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This paper continues studies on ground beetles (Carabidae) in differently managed winter wheat (Triticum aestivum) fields in Latvia. The main task of those studies was to assess how different soil tillage regimes (ploughing and non-inverse tillage) and different pre-crops (winter wheat and spring rapeseed (Brassica napus) affect assemblage and biodiversity of ground beetles in winter wheat fields. The research was carried out in the Latvia University of Agriculture Research and Study Farm 'Pēterlauki' (56°30'39.38''N; 23°41'30.15''E) during vegetation season of 2013. The results were compared with the results of similar research carried out at the same place during 2012. Totally 57 ground beetle species were observed in studied fields in 2013. Total species assemblage varied between both consecutive vegetation seasons of the research, however these were minor differences not connected with studied agro-ecological factors. Dominance structure of ground beetle species was significantly different between both vegetation seasons - species which were dominant and subdominant in 2012 became subdominant and dominant one year later, accordingly. Annual effects of soil tillage regime and pre-crop on ground beetle dominance structure also were observed, however some differences were recognized between both vegetation seasons. In case, if weed control was successful, higher ground beetle biodiversity might be observed in ploughed fields pre-cropped with spring rapeseed. Otherwise, significantly higher ground beetle biodiversity may be observed in harrowed soil independently from the pre-crop. [ABSTRACT FROM AUTHOR]

The honey bee ( Apis mellifera ) is the most widely managed pollinator and is vital for crop fertilization. Recently, bee colonies have been suffering high mortality rates, exacerbated by factors such as land-use changes and the use of pesticides. Our work aimed to explore the residues of pesticides in honey-bee-collected pollen and how this contamination was affected by seasonality and the landscape composition. We selected six apiaries from different landscapes in Latvia, and pollen samples were collected during the flowering season (2023). We analyzed 39 samples and found 21 pesticide residues (mainly fungicides) with a frequency of 93 occurrences where the values were above the limit of quantification. The most frequently encountered substances were acetamiprid, boscalid, fluopyram, and prothioconazole. However, the highest concentrations were for dimoxystrobin (44 µg kg -1 ), acetamiprid (37 µg kg -1 ), azoxystrobin (27 µg kg -1 ), prothioconazole (25 µg kg -1 ), and boscalid (15 µg kg -1 ). We then calculated the Pollen Hazard Quotient (PHQ) for each pollen sample. No sample had a PHQ value above the critical value of 50. The highest contamination level was observed in the first half of the vegetation season (the end of May and the beginning of June), but later, it significantly decreased. We did not find any significant influence of landscape composition on pesticide pollution.

Today's enormous amounts of freely available high-resolution satellite imagery provide the demand for effective preprocessing methods. One such preprocessing method needed in many applications utilizing optical satellite imagery from the Landsat and Sentinel-2 archives is mosaicking. Merging hundreds of single scenes into a single satellite data mosaic before conducting analysis such as land cover classification, change detection, or modelling is often a prerequisite. Maintaining the original data structure and preserving metadata for further modelling or classification would be advantageous for many applications. Furthermore, in other applications, e.g., connected to land cover classification creating the mosaic for a specific period matching the phenological state of the phenomena in nature would be beneficial. In addition, supporting in-house and computing centers not directly connected to a specific cloud provider could be a requirement for some institutions or companies. In the current work, we present a method called Geomosaic that meets these criteria and produces analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery.•The method described produces analysis-ready satellite data mosaics.•The satellite data mosaics contain pixel metadata usable for further analysis.•The algorithm is available as an open-source tool coded in Python and can be used on multiple platforms., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)