Sashka Savchovska* and Svetoslav Malchev

Fruit Growing Institute – Plovdiv, Agricultural Academy, 12 Ostromila Str., 4004 Plovdiv,
Bulgaria. Е-mails: кirilova.savchovska93@abv.bg*, svetoslav.m@outlook.com

In 2023, during scientific expeditions in the regions of Shumen, Ruen, Kuklen, Osmar, Varna and Aytos, part of a project for preservation of local genetic resources, nine local cherry cultivars and forms were discovered. The aim afterwards was to make a pomological description and identify the cultivars, as well as determining biometric parameters and ripening dates. Specimens were mapped with GPS coordinates in the project’s database, the ripening period of the fruits was determined, as well as their pomological characteristics. Biometric analyses of fruit and stones were performed and compared to the standard cultivars ‘Van’ and ‘Bigarreau Burlat’. The earliest ripening fruits are of specimen SS-08 on 8 June, and the latest ripening are of SM-29 is on 26 June. Specimen SS-06 had the highest fruit weight 6.09 g from the discovered cherry forms, and SM-29 had the lowest fruit weight 1.31 g. The stone weight ranged from 0.15 g in SM-29 to 0.36 g in SS-06. The shortest stalk was measured for SS-09 at 22 mm and the longest for SS-08 at 62 mm.

Received: 25 June 2025 / Accepted: 22 August 2025 / Available online: 27 August 2025

Open Access: This work is distributed under the Creative Commons Attribution 4.0 License.

Polya Avramova(1)*, Nikol Pashalieva (1), Desislava Gradinarska-Yanakieva (4),
Stoyan Stoyanov (2), Kostadin Kanchev (1), Gradimir Gruychev (2),
Evlogi Angelov (2), and Mihail Chervenkov (1,3)

1. Faculty of Veterinary Medicine, University of Forestry, 1797 Sofia, Bulgaria.
E-mails: polya_avramova01@abv.bg*, nikol.8161821@abv.bg, kkanchev@ltu.bg
2. Faculty of Forestry, University of Forestry, 1797 Sofia, Bulgaria.
E-mails: stoyani@ltu.bg, gradi.val@gmail.com, evoangelov@gmail.com
3. Institute of Biodiversity and Ecosystem Research Bulgarian Academy of Sciences, 1113 Sofia,
Bulgaria. E-mail: vdmchervenkov@abv.bg
4. Department of Reproductive Biotechnologies and Cryobiology of Gametes, Institute of Biology
and Immunology of Reproduction ‘Acad. K. Bratanov’, Bulgarian Academy of Sciences, 1113
Sofia, Bulgaria. E-mail: d.gradinarska@ibir.bas.bg

Representatives of Cervidae family are important bioindicators of the environmental pollution caused by various heavy metals. Significant amounts of toxic elements can accumulate in tissues and organs, particularly in the liver and kidneys. The study of accumulation levels can provide valuable information about the state of ecosystems and potential risks to human health, especially for areas with developed intensive industrial activity or proximity to pollution sources. The aim of this review is to summarise and compare reported data (2000–2024) on copper (Cu), mercury (Hg), and arsenic (As) accumulation in three edible organs (muscle, liver, and kidney) of the following members of Cervidae family: Roe deer (Capreolus capreolus L., 1758), Red deer (Cervus elaphus L., 1758), and Fallow deer (Dama dama L., 1758), and to discuss bioaccumulation trends and the potential health risks posed by the consumption of these products. The literature findings were compared with the established permissible limits according to European and international regulations. The possible health hazards associated with the consumption of meat and by-products, accumulation pathways, and organs affected by toxic damage were also examined. This information is essential for assessing the safety of wild game meat and by-products for human consumption, as well as for developing effective strategies for environmental protection and management of wild populations. In most studies, the reported levels of Cu, Hg, and As in deer meat, liver, and kidneys did not exceed the established safety thresholds. However, isolated cases of elevated concentrations, especially in the liver and kidney tissues from polluted areas, underscore the necessity of regular monitoring and cautious consumption.

Received: 18 July 2025 / Accepted: 29 August 2025 / Available online: 15 September 2025

Open Access: This work is distributed under the Creative Commons Attribution 4.0 License.

Asen Nikolov (1), Vesselin Koutev (2)*, Olga Nitcheva (3), Polya Dobreva (3),
and Donka Shopova (4)

1. Institute of Animal Science, Agricultural Academy, 1 Pochivka train stop, 2232 Kostinbrod,
Bulgaria. E-mail: asen30@abv.bg
2. Department of Agronomy, University of Forestry, 10 Kliment Ohridski Blvd., 1797 Sofia.
*E-mail: koutev@yahoo.com
3. Institute of Mechanics, Bulgarian Academy of Sciences, Acad. G. Bontchev Str., Bl. 4, 1113
Sofia, Bulgaria. E-mails: olganitcheva@yahoo.com, polya2006@yahoo.com
4. Climate, Atmosphere and Water Research Institute, Bulgarian Academy of Sciences,
66 Tsarigradsko shose Blvd., 1784 Sofia, Bulgaria. E-mail: dshopova@gmail.com

The objective of this study was to enhance the analysis of vegetation information using the normalised difference vegetation index (NDVI) through remote sensing data. NDVI values were correlated with ground measurements to derive insights into vegetation health and productivity. The experiment focused on courgette cultivation, where NDVI was measured at full growth using a Trimble GreenSeeker handheld sensor. Various nitrogen and phosphorus fertilisers were applied, including ammonium nitrate, phosphates, and polyphosphates. The highest NDVI value achieved was 0.802 with ammonium nitrate (nitrogen treatment) lacking phosphorus. On a polyphosphate background, the nitrogen source with the highest NDVI was KSC, yielding 0.800. With an orthophosphate background, KSC produced the best NDVI of 0.815. Its highest values were consistently linked with the orthophosphate background, and these results were statistically validated. A strong correlation coefficient (r = 0.72) was observed between NDVI and courgette yield. The integration of NDVI into our analytical approach significantly refined the analysis of vegetation data, yielding more precise insights.

Received: 26 June 2025 / Accepted: 29 August 2025 / Available online: 15 September 2025

Open Access: This work is distributed under the Creative Commons Attribution 4.0 License.

Kalin Markov (1, 5)*, Momchil Panayotov (2, 4, 5), Emilia Tcherkezova (1), and Andreas Huber (3)

1. National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences,
Acad. Georgi Bonchev Str., Bl. 3, 1113 Sofia, Bulgaria. *E-mail: kmarkov@geophys.bas.bg
2. University of Forestry, 10 Kliment Ohridski Blvd., 1797 Sofia, Bulgaria.
3. Department of Natural Hazards, Austrian Research Centre for Forests (BFW), Rennweg 1,
6020 Innsbruck, Tyrol, Austria.
4. Bulgarian Extreme and FreeSkiing Association (BEFSA), 7 Dubnitsa Str., Entr. B, 1756 Sofia,
Bulgaria.
5. Bulgarian Avalanche Association, 6 Shipka Str., 1000 Sofia, Bulgaria.

Backcountry skiing and touring have gained significant popularity in recent years, with numerous accidents occurring annually worldwide. Bulgaria’s Pirin Mountains have experienced similar trends, highlighting the urgent need for tools that enhance public awareness of avalanche-prone terrain. The Avalanche Terrain Exposure Scale (ATES) offers a systematic approach to classifying terrain based on its potential avalanche exposure, relying exclusively on static terrain and vegetation characteristics. While early ATES maps were manually produced by experts, recent advancements, such as machine learning-based approaches, have enabled scalable and accurate automated ATES (AutoATES) mapping over larger areas. A critical component of AutoATES classification involves modelling the protective effect of forests, which requires information on forest structure. Recent applications have incorporated remote sensing-derived forest structural parameters, such as percent canopy cover (PCC), to approximate forest protective effects in AutoATES model chains. In this study, we applied a recently developed machine learning framework for AutoATES classification to generate ATES maps for a study area in the Pirin Mountains, focusing on the effect of different PCC datasets on final and intermediate results of the model chain. Specifically, we tested a few forest cover scenarios: (1) the Copernicus Tree Cover Density product as a baseline, (2) a treeless simulation with PCC set to 0%, and (3) two alternative PCC estimates derived from RapidEye satellite imagery and information from digital elevation models. Our findings indicate that forests significantly reduce avalanche terrain exposure in the study area and that variations in PCC estimation can substantially alter the extent and distribution of different ATES classes. Notably, the second investigated RapidEye-based PCC estimation approach provided improved identification of forest gaps and dwarf mountain pine (Pinus mugo Turra) compared to the first, resulting in more accurate ATES maps in those respective areas.

Received: 30 June 2025 / Accepted: 01 September 2025 / Available online: 20 September 2025

Open Access: This work is distributed under the Creative Commons Attribution 4.0 License.

Penka Filyova (1)*, Plamen Ivanov (2), and Svetoslav Malchev (1)

1. Department of Breeding and Genetic Resources, Fruit Growing Institute, 12 Ostromila Str.,
4004 Plovdiv; Agricultural Academy, 30 Sukhodolska Str., 1373 Sofia, Bulgaria.
*E-mail: pepi0606@abv.bg
2. Department of Orchard Management and Plant Protection, Fruit Growing Institute, 12 Ostromila
Str., 4004 Plovdiv; Agricultural Academy, 30 Sukhodolska Str., 1373 Sofia, Bulgaria.

Phenological observations are among the most important for identifying the responses of fruit trees to climatic conditions of their habitat. Temperature changes have most significant influence on the phenology of fruit trees, particularly on flowering. This study aimed to monitor the pheno¬logical development of six sweet cherry cultivars grafted onto Maxma 14 rootstock. Phenological phases, including bud swelling (BBCH 51), full flowering (BBCH 65), and fruit harvest maturity (BBCH 87), were monitored to determine the duration of flowering, length of growing season, and the period from full flowering to fruit harvest maturity. Changes at the beginning of the growing period, occurring 2 to 8 days earlier in the second reporting year, were observed for most of stud¬ied cultivars. Deviations were also noted in the phenological phase BBCH 65, which occurred 4–8 days earlier and was 2–5 days shorter in the second year. This study demonstrated that temperature changes significantly impacted both duration and timing of the flowering and growing seasons in sweet cherries.

Received: 26 June 2025 / Accepted: 02 September 2025 / Available online: 20 September 2025

Open Access: This work is distributed under the Creative Commons Attribution 4.0 License.