Microclimatic and macroclimatic data differ, and microclimatic data are most useful at short time intervals (30‐min scales rather than daily/monthly scales). We developed an end‐to‐end system to acquire such data at a high temporal resolution, transfer them to a server through an Internet of Things network using low‐cost technologies, and make them available with dynamic graphical visualizations and download capabilities. The system has been used for 2 years to monitor environmental variables in contrasting agroecosystems in France, Bolivia and Kenya. It has been proven to be reliable and supports the mismatch between macro‐ and microclimates. This low‐cost Internet of Things system can capture the microclimate in contrasting environments with accuracy comparable to commercial solutions, and great flexibility in data processing. It thus constitutes a possible solution in an academic context and has the potential to be used by a broad audience of scientists interested in capturing environmental variables in real time and at a high temporal frequency.

The quinoa pest Copitarsia incommoda (Walker, Lepidoptera: Noctuidae) is a cause of significant damage, and it is thus critical for Andean countries to have access to phenological models to maintain production and food safety. These models are key components in pest control strategies in the context of global warming and in the development of sustainable production integrating agroecological concepts. Phenological models are mainly based on outlining the relationship between temperature and development rate. In this study, we investigated the combined effect of protein content within the diet (artificial diet; artificial diet with −20% protein; artificial diet with +20% protein; natural quinoa diet) and temperature (12, 16.9, 19.5, 22.7, 24.6°C) as drivers of the development rate. Our study supports the literature, since temperature was found to be the main driver of the development rate. It highlights the significant role played by protein content and its interaction with temperature (significant effects of temperature, diet, and diet:temperature on development time using GLMs for all foraging life stages). We discuss the implications of such drivers of the development rate for implementing and applying phenological models that may benefit from including factors other than temperature. While performance curves such as development rate curves obtained from laboratory experiments are still a useful basis for phenological development, we also discuss the need to take into account the heterogeneity of the insect response to environmental factors. This is critical if pest control practices are to be deployed at the optimal time.

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