from each other for independent insect counts. © 2020 Society of Chemical Industry

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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.