Aridity in the Central and Southern Pannonian Basin

Long Time No Hear, Magnificent Wohlfahrtia! Morphological and Molecular Evidence of Almost Forgotten Flesh Fly in Serbia and Western Balkans

The "beautiful viviparous fly", Wohlfahrtia magnifica, may have a magnificent appearance due to its striking morphology; however, it is a potentially deadly agent of obligate traumatic myiasis in humans and animals, with a serious impact on welfare and economics. The fly is found across the Palearctic realm, including the Western Balkan region, with reports from former Yugoslavian countries from the first half of the 20th century. In this paper, a recent case of wohlfahrtiosis recorded in Northern Serbia is evidenced using morphological and molecular techniques. Larvae were collected from two adult sheep with severe hoof myiasis and two young sheep with genital and interdigital myiasis. Morphological identification was performed for adults bred from the infested vulva and third-stage larvae (L3) collected from the hoof wounds, supported with barcoding sequences of the COI gene obtained from larval pairs from the hoof wounds of older and the genitalia of younger sheep. W. magnifica was identified according to the appearance of male fly terminalia and the morphology of L3, which was confirmed after the comparison of representative sequences of the COI gene (deposited in GenBank™ under accession numbers MT027108-MT027114) to those available in GenBank™. This finding represents the first reported case of wohlfahrtiosis in the Western Balkans in 80 years, highlighting the need to re-inform relevant stakeholders to achieve adequate disease control.

Natural multi-hazard risk perception and educational insights among Geography and Tourism students and graduates amid the Covid-19 pandemic

Multi-hazard risk perception is an emerging research topic that has been gaining more and more interest since the beginning of the Covid-19 pandemic, due to the unprecedented interactions between the management practices of the pandemic and of other co-occurring natural hazards, and to the compounded impact of these multi-hazards. This paper aims to investigate the specialization and study level-dependent differences concerning multi-hazard risk perception and hazard-related education insights of future potential specialists in natural hazard-induced risk management and tourism reconstruction. These differences were explored by applying a multi-layer questionnaire on 547 Geography and Tourism students and graduates from two universities in Iași City (Romania) and Novi Sad (Serbia), and performing descriptive and differential statistical analyses. The statistically significant differences that emerged refer to estimations of the impact level of the Covid-19 pandemic and of other natural hazards at different spatial scales and on different socio-economic activities, on the training/career of the respondents, and to the hazard-related education improvements. Up to date, none of the papers in the existing literature integrate both the Covid-19 pandemic and the co-occurrent natural hazards as objects of students' perception, which makes the present study a starting point for such research endeavours.

Detailed Analysis of Spatial–Temporal Variability of Rainfall Erosivity and Erosivity Density in the Central and Southern Pannonian Basin

Estimation of rainfall erosivity (RE) and erosivity density (ED) is essential for understanding the complex relationships between hydrological and soil erosion processes. The main objective of this study is to assess the spatial–temporal trends and variability of the RE and ED in the central and southern Pannonian Basin by using station observations and gridded datasets. To assess RE and ED, precipitation data for 14 meteorological stations, 225 grid points. and an erosion model consisting of daily, monthly, seasonal, and annual rainfall for the period of 1961–2014 were used. Annual RE and ED based on station data match spatially variable patterns of precipitation, with higher values in the southwest (2100 MJ·mm·ha−1·h−1) and southeast (1650 MJ·mm·ha−1·h−1) of the study area, but minimal values in the northern part (700 MJ·mm·ha−1·h−1). On the other hand, gridded datasets display more detailed RE and ED spatial–temporal variability, with the values ranging from 250 to 2800 MJ·mm·ha−1·h−1. The identified trends are showing increasing values of RE (ranging between 0.20 and 21.17 MJ·mm·ha−1·h−1) and ED (ranging between 0.01 and 0.03 MJ·ha−1·h−1) at the annual level. This tendency is also observed for autumn RE (from 5.55 to 0.37 MJ·mm·ha−1·h−1) and ED (from 0.05 to 0.01 MJ·ha−1·h−1), as for spring RE (from 1.00 to 0.01 MJ·mm·ha−1·h−1) and ED (from 0.04 to 0.01 MJ·ha−1·h−1), due to the influence of the large-scale processes of climate variability, with North Atlantic Oscillation (NAO) being the most prominent. These increases may cause a transition to a higher erosive class in the future, thus raising concerns about this type of hydro-meteorological hazard in this part of the Pannonian Basin. The present analysis identifies seasons and places of greatest erosion risk, which is the starting point for implementing suitable mitigation measures at local to regional scales.

Deoxynivalenol Occurrence in Triticale Crops in Romania during the 2012-2014 Period with Extreme Weather Events

This article aims to evaluate deoxynivalenol occurrence in triticale crops in Romania in years with extreme weather events (2012: Siberian anticyclone with cold waves and heavy snowfall; 2013 and 2014: “Vb” cyclones with heavy precipitation and floods in spring). The deoxynivalenol level in triticale samples (N = 236) was quantified by ELISA. In Romania, the extreme weather events favoured deoxynivalenol occurrence in triticale in Transylvania and the Southern Hilly Area (44–47° N, 22–25° E) with a humid/balanced-humid temperate continental climate, luvisols and high/very high risk of floods. Maximum deoxynivalenol contamination was lower in the other regions, although heavy precipitation in May–July 2014 was higher, with chernozems having higher aridity. Multivariate analysis of the factors influencing deoxynivalenol occurrence in triticale showed at least a significant correlation for all components of variation source (agricultural year, agricultural region, average of deoxynivalenol, average air temperature, cumulative precipitation, soil moisture reserve, aridity indices) (p-value < 0.05). The spatial and geographic distribution of deoxynivalenol in cereals in the countries affected by the 2012–2014 extreme weather events revealed a higher contamination in Central Europe compared to southeastern and eastern Europe. Deoxynivalenol occurrence in cereals was favoured by local and regional agroclimatic factors and was amplified by extreme weather events.

Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.