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.

Corn (Zea mays) is the most widely planted crop in the world. Dalbulus maidis (Hemiptera: Cicadellidae) is currently a primary corn pest. The starting point for the development of pest control decision-making systems is the determination of a conventional sampling plan. Therefore, this study aimed to determine a practical conventional sampling plan for D. maidis in corn crops. Insect density was evaluated in 28 commercial fields. Subsequently, D. maidis densities were sampled from fields ranging from 1 to 100 ha. Insect density conformed to a negative binomial distribution in 89.29% of the fields. The insect densities determined using the sampling plan had a low error rate (up to 15%). Sampling time and costs ranged from 2.06 to 39.45 min/ha and 0.09 to 1.81 USD/ha for fields of 1–100 ha, respectively. These results provide the first precise and representative conventional sampling plan for scouting D. maidis adults grown in corn fields. Therefore, the conventional sampling plan for D. maidis determined in this study is practical and can be incorporated into integrated pest management programs for corn crops owing to its representativeness, precision, speed, and low cost.

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.