Significance: Integrated pest management (IPM) has been the dominant crop protection paradigm promoted globally since the 1960s. However, its adoption by developing country farmers is surprisingly low. This article reports 51 potential reasons why, identified and prioritized by hundreds of IPM professionals and practitioners around the world. Stakeholders from developing countries prioritized different adoption obstacles than those from high-income countries. Surprisingly, a few of the obstacles prioritized in developing countries appear to be overlooked by the literature. We suggest that a more vigorous analysis and discussion of the factors discouraging IPM adoption in developing countries may accelerate the progress needed to bring about its full potential.

The study of insect responses to temperature has a long tradition in science, starting from Réaumur's work on caterpillars in the 18th century. In 1932, Ernst Janisch wrote: ‘The problem is (and will be more and more in the future) one of the most important ones in entomology […]’. Almost 90 years after this paper, its prediction still holds true, with a sustained interest of the scientific community for the study of insect responses to temperature, especially in the context of climate change. We present a review of the major developments in the field of insect development responses to temperature and analyze the growing importance of modeling approaches in the literature using a bibliographic analysis. We discuss recent advances and future directions for phenology‐modeling based on temperature‐dependent development rate. Finally, we highlight the need for a change of paradigm toward a system‐based approach in order to overcome current challenges and to predict insect phenology more accurately, with direct implications in agriculture, conservation biology, and epidemiology.

A large body of literature has recently recognized the role of microclimates in controlling the physiology and ecology of species, yet the relevance of fine-scale climatic data for modeling species performance and distribution remains a matter of debate. Using a 6-year monitoring of three potato moth species, major crop pests in the tropical Andes, we asked whether the spatiotemporal resolution of temperature data affect the predictions of models of moth performance and distribution. For this, we used three different climatic data sets: i) the WorldClim dataset (global dataset), ii) air temperature recorded using data loggers (weather station dataset), and iii) air crop canopy temperature (microclimate dataset). We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250m radius disks for the microclimate dataset). Then, we computed pest performances based on these three datasets. Results for temperature ranging from 9 to 11ºC revealed discrepancies in the simulation outputs in both survival and development rates depending on the spatiotemporal resolution of the temperature dataset. Temperature and simulated pest performances were then combined into multiple linear regression models to compare predicted vs. field data. We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales. Results showed that the model implemented with microclimatic data best predicted observed pest abundances for our study sites, but was less accurate than the global dataset model when performed at larger scales. Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances. In conclusion, keeping in mind that the mismatch between the size of organisms and the scale at which climate data are collected and modeled remains a key issue, temperature dataset selection should be balanced by the desired output spatiotemporal scale for better predicting pest dynamics and developing efficient pest management strategies.

Thermal biologists are interested in generating paired data on development rates and temperatures, but they are in need of automated tools to accurately analyse this data. Fed with a database of more than 30 existing development models, the devRate package provides tools for plotting and visualizing data, selecting the most appropriate model, and mapping and forecasting ectotherm phenology using temperature time‐series data. Most proposed models are for arthropods, mainly insects (e.g. Lepidoptera, Coleoptera, Hemiptera, Diptera, Hymenoptera). This paper presents the main functions of the package and provides an example using an empirical dataset. The devRate package provides investigators with a unique set of tools and a database that allows them to fit development rate models, and to compare arthropod response to temperature.