AIMSurv: First pan-European harmonized surveillance of Aedes invasive mosquito species of relevance for human vector-borne diseases

Human and animal vector-borne diseases, particularly mosquito-borne diseases, are emerging or re-emerging worldwide. Six Aedes invasive mosquito (AIM) species were introduced to Europe since the 1970s: Aedes aegypti, Ae. albopictus, Ae. japonicus, Ae. koreicus, Ae. atropalpus and Ae. triseriatus. Here, we report the results of AIMSurv2020, the first pan-European surveillance effort for AIMs. Implemented by 42 volunteer teams from 24 countries. And presented in the form of a dataset named “AIMSurv Aedes Invasive Mosquito species harmonized surveillance in Europe. AIM-COST Action. Project ID: CA17108”. AIMSurv2020 harmonizes field surveillance methodologies for sampling different AIMs life stages, frequency and minimum length of sampling period, and data reporting. Data include minimum requirements for sample types and recommended requirements for those teams with more resources. Data are published as a Darwin Core archive in the Global Biodiversity Information Facility- Spain, comprising a core file with 19,130 records (EventID) and an occurrences file with 19,743 records (OccurrenceID). AIM species recorded in AIMSurv2020 were Ae. albopictus, Ae. japonicus and Ae. koreicus, as well as native mosquito species

Broutage du phytoplancton par le zooplancton dans un lac peu profond

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Representation Learning with a Variational Autoencoder for Predicting Nitrogen Requirement in Rice

The scope of this research was to provide rice growers with optimal N-rate recommendations through precision agriculture applications. To achieve this goal, a prediction rice yield model was constructed, based on soil data, remote sensing data (optical and radar), climatic data, and farming practices. The dataset was collected from a rice crop surface of 89.2 ha cultivated continuously for a 5-year period and was analyzed with machine learning (ML) systems. A variational autoencoder (VAE) for reconstructing the input data of the prediction model was applied, resulting in MAE of 0.6 tn/ha, with an average yield for the study fields and period measured at 9.6 tn/ha. VAE learns the original input data representation and transforms them in a latent feature space, so that the anomalies and the discrepancies of the data are reduced. The reconstructed data by VAE provided a more sophisticated and detailed ML model, improving our knowledge about the various correlations between soil, N management parameters, and yield. Both optical and radar imagery and the climatic data were found to be of high importance for the model, as indicated by the application of XAI (explainable artificial intelligence) techniques. The new model was applied in the 2022 rice cultivation in the study fields, resulting in an average yield increase of 4.32% compared to the 5 previous years of experimentation

Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring

The objective of this study was to develop a methodology for mapping olive plantations on a sub-tree scale. For this purpose, multispectral imagery of an almost 60-ha plantation in Greece was acquired with an Unmanned Aerial Vehicle. Objects smaller than the tree crown were produced with image segmentation. Three image features were indicated as optimum for discriminating olive trees from other objects in the plantation, in a rule-based classification algorithm. After limited manual corrections, the final output was validated by an overall accuracy of 93%. The overall processing chain can be considered as suitable for operational olive tree monitoring for potential stresses

Optimization of fertilization recommendation in Greek rice fields using precision agriculture

The objective of this research was to evaluate the correlation between rice yield and soil properties for improving fertilizer recommendations. This was achieved through the application of a new precision agriculture methodology on a large experimental setup of about 115 ha in Chalastra, Greece. The methodology uses multispectral satellite images acquired during the previous cropping season for the delineation of preliminary zones, inside of which representative soil samples are extracted and analyzed and fertilizer recommendations are formulated per zone. The spatial distribution of yield performances was recorded with a yield mapper mounted on the harvester. As a result of the zone-based applications in the 2017 cropping season, the grower realized 15% increase in yield compared to the mean of the previous decade’s yields, while the fertilizer inputs were reduced by 20%. Moreover, it was showed that the nitrogen added with the basal fertilization and soil magnesium were the major contributors of yield differences, whereas phosphorus and potassium needs were covered with the applied fertilization

Embedding a precision agriculture service into a farm management information system - ifarma/PreFer

Today, bridging precision agriculture (PA) with farm management is a high priority. To respond to this challenge, a precision fertilization service for extended crops, namely ‘PreFer’ (developed by 'Ecodevelopment' enterprise), was embedded as a new module in a cloud-based farm management information system (FMIS), namely ‘ifarma’ (developed by 'Agrostis' enterprise). The new PreFer module preserves the full potential of the original service methodology, while taking advantage of all fundamental functionalities of the ifarma platform. PreFer as a service uses a GIS to store and process the farmers’ geodatabases, which are fed from multiple sources, such as soil surveys, satellite data, yield monitors, etc. This GIS is also used to feed the machine learning algorithms of ‘PreFer’ with the required data to produce the prescription maps for both broadcasting and topdressing fertilizations. Then, all tables and maps are transferred from the GIS to the platform upon their production, thus becoming immediately available to the farmers. The ‘ifarma/PreFer' module was tested during the 2022 cultivation season, showing that fully meets farmers' requirements. This work also indicates that synergies like this are more than necessary to create added-value in commercial precision agriculture

Effects of climatic and environmental factors on mosquito population inferred from West Nile virus surveillance in Greece

Abstract Mosquito-borne diseases’ impact on human health is among the most prominent of all communicable diseases. With limited pool of tools to contrast these diseases, public health focus remains preventing mosquito-human contacts. Applying a hierarchical spatio-temporal Bayesian model on West Nile virus (WNV) surveillance data from Greece, we aimed to investigate the impact of climatic and environmental factors on Culex mosquitoes’ population. Our spatio-temporal analysis confirmed climatic factors as major drivers of WNV-transmitting-Culex mosquitoes population dynamics, with temperature and long periods of moderate-to-warm climate having the strongest positive effect on mosquito abundance. Conversely, rainfall, high humidity, and wind showed a negative impact. The results suggest the presence of statistically significant differences in the effect of regional and seasonal characteristics, highlighting the complex interplay between climatic, geographical and environmental factors in the dynamics of mosquito populations. This study may represent a relevant tool to inform public health policymakers in planning preventive measures

West Nile Virus in <i>Culex</i> Mosquitoes in Central Macedonia, Greece, 2022

In 2022, Greece was the second most seriously affected European country in terms of the West Nile virus (WNV), after Italy. Specifically, Central Macedonia was the region with the most reported human cases (81.5%). In the present study, 30,816 female Culex pipiens sensu lato mosquitoes were collected from May to September 2022 in the seven regional units of Central Macedonia; they were then grouped into 690 pools and tested for WNV, while next-generation sequencing was applied to the samples, which showed a cycle threshold of Ct < 30 in a real-time RT-PCR test. WNV was detected in 5.9% of pools, with significant differences in the detection rate among regional units and months. It is of interest that in the Thessaloniki regional unit, where most of the human cases were observed, the virus circulation started earlier, peaked earlier, and lasted longer than in the other regional units. All sequences clustered into the Central European subclade of WNV lineage 2, and the virus strain differed from the initial Greek strain of 2010 by 0.52% and 0.27% at the nucleotide and amino acid levels, respectively. Signature substitutions were present, such as S73P and T157A in the prM and E structural proteins, respectively. The screening of mosquitoes provides useful information for virus circulation in a region with a potential for early warning, while the availability of whole-genome sequences is essential for further studies, including virus evolution

Environmental factors influencing the prevalence of Culex mosquitoes: An ERA-Interim approach.

Mosquitoes of the genus Culex act as vectors for the transmission of West Nile Virus (WNV) infection in humans. The factors which determine the spatial and temporal distribution of WNV in Culex mosquitoes are not well understood. Studies have reported associations between environmental factors, such as temperature, precipitation and the prevalence of West Nile virus in Culex mosquitoes. We investigated the association between environmental factors and the quantitative presence of Culex mosquitoes in a WNV prevalent area to identify environmental factors which may create conditions that favour the proliferation of Culex mosquitoes and thus increase the risk of WNV infection in humans. We also explored climate variability effects. We analysed data on numbers of Culex mosquitoes from 11 traps distributed in a region of Northern Greece over the years 2011 – 2013, a period where WNV infections in humans were notified in Greece including that region. Time series of environmental data of temperature, relative humidity, soil temperature, volumetric soil water layer, wind speed, and precipitation were determined utilising the ECMWF’s (European Centre for Medium-Range Weather Forecasts) -Re-Analysis (ERA-Interim) approach as it is used in atmospheric sciences where observational data are sparse allowing for a homogeneous set of data in time and space. Employing a regression model we identified associations between the above variables and the population of Culex mosquitoes. A clear relationship between the mean value anomalies over the last 30 years, defining climate, of almost all variables and the abundance of Culex could be shown. Similar associations were identified when the mean values of the variables were regressed with the population of Culex mosquitoes of the period 2011-2013. However, these associations found to differ in the case of climate anomalies and absolute values. Utilising the ERA-Interim approach for the assessment of the effects of environmental factors on the abundance of Culex mosquitoes at a regional scale it could be shown that factors other than temperature and precipitation may also affect mosquito abundance. The methodology used to capture climate conditions in a more complete temporal and spatial manner represents a valuable alternative when detailed observations are sparse or lack quality.JRC.H.2-Air and Climat