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Flax is cultivated on small areas in Latvia. Flax gives dual-purpose production – fibre and seeds. Latvian flax has a high fibre and seed quality. The quantity of flax fibre and seed yield depends on many factors. Several qualitative and quantitative traits, such as technical stem length, resistance to lodging, vegetation period, yield of straw and seeds, fibre and oil content and quality were evaluated. For the further development of Latvian economy flax varieties and hybrids of Latvian origin are an important goal. Fibre flax varieties 'Blue di Riga', 'Priekuļu 665', 'Ošupes 30' and breeding lines 'S-64-17-93' and 'L11-11/11-94' are valuable material containing a qualitative and quantitative indicators of variety. 92 Latvian accessions of flax hybrids have been evaluated, and 12 accessions with the best seed and straw yield have been selected for further DNA analysis. This will simplify and accelerate the selection of new valuable hybrids that will provide particular advantages in agriculture. The genetic analysis of DNA determines the kinship and diversity of flax varieties and breeding lines which can be used further in flax breeding.

Consumers are increasingly demanding choices of ready-made foods with excellent organoleptic and health-related properties. There are two main trends in Europe; firstly, consumers are increasingly choosing foods that are comfortable for use, secondly, the number of people who are overweight is increasing, with more consumers paying close attention to the ingredients and nutritional value of products in order to balance the amount of the food they consume per day. The aim of the research was to develop new potato main courses and to determine their nutritional value. The research was carried out at the Faculty of Food Technology of the Latvia University of Agriculture, Institute of Food Safety, Animal Health and Environment "BIOR" and Laboratory of Mineral Nutrition at the Institute of Biology of the University of Latvia. Four different potato main course types with amaranth, quinoa, bulgur and chicken were prepared for the study; plain potatoes were used as the control sample. The content of protein, carbohydrates, lipids, fibre and minerals (N, P, K, Ca, Mg, S, Fe, Mn, Zn, Cu, Mo, B) was determined in all potato main course samples. The addition of amaranth, quinoa and bulgur significantly increased the content of dietary fibre, protein, carbohydrates and lipids (p<0.05), whereas the addition of chicken fillet significantly increased protein and lipid content, but reduced the content of carbohydrates and dietary fibre. The content of various minerals, which are an indispensable part of the diet as they are necessary for the body's life processes and normal development, was significantly increased by the addition of chicken to the potato main course. The highest dietary fibre content was detected in potato main course with amaranth (3.0 g per 100 g product), drawing up to 9.0 g dietary fibre per one serving (300 g). Following the Regulation (EC) No 1924/2006, potatoes with amaranth can be defined as the “source of fibre”. Keywords: nutritional value, potato main course, retorts packaging. Article DOI: http://doi.org/10.15544/RD.2017.078

Agronomy/agriculture and biodiversity (ag & biodiv) communities face several major societal, economic, and environmental challenges that data science approaches will help address. To achieve their goals, researchers of these communities must be able to rapidly discover, aggregate, integrate, and analyse different types of data and information sources. Semantic technologies, combined to open, FAIR data and services, is one of the answers to fully knowledge-driven, and transparent science and innovation. The D2KAB project (www.d2kab.org) aims to create a framework to turn agronomy and biodiversity data into knowledge – semantically described, interoperable, actionable, open – and investigate the scientific methods and tools to exploit this knowledge for applications in agriculture and biodiversity sciences. This project, funded by French ANR (2019-2023), will provide the means –ontologies and linked open data– for ag & biodiv to embrace semantic Web technologies in order to produce and exploit FAIR data and services. To do so, D2KAB will develop new original methods and algorithms in the following areas: data integration, text mining, semantic annotation, ontology alignment and linked data exploitation and visualization. D2KAB project brings together a unique multidisciplinary consortium of 12 partners to achieve this objective: 2 informatics research units (LIRMM, I3S); 6 INRA/IRSTEA/IRD research units at the interface of computer science and ag & biodiv (URGI, MaIAGE, IATE, DIST, TSCF, DIADE) specialized in agronomy or agriculture; 2 labs in biodiversity and ecosystem research (CEFE, URFM); 1 association of agriculture stakeholders (ACTA); and 1 partnership with Stanford BMIR department. Three main goals drive D2KAB’s roadmap: To develop state-of-the-art methods and technologies for ontology lifecycle and alignment. To build the agronomy, agriculture and biodiversity Linked Open Data cloud. To enable new semantically driven agronomy and biodiversity science. The work is starting from the recommendations of several RDA WG and IG already published or in progress (e.g. Agrisemantic WG, Vocabulary Services IG, Wheat and Rice Data Interoperability WGs, Agricultural Data IG, SHARC IG). Some of the key technological building blocks of D2KAB are AgroPortal, a reference repository for ontologies and vocabularies in agronomy; AgroLD, a semantic Web knowledge base that integrates agronomic data from public databases including GO associations, Gramene, UniprotKB, and OryGenesDB ; Corese, a semantic Web factory that implements the W3C standards RDF, RDFS, OWL-RL and SPARQL, and LDScript, a Linked Data Script Language, and STTL, the SPARQL Template Transformation Language for RDF; and Alvis, a text mining for semantic normalisation of free text by ontologies. D2KAB will allow the valorization of ag & biodiv data into real world applications leading to economic impact, smart agriculture and ecological preservation. Five driving scenarios are planned: development of an ontology-based expert system to select food packaging solutions; creation of an augmented semantic reader for Plant Health Bulletins; advanced integration of textual and experimental data on wheat phenotypes; development of new ontologies on plant root traits and extension of the Thesaurus Of Plant Characteristics; integration of plant functional biogeography data related to the Mediterranean Basin. Each of the project scenarios will have a significant impact and produce concrete outcomes for ag & biodiv scientific communities and socio-economic stakeholders in agriculture.

Coastal area monitoring is a significant task in the national development and environmental protection. Study area of this work is the Baltic Sea Region, particularly focusing on the land cover changes in the coastal area from Cape Kolka to the Latvian-Lithuanian border. The aim of this research is to estimate and illustrate different examples of monitoring and mapping land cover changes in the coastal area using remotely sensed data - orthophoto, multispectral data and radar data. The results of the research include vector maps created from satellite images and comparison between different land cover value identification methods. IOP Conference Series: Materials Science and Engineering, 96 ISSN:1757-8981 ISSN:1757-899X

Remote sensing has been widely used to obtain land cover information using automated classification. Land cover is a measure of what is overlaying the surface of the earth. Accurate mapping of land cover on a regional scale is useful in such fields as precision agriculture or forest management and is one of the most important applications in remote sensing. In this study, multispectral MODIS Terra NDVI images and an artificial neural network (ANN) were used in land cover classification. Artificial neural network is a computing tool that is designed to simulate the way the human brain analyzes and process information. Artificial neural networks are one of the commonly applied machine learning algorithm, and they have become popular in the analysis of remotely sensed data, particularly in classification or feature extraction from image data more accurately than conventional method. This paper focuses on an automated classification system based on a pattern recognition neural network. Variational mode decomposition method is used as an image data pre-processing tool in this classification system. The result of this study will be land cover map.

Sustainable and economically based forestry needs modern inventory and monitoring techniques. One of the most common technologies for identification of forest tree species and monitoring of forest growth conditions is the hyperspectral remote sensing. This technology gives an opportunity to economize human resources and time for data collecting and processing. The spectral behaviour of plant leaves depends on number of factors, including environmental background. The aim of this study was to assess the tree reflectance spectra in relation to the growth conditions to take into account potential differences for increasing precision of species identification in Latvian forests and for estimating of forest growth conditions. Remote sensing data were obtained using a specialized aircraft (Pilatus PC-6), which is equipped with a high-performance airborne VNIR pushbroom hyperspectral system (AisaEAGLE). The study area was flown at 1000 m altitude. Data was recorded in the 400–970 nm spectral range, spectral resolution was 3.3 nm, ground resolution 0.5 m. Data processing consisted of manually selecting trees with a recognizable tree crowns in the airborne images. Tree centres were adjusted by putting them in the accurate position according to the situation in aerial photography. All trees with a diameter at breast height DBH of more than 5 cm were measured and for each tree coordinates, its species, height, DBH, crown width and length were recorded. Differentially corrected Global Positioning System measurements were used to determine the position of each plot centre. Data from different hyperspectral bands were compared using ANOVA at confidence level 95 %. Four species: Scots pine ( Pinus sylvestris L.), Norway spruce ( Picea abies (L.) H. Karst), silver birch ( Betula pendula Roth), and European aspen ( Populus tremula L.) – were examined in distinct forest site types. The spectral response of studied species was 1) different between species and 2) different between site types within each species, correlating with soil fertility gradient and soil moisture gradient. Differences between species occurred most in the intensity of reflected electromagnetic radiation rather than distinctive locations of maximums or minimums in spectrum curve, and near infrared (NIR) region of spectrum showed more differences between species than visible light zone. Most informative wavebands for distinguishing differences between site types were 805 nm and 644 nm. Keywords: Hyperspectral, remote sensing, leaf reflectance, species identification, forest growth conditions, soil fertility, forest site types, forest mapping. Article DOI: http://doi.org/10.15544/RD.2015.045

One of the most important stand productivity and competition indicators is height annual increment, which is affected by various factors such as soil preparation, initial density as well as various management risk factors. Empirical material for the research was collected in the northern part of Latvia. In four pure Scots pine stands in Myrtillosa forest site type 29 circular plots tree diameter, height and the last five years annual height increment was measured and visual state of health was described. The aim of this research is to analyse Scots pine height annual increment in naturally regenerated young forest stands in Myrtillosa site type forest stands and to give an evaluation of the impact of the initial stand density and the health status on height growth. The mean height increment in studied stands is 0.26 ± 0.009 m and the average periodical increment is 0.37 ± 0.042 m. The annual height increment has been in the height range from 0.23 to 0.53 m. Initial stand density affects the annual height increment significantly. In the stand with an initial density of 5770 ± 961 trees the height increment during the last five years has risen by 36%, but in stand with initial density of 12,650 ± 1,581 trees (P = 51.8 % and R = 6.0 %) the height increment during the five-years period has increased by only 12 %. The tree health status does not affect the tree height increment significantly. Keywords: health status, risk factors of management, stand density. Article DOI: http://doi.org/10.15544/RD.2015.076

The aim of this paper is to examine the relationship between the factor endowment and the pattern of intra-industry trade. Our empirical analysis relates to Hungary’s intra-industry trade in agri-food products with 26 member states of the EU over the period 1999-2010. Estimations reject the comparative advantage explanation of vertical intra-industry trade and provide partial support the prediction of Flam and Helpman model. Findings highlight that nature of factor endowments play also important role in explanation of vertical intra-industry trade. Other variables like market size and distance confirm the theoretical expectations. In addition, trade with new member states positively, whilst the EU accession ambigouosly influence the share of vertical IIT.