Leucoptera coffeella (Lepidoptera: Lyonetiidae) is one of the main pests in coffee crops. The economic injury level (EIL) is the lowest density of the pest at which economic damages match the costs of control measures. The economic threshold (ET) is the density of the pest at which control measures must be taken so that this population does not reach the EIL. These are the main indices used for pest control decision-making. Control of L. coffeella is carried out by manual, tractor, airplane or drone applications. This work aimed to determine EILs and ETs for L. coffeella as a function of insecticide application technology in conventional and organic Coffea arabica crops. Data were collected over five years in commercial C. arabica crops on seven 100 ha central pivots. The cost of control in organic crops was 16.98% higher than conventional. The decreasing order of control cost was manual > drone > airplane > tractor application. Coffee plants were tolerant to low densities (up to 15% mined leaves) of the pest that caused losses of up to 6.56%. At high pest densities (54.20% mined leaves), losses were high (85.62%). In organic and conventional crops and with the use of different insecticide application technologies, EIL and ET were similar. The EIL and ET were 14% and 11% of mined leaves, respectively. Therefore, these indices can be incorporated in integrated pest management programs in C. arabica crops. The indices determined as a function of insecticide application technology in organic and conventional coffee are important as they serve producers with different technological levels. Additionally, EILs and ETs can contribute to more sustainable production, as control methods will only be employed when the pest density reaches these indices.

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.

The intrinsic rate of natural increase of a population (r_m) has been in focus as a key parameter in entomology and acarology. It is considered especially important in studies of predators that are potential biological control agents of fast-growing pests such as mites, whiteflies and thrips. Life-table experiments under controlled laboratory conditions are standard procedures to estimater_m. However, such experiments are often time consuming and may critically depend on the precise assessment of the developmental time and the fecundity rate early in the reproductive phase. Using selected studies of predatory mites with suitable life-table data, we investigated whether and how measurements of growth rates can be simplified. We propose a new method for estimatingr_mfrom partial life tables, in which the researcher can choose a level of precision based on a stand-in measure of relative error. Based on this choice, the procedure helps the researcher to decide when a life-table experiment can be terminated. Depending on the chosen precision, significant amounts of experimental time can be saved without seriously compromising the reliability of the estimated growth parameter.