The present study describes situation of antimicrobial resistance of animal pathogens and resistance trends in Estonia in years 2006-2009. Bacterial strains isolated during period 2006-2009 were Escherichia coli (E. coli), Enterococcus faecium (E. faecium), Enterococcus faecalis (E. faecalis), collected from healthy pigs faeces as well as from diagnostic submissions of pig samples. Staphylococcus aureus (S. aureus) isolates originated from cows with clinical mastitis and Staphylococcus pseudointermedius (S. pseudointermedius) isolates from dogs with pyoderma or otitis externa. Antimicrobial susceptibility was detected by microdilution method. Normal enteric microflora from health y pigs had resistance against streptomycin, tetracyclin, sulfametoxazol and trimethoprim. E. faecalis and E. faecium were resistant to erythromycin, tetracyclin, streptomycin and kanamycin. Multiresistance occured mainly against kanamycin, streptomycin and tetracyclin. E. coli strains isolated from pathological material showed high resistance to ampicillin, tetracycline, streptomycin, sulphonamides and trimethoprim. Multiresistance was detected between 60–73% during study years. In 2009, one ESBL (extended spectrum betalactamase) producing isolate was observed. S. aureus strains isolated from clinical mastitis samples were mainly penicillin resistant (58–86%). Meticillinresistant S. aureus was not found during the study. In 2009, resistance to lincomycin (30%) and fucidinic acid (22%) was detected. In S. pseudointermedius strains isolated from canine skin samples the prevalence of resistance to penicillin as high as 53–81% was found. Multidrug resistance was relatively stable being 38% in 2006, 29% in 2007 and 25% in 2009. In conclusion, antimicrobial resistance of animal pathogens in Estonia was high. Further improvement of prudent use of antimicrobials and infection control is needed.

Research on identification of Ceutorhynchus spp. on winter oilseed rape in Latvia was done during the vegetation season of 2009 and 2010. Field trials were located in the Research and Study Farm ''Peterlauki'', Jelgava municipality, Latvia (56 deg 32 min 17.38N, 23 deg 43 min 17.65E). Four species of brassica stem weevils were identified: cabbage stem weevil Ceutorhynchuspallidactylus Marsh., syn. C. quadridens Pan., rape winter stem weevil C. picitarsis Gyll., blue stem weevil C. sulcicollis Pay., and C. typhae Herb. One of the most common species in winter oilseed rape was cabbage stem weevil C. pallidactylus. Use of a sweep net for identification of particular species cannot be applied, as in practice no specimens have been collected by this method. One of the most appropriate methods for identification of Ceutorhynchus spp. on winter oilseed rape has been yellow sticky traps and water traps that have been used to collect the greatest number of specimens (24) during the vegetation season. First pests appear during the 2nd decade of April, when the plant reaches 33 BBCH. Pest control using systemic insecticide Proteus 110 OD (tiakloprid 100 g LE-1, deltametrin 10 g LE-1) with a dose of 0.75 L haE-1 was applied when the plant reached 39-44 BBHC. Despite the high population density (2 to 10 insects on 40 plants), significant (p is less than 0.05) decrease in pest damage by 43-51%, when compared to untreated area, was recorded during vegetation seasons of 2009 and 2010. Pest control with a systemic insecticide when the plant is in the stem elongation phase provided a significant (p is less than 0.05) yield increase of 0.5 to 0.7 thaE-1.

The theme of landscape identity becomes actualized beside with the impact of globalization. Landscape identity is closely linked to the Latvian national identity. The concept of identity is multifaceted and touches on a number of scientific areas that currently in Latvia are actively investigating this phenomenon. Landscape investigators admit the multidisciplinary structure of the concept of identity, including in their researches the investigations of historical, visual and associative aspects. Landscape identity is related to detection, identification and definition of landscape elements, because landscape elements are the key to the perception of identity. The method of landscape identity assessment is based on three stages: the assessment of the historic, visual and cognitive elements in formation of landscape identity. The assessment of historic elements in formation of landscape identity is associated with the investigation of historic materials and description of structural elements according to the stages of development. The assessment of visual structural elements of landscape identity is based on the material collected during the field work by filling in the landscape assessment matrix. The assessment of cognitive structural elements of landscape identity is based on the opinion survey, which makes it possible to find out the concealed associative identity. The information obtained in all stages of landscape identity assessment forms the landscape identity model. The aim of the research is to develop the method of landscape identity assessment. The research was carried out at Latvia University of Agriculture in 2010.

The most important part in forest inventory based on remote sensing data is individual tree identification, because only when the tree is identified, we can try to determine its characteristic features. The objective of research was to explore remote sensing methods to determine individual tree position using LiDAR and digital aerial photography in Latvian forest conditions. The study site was a forest in the middle of Latvia – in Jelgava district (56º39’ N, 23º47’ E). Aerial photography camera (ADS 40) and laser scanner (ALS 50 II) were used to capture the data. LiDAR resolution was 9p m2 (500 m altitude). The image data is RGB, NIR and PAN spectrum with 20 cm pixel resolution. Image processing was made using Fourier transform, frequency filtering, and reverse Fourier transform. LiDAR data processing methods was based on canopy height model, Gaussian mask, and local maxima. Field measurements were tree coordinates, species, height, diameter at breast height, crown width and length. Using combined LiDAR and optical imagery data allows detecting at least 63% of all trees and about 85% of the dominant trees.

Wheat crown rot is a harmful disease that can be caused by different pathogens. The control of this disease is complicated because of the diversity of pathogens and an insufficient efficacy of fungicides; therefore, the agronomic practices of wheat production are an important tool for reducing the disease development. The aim of this study was to estimate the incidence of wheat crown rot depending on soil tillage system and on the pre-crop of wheat in the year 2016. The field experiment was carried out at the Research and Study farm ‘Peterlauki’ of the Latvia University of Agriculture in the autumn of 2008. The data obtained in 2016 are analysed in this study: A – soil tillage system: 1 – traditional soil tillage with ploughing at the depth of 22 – 24 cm, 2 – reduced soil tillage with disc harrowing up to the depth of 10 cm; B – pre-crop of winter wheat: 1 – wheat, 2 – oilseed rape, 3 – faba beans. The incidence of crown rot was not influenced by soil tillage system, but the impact of pre-crop was significant (p = 0.006). The level of disease was essentially higher in continuous wheat sowings. The experiments showed that the main causal agents of the disease were Fusarium spp. and Oculimacula spp. The spectrum of pathogens was not dependent on a particular agronomic practice.

Deep learning algorithms are becoming default solution for application in business processes where recognition, identification and automated learning are involved. For human identification, analysis of various features can be applied. Face feature analysis is most popular method for identification of person in various stages of life, including children and infants. The aim of this research was to propose deep learning solution for long-term identification of children in educational institutions. Previously proposed conceptual model for long-term re-identification was enhanced. The enhancements include processing of unexpected persons’ scenarios, knowledge base improvements based on results of supervised and unsupervised learning, implementation of video surveillance zones within educational institutions and object tracking results’ data chaining between multiple logical processes. Object tracking results are the solution we found for long-term identification realization.

Puccinia striiformis is a biotrophic pathogen able to cause broad scale epidemics in wheat growing regions. P. striiformis is genetically highly variable pathogen. New, aggressive genetic lineages, adapted to warm temperatures have been observed in the last decades worldwide. The study aimed to ascertain the structure of genetic lineages of P. striiformis in Latvia. Forty one wheat leaf samples with yellow rust symptoms were collected in 2017–2019. Fenotyping and genotyping methods were used for identification of genetic lineages in Global Rust Reference Center, Denmark. Assessments of leaf diseases on winter wheat differentials – ‘Ambition’, ‘Mariboss’, ‘Moro’, ‘Compair’, ‘Rendezvous’, ‘Spalding Prolific’ and local variety ’Fredis’ were made during the research. Five genetic lineages of P. striiformis – PstS4, PstS7, PstS10, PstS13 and PstS14 were found. 56% from the samples belonged to PstS14, 17.1% PstS10, 12.2% PstS4 and PstS7, 2.4% PstS13. Genetic lineages identified from Latvian wheat samples are found in the biggest cereal growing regions in Europe and are able to cause epidemics on wheat. Genetic lineages of P. striiformis from Latvian samples have not been identified before. All differential varieties were infected with P. striiformis in 2017, ‘Ambition’ and ‘Moro’ in 2018, no infection was observed on differentials in 2019 despite the presence of P. striiformis on winter wheat variety ‘Fredis’. The identification of genetic lineages of P. striiformis on wheat in Latvia is necessary to continue.

Japanese quince (Chaenomeles japonica) is a minor but prospective fruit crop due to the value of fruits and suitability for processing. Research aimed to clarify the incidence of quince leaf and fruit diseases depending on the year and plantation and determine the critical periods of diseases. The study was conducted from 2017 to 2019 in eight commercial Japanese quince plantations across different regions of Latvia. The incidence (%) of leaf and fruit spots and fruit rot was assessed several times during vegetation period. Considerable rot development was observed in only the first year of the study. The incidence of fruit rot in the middle of 2017 ranged from 0.7 to 11.7% depending on the plantation, while in 2018–2019 did not exceed 2% in any of the plantations. The critical period of fruit rot development was determined in the middle of fruit development and time closer to ripeness. Development of fruit spots increased closer to ripeness. In 2017, the incidence of fruit spots fluctuated from 0.7 to 44.0%, in 2018 – 7.7 to 46.0%, and in 2019 – 0.7 to 24.0%, depending on the plantation. The incidence of leaf spots reached high levels at the end of the vegetation periods (21–100% depending on year and plantation). The most critical periods of leaf spot development were the end of fruit development and full ripening of fruits. Determination of the critical periods of quince disease development could be used to create an effective and environmentally friendly disease control system.

Indexing, the ELISA test, Spiroplasma citri culturing, nucellar selection and shoot tip grafting are procedures used in Corsica to maintain virus-free budwood. Corsica has managed to remain free of the major diseases of citrus: Tristeza, Phoma tracheiphila (mal secco), Spiroplasma citri (stubborn) greening and Xanthomonas citri (citrus canker)