To investigate how to accurately identify bee species using their sounds, we conducted acoustic analysis to identify three pollinating bee species (Apis mellifera , Bombus ardens , Tetralonia nipponensis ) and a hornet (Vespa simillima xanthoptera ) by their flight sounds. Sounds of the insects and their environment (background noises and birdsong) were recorded in the field. The use of fundamental frequency and mel-frequency cepstral coefficients to describe feature values of the sounds, and supported vector machines to classify the sounds, correctly distinguished sound samples from environmental sounds with high recalls and precision (0.96-1.00). At the species level, our approach could classify the insect species with relatively high recalls and precisions (0.7-1.0). The flight sounds of V.s. xanthoptera , in particular, were perfectly identified (precision and recall 1.0). Our results suggest that insect flight sounds are potentially useful for detecting bees and quantifying their activity. species classification / Hymenoptera / machine learning / acoustic analysis

No abstract

No abstract

Predicting certain crop phenological stages is important for scheduling agricultural practices and predicting crop responses to climate change. In this study, we developed three different wheat phenological models, a polynomial model and two sigmoid and exponential mixed SEM models developed by different parameter determination methods the Nelder-Mead and augmented Lagrange multiplier methods , and determined which of these models is the most effective for predicting the flowering date in wheat. Five winter wheat cultivars were cropped in western Japan for four years; we split the cultivation data for model calibration and validation. The SEM models showed higher precision in root mean square error RMSE; 3-5 days than the polynomial model when using the validation data. The models developed using the Nelder-Mead and augmented Lagrange multiplier methods showed similar RMSE values Mean SD: 4.24 0.59 and 4.16 0.36, respectively . On the other hand, in the context of validity, the model developed using the Nelder-Mead method showed an unnatural development response to changes in environmental variables; thus, we found that the model developed using the augmented Lagrange multiplier method would be more realistic and effective to express the response of wheat growth to environmental factors. The results of our study shed new light on the optimization methods used in crop development models and on the advantages of using the augmented Lagrange multiplier method for determining the parameters of a non-linear crop development model.

The simultaneous wing movement by multiple worker bees in a colony produces a hissing sound, which is a novel acoustic and vibrational signal of the honey bees. Hissing of honey bees is thought to be a response to direct, threatening stimuli. However, we discovered Japanese honey bees (Apis cerana japonica ) can hiss even without obvious disturbances in previous study. In this study, to understand the temporal characteristics of honey bee hissing, we conducted 24-h sound recordings over 7 months in 2015 and investigated when A. cerana japonica hissed every day. Additionally, we also investigated the relationship of hissing onset and offset times with sunrise and sunset times, and environmental factors. We found that honey bees hiss daily during daytime and most frequently at dawn, with hissing onset/offset occurring mostly within 30 min of sunrise/sunset time. Hissing onset and offset were significantly related to sunrise and sunset times, respectively, and also to solar radiation intensity. The findings reveal that A. cerana japonica hissing has unique temporal patterns, and also shed a new light on vibrational collective behavior in honey bees. acoustic/vibrational behavior / Apis cerana japonica / hissing / honey bees / shimmering

Wheat is one of the world's most important crops, and its phenological model is useful for scheduling agricultural practices such as fungicide or fertilizer application. Although various wheat phenological models have been developed throughout the world, a conventional modelmostly used for Japanese cultivarsis one wherein temperature and daylength responses are expressed as sigmoidal and exponential functions that do not have a vernalization function. Since a gradual rise in daily development rate is expressed as an increase in mean temperature in the conventional model, the model may potentially miscalculate the wheat development when used to predict the phenology of a cultivar with a strong vernalization requirement. In this study, we proposed a modified model that combines the conventional model and a vernalization function that expressed the daily vernalization rate using an inverse sigmoid function. Cultivation data for five winter wheat cultivars with relatively strong vernalization requirement were collected for several years more than 4 years , and the model for flowering date prediction was calibrated based on the sowing date for each cultivar. Six-fold cross-validation was conducted to calibrate and validate the models. We found that the proposed model predicted the flowering date of the wheat cultivars more accurately in the median of root mean square error RMSE: 1 -2 days than the conventional model RMSE: 2 -5 days . Although the accuracy of the model varies with the cultivar, our results indicated the advantage of using the proposed model compared with that of using the conventional model for describing winter wheat phenology. These findings can contribute to further studies on the crop models of winter wheat and would be an example of combining the vernalization function expressed by an inverse sigmoidal function with the crop model.

BACKGROUND Developing a model that adequately explains pest population dynamics based on weather‐related parameters is fundamentally important for proper pest management. Autocorrelation with past occurrences should be considered when modeling the relationship between the time series of pest occurrence data and meteorological factors; however, few attempts have been made to model these factors simultaneously. In this study, we constructed an autoregressive integrated moving average with exogenous variables (ARIMAX) model to represent the occurrence of the common cutworm, Spodoptera litura (F.) (Lepidoptera: Noctuidae), a major moth pest species in Asia, using the trap catch data of S. litura recorded approximately every 5 days. The multiple meteorological measurements taken over several past periods before S. litura occurrence were included as explanatory variables to evaluate their lag effects on future occurrences. RESULTS It was suggested that temperature had the most important effect on S. litura occurrences among other meteorological factors (i.e., humidity, wind speed, and precipitation). Especially, higher temperatures during the larval/egg stage seemed to presage a higher moth abundance. When the model was fitted using independent data that were not used for calibrating the model, the model was able to capture trends in increases in the scale of occurrence, particularly after July, when the occurrence rapidly increased. CONCLUSION Past temperature condition, particularly during the larval and egg states, is suggested to be highly important for predicting future S. litura occurrences. The ARIMAX model proposed here will allow preventive measures to be taken, effectively safeguarding food resources against pest outbreaks. © 2022 Society of Chemical Industry.

Temperature is one of the most influential factors in crop phenology and is projected to increase substantially due to climate change. To adapt to climate change, it is necessary to understand how crops develop depending on cultivar choice, sowing time, and growing environment under various warming conditions. To clarify the influence of these interactions, we investigated 216 combinations of the genotype × environment × management interaction of wheat (Triticum aestivum L.) development. These included nine scenarios of constant and seasonal warming conditions, two cultivar types (spring‐type and winter‐type wheat), six sowing dates, and two cultivation environments (relatively cool or warm area), and the change in wheat heading dates was simulated using sigmoid and exponential function–based models. Simulations of growing conditions 1−5 °C above the average temperature during 1981–2010 resulted in the faster growth of spring‐type cultivars, and the earlier the cultivars were sown, the faster they grew to heading stage. The winter‐type cultivars showed a smaller advancement to heading than the spring‐type cultivars, but the shortening period from sowing to heading became greater when grown in a cooler environment if the temperature significantly increased. In addition, increased temperatures in spring induced the advancement of heading more than similar conditions in winter. Our findings indicated that the advancement of the heading date varied greatly depending on the four‐factor combinations. Delaying sowing time and changing to winter‐type cultivars from spring‐type cultivars were thought to be effective adaptations; however, the adaptation strategies should be carefully planned depending on the agricultural management and warming conditions.