Gergana Kuncheva

В настоящото изследване е проведен метаанализ на данни за ефективността на подбрани микробиологични и ботанически биоинсектициди за борба с доматен миниращ молец (Tuta absoluta, Mayerick, 1917). За изследването са събрани 67 резултата от проучвания, публикувани в 31 научни публикации, в които се изследва въздействието им върху Tuta absoluta. Проведеният метаанализ не само оценява сравнителната ефективност на ботаническите и микробиологичните биоинсектициди спрямо конвенционалните инсектициди, но и предоставя научно обоснована подкрепа за по-широкото им внедряване в практиката. Резултатите имат пряко значение за разработването на устойчиви стратегии за контрол на икономически значими неприятели, включително Tuta absoluta, в условията на нарастващи екологични и регулаторни изисквания.

Water erosion is the most important degradation process affecting Epicalcic Chernozems quality. Many technologies and tillage systems are used to control water erosion in agricultural areas and to preserve soil fertility. Cover crops are considered an agricultural practice that contributes to carbon sequestration, improved infiltration, erosion control, and improved microbiological processes. The aim of the present study was to determine how the continuous application of a cover crop, in a wheat-maize rotation on sloped arable land, would alter the soil organic carbon, greenhouse gas fluxes and the soil microbial community, as the most significant regulator of soil carbon dynamics and biogeochemical cycles. An experiment with wheat and maize, in rotation, with the application of up-and-down slope culture (T0), traditional contour tillage (T1) and crop specific erosion control contour tillage (T2) was performed for four years in the experimental field Trastenik, Ruse. The T2 variant includes minimum tillage, direct sowing and cover crop after wheat harvest and before maize sowing. The content of total organic carbon, labile organic matter, the number of main groups of soil microorganisms, as well as CO2 and N2O soil emissions were monitored. In comparison to T0 and T1 treatments, an increasing number of ammonifying, cellulose decomposers, actinomycetes, and nitrogen-fixing bacteria were determined in the T2 variant under maize. In the spring and summer, there were higher number actinomycetes and cellulose decomposers in wheat variants. The enhanced total organic carbon was determined in T2 treatments. Soil CO2 emissions (29.8 kg ha-1 day-1) were reduced in the minimum tilled wheat variant, followed by the T0 (30.0 kg ha-1 day-1) and T1 (30.8 kg ha-1 day-1). The soil crust formation due to more intensive erosion process explains the lower average emissions under maize at the T0 variant, grown conventionally up and down the slope in comparison with T1 and T2. N2O emissions were generally lowest in the T2 treatments for both crops in four years. The minimum tillage for maize and wheat growing on slope lands with the inclusion of cover crop in the rotation, have a positive effect on soil organic matter, microflora and GHG emissions.

Modeling crop development under different agrotechnologies is important not only for assessing the factors that affect their yields but also because of the role of vegetation in regulation of the hydrology regime. For this reason, interest in the plant module in the semi-distributed hydrological model SWAT is increasing. The model has to be supplied with a lot of information for running and testing, which can be achieved with ground-based, statistical and satellite data. The aim of the study is to determine the accuracy of the SWAT model to predict crop development by using ground-based and satellite data for LAI in the case of a 5-year field experiment. Two staple crops in rotation were monitored—winter wheat and maize—under different erosion control technologies (up-and-down conventional tillage, conventional contour tillage, and minimum contour tillage with inclusion of cover crop before maize) on sloping terrain on moderately eroded Epicalcic Chernozem in the region of Ruse, north Bulgaria. The remote sensing data from the Copernicus Sentinel-2 mission were used for estimation of LAI of both crops and verified against ground-based data in two ways—via a custom LAI script available through the Sentinel Hub cloud platform and as input to a machine learning quantile regression forests (QRF) model. The calibrated satellite-derived LAI, ground-based soil moisture and yields data were used to calibrate several SWAT model parameters (EPCO, ESCO, CN2, LAImax, HU, HI) and assess the model performance regarding these variables. Although a good temporal fit of the SWAT-modeled LAI data with the satellite data was achieved, the accuracy of predicted LAI is moderately high only in the last two years of the rotation (R² = 60.4%). The accuracy of calibrated yields (R² = 55.5%) is acceptable in four of the years. On average for the period, the applied erosion control agrotechnologies did not cause significantly different yields, but they are 14% higher compared to the up-and-down conventional tillage. The most sensitive SWAT parameters accounting for this effect are EPCO and ESCO.

Microplastic pollution threatens soil health by disrupting chemical and microbial balances and impairing nutrient cycling, with effects that vary depending on soil properties. The objective of this study is to determine the effects of contamination with three types of microplastics (<5 mm)—polypropylene (PP), polyethylene (PE), and polyethylene terephthalate (PET)—on the microbiological and chemical parameters of four soil types in Bulgaria: Calcic Chernozem, Vertisol, Luvisol, and Fluvisol. A controlled 180-day laboratory incubation experiment was performed, where each soil type was contaminated with microplastics at three concentrations to monitor changes in key microbiological and chemical properties. It was established that microplastics contamination suppressed abundance of key microbial groups and limited nutrient availability, inducing a state of biological and chemical imbalance in the soil. PET exerted the strongest impact on soil chemical properties, with the agrochemical properties of Fluvisol being the most sensitive to MP contamination. PE and PET had the greatest influence on microbial communities, with Vertisol and Luvisol being the most affected in this regard. PP contamination altered key metabolic processes, with the specific impact being highly dependent on soil type and most pronounced in Chernozem. Furthermore, the concentration influenced the measured parameters, with the effects varying depending on the soil type and the microplastic type. Among the factors influencing soil responses to microplastic contamination, soil type appears to be the most decisive, followed by the type of microplastic.

The aim of the current study was to select and test the appropriate model and input parameters for remote sensing retrieval of surface soil moisture (SSM) in the case of bare Chernozems on flat and sloping terrains in northern Bulgaria under different tillage systems. Normalized synthetic aperture radar (SAR) measurements from Sentinel-1 C-band dual-pol products (Gamma-Nought in VV, ratio) were utilized in two ways to delineate SSM from environmental factors that bias determination. The accuracy of the obtained SSM prediction was evaluated against ground-based volumetric water content (VWC) measured in the 0–3.8 cm soil layer at multiple points using a TDR meter. The TDR VWC data were preliminarily calibrated against gravimetric measurements in the 0–5 cm soil layer. The obtained data for soil water retention curves in all studied variants were used to determine the range of soil moisture variation. The measured ground-based data for surface roughness generally correlate with the co-pol Gamma-Nought in VV. The data modeled with the surface soil moisture script in Sentinel Hub (SSM-SH) was calibrated using the ground-based data. Incidence angle normalization of Sentinel-1 products improved the relationship between SAR observables and SSM, when expressed as the ratio of soil moisture to total porosity (rVWC). The modeling indicated the highest importance of the optical indices, together with the temporal differences of radar descriptors sensitive to variations in soil moisture over time. Although the applied Random Forest Regression (RFR) model achieved higher accuracy during training (nRMSE of 7.27%, R² of 0.86), the Gaussian Process Regression (GPR) model provided better generalization performance on the independent validation dataset. The results proved the advantages of the joint utilization of temporal Sentinel-1 SAR measurements with Sentinel-2 optical acquisitions to determine SSM in different bare soil conditions for achieving high accuracy.

In drought-prone regions without irrigation systems, effective agrotechnologies such as subsoiling are crucial for enhancing soil infiltration and water retention. However, the effects of subsoiling can vary depending on crop type and environmental conditions. Despite previous research, there is limited understanding of the contrasting responses of C3 (sunflower) and C4 (maize) crops to subsoiling under drought stress. This study addresses this knowledge gap by assessing the effectiveness of subsoiling as a drought mitigation practice on Haplic Chernozem in Northern Bulgaria, integrating ground-based and remote sensing data. Soil physical parameters, leaf area index (LAI), canopy temperature, crop water stress index (CWSI), soil moisture, and yield were evaluated under both conventional tillage and subsoiling for the two crops. A variety of optical and radar descriptive remote sensing products derived from Sentinel-1 and Sentinel-2 satellite data were calculated for different crop types. Consequently, the use of machine learning, utilizing all the processed remote sensing products, enabled the reasonable prediction of LAI, achieving a coefficient of determination (R²) after a cross-validation greater than 0.42 and demonstrating good agreement with in situ observations. Results revealed differing responses: subsoiling had a positive effect on sunflower, improving LAI, water status, and slightly increasing yield, while it had no positive effect on maize. These findings highlight the importance of crop-specific responses in evaluating subsoiling practices and demonstrate the added value of integrating unmanned aerial systems (UAS) and satellite-based remote sensing data into agricultural drought monitoring.

Agroecosystems play a key role in the global carbon cycle, with CO₂ exchange driven by photosynthesis and respiration. Indicators such as gross primary productivity (GPP), net primary productivity (NPP), and carbon, water, and light use efficiency (CUE, WUE, LUE) are essential for assessing resource use in agricultural systems. Conventional tillage depletes carbon, water, and nutrients, negatively impacting the environment, while conservation practices aim to improve soil health and biodiversity. This study evaluated the effects of a cover crop in a wheat–maize rotation on sloped arable land prone to water erosion. The experiment involved minimum contour tillage combined with cover cropping, and its impact on carbon balance components and resource use efficiency was assessed. The results demonstrated that the inclusion of a cover crop significantly improved GPP and NPP. Water and light use efficiency also increased, particularly in 2022 and 2023, which were characterized by summer drought. However, carbon use efficiency remained unchanged over the study period. These findings highlight the potential of conservation practices, such as cover cropping and reduced tillage, to enhance productivity and resource efficiency in sloped agricultural landscapes under water stress conditions.

The key objectives of contemporary agriculture are restoring biodiversity, preserving ecosystem health, reducing the effects of climate change, and producing safe and healthy foods. Maintaining high soil fertility while reducing greenhouse gas emissions requires a precise assessment of how fertilization and crop rotation affect carbon and nutrient cycles in agroecosystems. Fertilization affects soil conditions, which alters the environment for soil microbial development and influences the number and composition of soil microbial communities, leading to changes in nutrient and carbon cycling. There is a lack of long-term experimental data on the impact of fertilizer treatments on soil CO₂ emissions, soil microbial communities, and their interactions. The novelty of this study is that it identified the fertilization effects on soil carbon sequestration, soil properties, and microbial communities in the context of a long-term fertilizer experiment in Luvic Chernozem. The fertilization treatments that were continuously pplied for 64 years under a four-crop (wheat, barley, corn, and bean) rotation were nitrogen (N), phosphorus (P), potassium (K), NP, NK, PK, NPK, and control. The chemical and microbiological soil properties and soil CO₂ emissions were monitored. The highest organic carbon content was observed under the NPK (1.42%) and NP (1.43%) treatments. N fertilizer application most significantly affected soil properties, including pH, electrical conductivity, and soil organic carbon content, altering the environment for soil microbial development and influencing the number and composition of soil microbial communities. On average, the field-measured soil C-CO₂ emissions were the most intensive under NP (2.76 kg ha⁻¹ h⁻¹), NPK (2.83 kg ha⁻¹ h⁻¹), and PK (2.51 kg ha⁻¹ day⁻¹) treatments.

A key objective of contemporary agriculture is to restore biodiversity, preserve ecosystem health, reduce the effects of climate change, and produce safe and healthy foods. Maintaining high soil fertility while reducing greenhouse gas emissions requires a precise assessment of how fertilization and crop rotation affect the carbon and nitrogen cycles in agroecosystems. In the context of a long-term fertilizer experiment on Luvic Chernozem, a monitoring of chemical and microbiological soil properties and CO2 emission was carried out. The fertilization treatments that have been continuously applied for 64 years under four-crop (wheat, barley, corn, and beans) rotation are N, P, K, NP, NK, PK, NPK, and 0. The highest organic carbon content was observed in the treatments NPK (1.42%) and NP (1.43%), and the lowest in the control (1.05%). The constant application of nitrogen as single fertilizer led to lower SOC values (1.22%). On average, CO2 emissions were the most intensive in NP (66.34 kg ha-1 day-1), NPK (67.82 kg ha-1 day-1) and PK (60.16 kg ha-1 day-1) treatments. The nitrogen fertilizer application most significantly alters soil properties including pH, EC, and SOC, which alters the environment for soil microbial development and influences the number and composition of soil microbial communities.