Ensuring animal welfare, labor efficiency, and sustainable productivity in dairy farms requires barn and facility designs that are compatible with herd structure and animal behaviour. The aim of this study is to present the development of an expert system called the Architectural Designing of Dairy Farms (ADDF), designed to automate and support the architectural planning process of dairy cattle operations. The system is based on ten modules encompassing herd size estimation, group management, and architectural layout of pens and service structures. Each module operates with clearly defined input-output relationships, and the system was implemented as a desktop application using Visual Basic 6.0. User-defined parameters, including the number of mature cows and milking shift length, allow for dynamic scenario analysis across different herd sizes and farm management strategies. A hypothetical case involving 250 dairy cows and a 3-hour milking shift was used to validate the model, and architectural plans were generated using AutoCAD. The results demonstrate that ADDF significantly reduces design time, improves decision-making in herd housing and infrastructure planning, and supports animal welfare-focused design principles. The system offers a flexible and practical tool to improve the efficiency of dairy farm design processes, with potential for integration into future smart farming systems.
In the study, the effect of carbon dots (CDs) obtained from different biological sources, such as Spirulina and banana peel, which serve as carbon and nitrogen sources, on corn seed germination was investigated using the green hydrothermal synthesis method. The synthesized CDs were thoroughly characterized using various analytical techniques. The surface morphology, wettability, and moisture retention of the seeds after CD application were evaluated using appropriate analytical methods. The synthesized carbon dots exhibited a narrow particle size distribution ranging from 10 to 15 nm and a high antioxidant activity value of 87.72%. Application of CDs to the seeds enhanced their surface properties, improving water retention and accelerating germination, with the most pronounced effect observed at a concentration of 0.1 mg mL-1. At this concentration, root and shoot lengths increased nearly threefold compared to the control. Notably, SB-CD treatment exhibited a clear dose-dependent hormetic pattern, in which low concentrations stimulated germination and seedling growth whereas higher concentrations progressively inhibited development. These results demonstrate the potential of naturally derived SB-CDs as sustainable nanomaterials for agricultural biotechnology, offering promising applications in seed treatment, crop enhancement, and eco-friendly agricultural product development.
Aphids were previously considered insignificant pests in wheat production areas in the Mediterranean region of Türkiye. However, in recent years, it has been observed that the honeydew they produce negatively affects plant development due to the increase in their population density. Although abiotic factors (primarily climate) and natural enemies may play an important role in the population development of aphids, their effects are not yet well understood, revealing this information may contribute to aphid’s pest management. The field study was conducted in 2022 and 2023 in the Soil and Water Research Station in Tarsus, Mersin province located in the eastern Mediterranean region of Türkiye. The wheat aphid Sitobion avenae (F.) (Hemiptera: Aphididae) and their predators and parasitoids were randomly sampled from leaves and ears and counted in the wheat field (cultivar Adana 99). A significant number of aphids and beneficial insects such as coccinellids, syrphids and mummified aphid individuals were collected from the ears. Although no significant relationship was observed between aphid population and climatic factors, relatively warmer (daily average temperature exceeded 20 °C) and drier (varying between 0.95 and 2.27 kg/m2) spring period may have shortened the vegetation period, leading to earlier plant maturation, thereby reducing aphid development and density in 2023. Despite the positive and strong relationships found between aphid and beneficial insect population densities (total) in both years, this was not sufficient to prevent aphid density when on average, there was slightly more than 20 aphids per leaf + ear, while the number of predators was approximately 2, consequently leading to formation of honeydew and sooty mold in 2022. Therefore, to avoid chemical control, first, habitat planning is recommended to promote earlier colonization of beneficial insects in wheat fields.
In controlled, shaded agricultural environments, conventional remote sensing technologies are often ineffective due to their reliance on direct sunlight and sensitivity to variations in reflectance and absorption. These limitations reduce the effectiveness and precision of image-based leaf area estimation methods, especially in settings such as polytunnel farming, greenhouses, and areas covered by shade netting. Machine learning methods, on the other hand, are used to obtain reliable estimates from complex datasets in various fields, including biology, economics, and engineering. They provide significant practical advantages in applications such as production forecasting and environmental impact analysis in agriculture. This study investigates the use of machine learning models (ElasticNet, Support Vector Regression, Random Forest, Gradient Boosting, and XGBoost) to estimate the leaf area of faba bean cultivars grown under different shading levels. The study used non-destructive morphological traits, such as plant height, leaf number, and leaf density index, as predictive variables. The dataset, incorporated diverse environmental and biological factors, including varying shading intensities, plant densities, and growth stages across different years. The Support Vector Regression (SVR) model achieved the highest predictive accuracy (R² = 0.92, RMSE = 0.07), significantly outperforming the linear ElasticNet model (R² = 0.76). The proposed model provides a low cost, efficient, and non-destructive approach to leaf area estimation in shaded environments where conventional remote sensing is limited.
In this study, the effects of different organic fertilizer applications (cattle manure, leonardite and seaweed) on grain yield and some plant characteristics of oat (Avena sativa L.) were investigated. The oat variety 'Kahraman'was utilized in the experiment. The trial was established in Kahramanmara & scedil; S & uuml;t & ccedil;& uuml; & Idot;mam University, Faculty of Agriculture Research and Application Area in 2020-21 and 2021-22 growing seasons according to the Bi-level Nested Classification Model trial design with 4 replications. Fertilizer doses were applied as leonardite 50, 100, 150, 200 and 250 kg da-1, cattle manure 1000, 1500, 2000, 2500 and 3000 kg da-1, seaweed 30, 40, 50, 60 and 70 g da-1 and chemical fertilizer pure nitrogen 5, 10, 15, 20 and 25 kg da-1. According to the 2-year average results of the study, it was determined that the differences between the treatments were statistically significant in the traits examined except ripening time, biological yield and harvest index. Grain yield varied between 264.13-416.29 kg da-1 in the first year, 212.48-449.17 kg da-1 in the second year and the average of the years varied between 238.31-420.73 kg da-1. In the study, the highest grain yield was observed in the first year in chemical fertilizer (CF-3) application, while in the second year and the average of the years, it was obtained in cattle manure (CM-5). It has been determined that cattle manure reached the highest value in terms of grain yield (3000 kg da-1) and seaweed in terms of upper internode length (40 g da-1). It has been observed Cattle manure was found to be superior to chemical fertilizer and other organic fertilizer forms. In this study, it was concluded that cattle manure (3000 kg da-1) dose was the most suitable dose for oat cultivation in Kahramanmara & scedil; ecological conditions.
Biochar (B) and pumice (P) enhance water retention and soil-plant productivity, while arbuscular mycorrhizal fungi (AMF) improve plant access to soil moisture and promote productivity. The use of recycled wastewater (WW), is an effective approach to protect freshwater resources, increases soil-plant fertility, reduces the need for fertilizer, and contributes to the sustainable European Green Consensus by reducing the discharge of WW. However, the heavy metal (HM) content of WW can negatively affect the environment, soil, and plant health. The study investigated the B, P, and their combination (B+P) in the soil of pepper irrigated with different water qualities in conditions with and without AMF, hypothesized that B+P under AMF would limit HM contamination in irrigation with WW, in addition to reducing irrigation water quantity (IWQ) and increasing irrigation water productivity (IWP), and soil-plant efficiency. As a result, IWP increased between 14% and 38% in parallel with the IWQ reduction by 2% to 15%, and yield increased between 7% and 20% with B+P, B, P, respectively, also AMF and WW. These treatments increased the yield of the plant by improving the organic matter, total nitrogen, and cation exchange capacity of the soil, but a moderate increase in soil salinity for B and WW treatments, thus increasing the electrolyte leakage. Although there was more Fe, Cu, Mn, Zn, Pb in the soil of the B, P, B+P and AMF and WW, no-contamination was observed and B, P, B+P increased the plant's uptake of Fe, Cu, Mn, Zn, Pb while limiting the uptake of Cd, Cr, Ni, and AMF also created a barrier in the uptake of HM. However, in WW, the accumulation of HM in pepper was higher but did not exceed threshold values. It was found that B+P under AMF conditions can be safely used in irrigation with WW due to its effects in reducing HM, its regulating properties of soil and plants, and its increase in IWP.
The aim of this study was to comprehensively evaluate the antioxidant and antimicrobial properties of extracts obtained using different solvents (hexane, acetone and methanol) from two freshwater green microalgae species Chlorella sp. and Scenedesmus sp., commonly found in T & uuml;rkiye. In addition to antioxidant and antimicrobial assays, total phenolic content (TPC), total flavonoid content (TFC), chlorophyll (Chl a and b) and total carotenoid levels were quantitatively determined. A qualitative phytochemical screening was also conducted to detect the presence of alkaloids, tannins, saponins, terpenoids, steroids and glycosides. Antioxidant activity was assessed using DPPH radical scavenging and CUPRAC reducing power assays. The three solvents of different polarity, methanol extract was the most efficient and had the highest extraction efficiency (17%). The hexane extract of Scenedesmus sp. showed the highest DPPH scavenging activity (lowest IC50), while the methanol extract of Chlorella sp. exhibited the strongest reducing power in the CUPRAC assay. Antimicrobial activity, evaluated using the disc diffusion method, revealed that the acetone and methanol extracts showed significant antimicrobial activity, particularly against E. coli, K. pneumoniae and Candida albicans. The highest TPC values were observed in the methanol extracts: 55.95 mg GAE/g for Chlorella sp. and 70.24 mg GAE/g for Scenedesmus sp. Similarly, the methanol extract of Chlorella sp. exhibited the highest TFC (73.41 mg QE/g), while Scenedesmus sp. showed a lower flavonoid content (16.16 mg QE/g). Chlorophyll levels were relatively similar across all solvent extracts. These findings highlight the potential of Chlorella and Scenedesmus species as valuable sources of natural antioxidants and antimicrobial agents, with solvent type significantly influencing their bioactive compound profiles and biological activities.
Using Antalya province as a case study, this research investigates access to essential public services for individuals with physical, hearing, and visual impairments and the factors influencing it. Data were collected via face-to face interviews and questionnaires from 61 individuals with physical disabilities, 60 with hearing impairments, and 60 with visual impairments. Independent samples t-tests were employed to assess geographic disparities in access, while ordinal logistic regression analysis identified predictive factors. Analysis revealed that individuals with physical and visual disabilities experienced Limited Access (Level 2) in both rural and urban areas. In contrast, individuals with hearing disabilities demonstrated Full Access (Level 1) in urban areas but Limited Access in rural settings. The results indicate that while place of residence (rural versus urban) did not significantly impact access for the physically disabled group, rural residence negatively affected service access for those with hearing and visual impairments. Public services should therefore be reconfigured to address specific barriers for different disability groups and to implement effective oversight to improve overall accessibility.
Agricultural land is increasingly affected by rapid and uncontrolled urban expansion processes. The intensification of land use/land cover (LULC) changes, driving by rising population densities in urban areas, poses significant threats to environmental sustainability and agricultural land resources. Focusing on the city of Adapazari, this study aims to simulate future urban growth and assess its potential impact on agricultural land loss by integrating Cellular Automata and Markov Chain (CA-Markov) modeling with Land Parcel Identification System (LPIS) data. Using satellite images from 1990, 2005, and 2020, LULC changes were analysed and projected for the years 2035 and 2050. The model achieved a high level of accuracy with an overall agreement of 93% and a strong Kappa coefficient. Simulation results indicate that by 2050, settlement areas could increase by about 82%, primarily at the expense of agricultural land. LPIS-based analysis shows that 3,173 ha of agricultural land, including arable lands, grassland, and hazelnut, are at risk of conversion to urban use. These findings highlight the urgent need for sustainable urban planning policies that protect fertile agricultural land while accommodating urban growth. This research provides valuable insights for decision-makers and urban planners concerned with balancing development and environmental preservation in rapidly urbanizing regions.
This study aims to estimate the live weight of broilers using image processing and deep learning techniques. The proposed method is designed to optimize management processes in broiler production systems, reduce labor requirements and operational costs, minimize animal stress caused by direct human contact, and serve as an effective alternative to traditional manual weighing practices. The study was conducted at a commercial broiler farm located in Kahramanmara & scedil;, in the Mediterranean region of T & uuml;rkiye. An automatically controlled measurement enclosure was constructed to capture broiler images with minimal human intervention. Digital cameras mounted at the top of the enclosure recorded images of broilers that spontaneously entered the enclosure. By using these images, live weight estimation was carried out in two stages: the first stage involved morphological image processing in the MATLAB environment, while the second stage focused on deep learning-based modeling for prediction. During the image processing stage, multiple regression analysis was performed using the actual weights obtained through manual weighing and the estimated weights derived from image-based measurements. The analysis resulted in an adjusted R-2 value of 0.97 and a standard error of +/- 131 g (P<0.01). The mean absolute error (MAE) was calculated as 84.4 g, while the mean relative error (MRE) was found to be 7.6%. In the deep learning stage, the YOLOv8 model was trained for 150 and 500 epochs. Notable improvements in both accuracy and generalization capability were observed after 500 epochs. Under these conditions, the model achieved a high mean Average Precision (mAP) of 0.969, with substantial increases in precision, recall, and F1-score across all 17 predefined broiler live weight classes. Furthermore, regression-based performance indicators were approximated from the class-based predictions to enable a quantitative assessment of weight estimation accuracy. Based on this indirect evaluation, the proposed model achieved an estimated MAE of 37.0 g and MRE of 4.43%. Overall, the findings suggest that the proposed framework has strong potential for adaptation to live weight prediction in other livestock species.
Wheat (Triticum aestivum L.) is a staple crop globally, but its yield and quality are significantly affected by viral diseases, particularly the Wheat Dwarf virus (WDV). Developing antiviral strategies is crucial for enhancing wheat productivity. This study aims to investigate the use of CRISPR/Cas12a as an antiviral agent against WDV without requiring genetic modification of the host plants. A total of 10 WDV isolates from wheat and barley collected across six provinces in T & uuml;rkiye, along with four reference isolates from Hungary, Sweden, and Iran, were analysed. The study demonstrates selective cleavage of conserved WDV regions by CRISPR/Cas12a RNPs in vitro, representing an early proof-of-concept for non-GMO antiviral approaches rather than a direct field application. The designed gRNAs effectively induced DNA cleavage in WDV genomes, with gRNA1 showing consistent performance across all tested isolates. gRNA2 exhibited variability, successfully targeting seven out of ten isolates. No off-target effects were observed in control assays, confirming the specificity of the gRNAs. The CRISPR/Cas12a system, guided by specifically designed gRNAs, can effectively target and cleave WDV genomes, providing a potent, non-GMO antiviral strategy. This approach has potential applications for managing WDV infections in wheat and barley, highlighting its versatility and effectiveness.
Despite its strategic role in the wheat trade, Türkiye’s wheat markets remain vulnerable to volatile price movements and speculative behavior. While significant progress has been made in the global literature on detecting speculative bubbles in agricultural commodities, there is a clear empirical gap in applying advanced econometric bubble detection methods to the wheat sector in Türkiye. This study aims to address this issue by offering region-specific insights into the speculative dynamics that influence wheat prices in major production centers in Türkiye. We applied the Supremum Augmented Dickey-Fuller (SADF) and Generalized Supremum Augmented Dickey-Fuller (GSADF) tests to monthly wheat price data from Ankara, Edirne, and Konya provinces to detect speculative price bubbles. After identifying bubble periods, we used probit and logit models to explore potential drivers, including the producer price index (UFE), input price index (IPI), real effective exchange rate (R), and the marketed quantity of wheat (LNQ). The empirical results revealed multiple statistically significant bubble episodes in Ankara and Edirne between 2020 and 2022. In contrast, Konya exhibited fewer, shorter-lived bubbles concentrated in 2023–2024. GSADF results confirmed that price exuberance was neither uniformly distributed nor persistent across provinces. Regression analyses showed that the UFE and IPI significantly increased the probability of bubble formation in Ankara and Konya. Meanwhile, R had a stabilizing effect in Ankara and Edirne. These results imply regional differences in speculative price behavior within Türkiye’s wheat market. The results highlight the importance of incorporating localized market structures and macroeconomic indicators into national agricultural price stabilization policies. Methodologically, this study contributes by integrating recursive right-tailed unit root tests with nonlinear probability models to better understand speculative forces in commodity markets. As a policy recommendation, the licensed warehousing system should be revised to enable physical and/or online transactions through regionally authorized procurement centers. Real-time monitoring and analysis of wheat exchange prices would allow timely intervention when price bubble signals emerge.
Lettuce (Lactuca sativa L.) is an annual leafy vegetable crop from the Asteraceae family with the possibility to grow all year round. Two lettuce cultivars (red 'Murai' and green 'Aleppo') were cultivated in a greenhouse using two biofertilizers (a mixture of different effective microorganisms and Trichoderma spp. fertilizer), and their combination, during three consecutive growing seasons. This study aimed to find the optimal combination of fertilization treatments and growing seasons depending on different production criteria, using a multi-criteria decision-making (MCDM) method, Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS). A total number of 12 lettuce production models (alternatives) were proposed regarding fertilization treatments and planting dates. The study analyzed 12 criteria, which were divided into three groups: quantity, quality, and economic criteria. Weight coefficients were determined using the Analytic Hierarchy Process (AHP) method while, the stability of the obtained ranking list was investigated by applying comparative analysis using four other MCDM methods, as well as by performing sensitivity analysis through four scenarios. Cultivar 'Murai' showed the best results in the winter growing season with the application of Trichoderma spp. fertilizer, while cultivar 'Aleppo' in the spring with the combined fertilizers (effective microorganisms + Trichoderma spp). This study supports farmers and agricultural companies in optimizing planting schedules and biofertilizer selectiontailored to specific cultivars, integrating quantity, quality, and economic criteria to enhance productivity and sustainability. Furthermore, it reveals the significant role of cultivar choice in optimizing biofertilizer type and growing season selection for lettuce production, demonstrating the MARCOS method’s robustness as a decision-support tool in agricultural planning and management.
Understanding the factors influencing phosphorus (P) and nitrogen (N) use efficiencies under soil P deficiency, is essential for developing strategies to mitigate P limitation. This study was conducted to investigate how common bean copes with P deficiency under Mediterranean agroecological conditions. Three genotypes (Contender (C), Djadida (Dj), and Tema (T)) were grown across nine field sites in the Tizi-Ouzou province of Algeria. The sites were grouped into three clusters according to soil texture, pH, and altitude. At flowering stage, plants were uprooted and analyzed for growth, nodulation, arbuscular mycorrhizal fungi (AMF) colonization, P and N content, and corresponding nutrient use efficiencies. The study results show highly significant effect of cluster (P<0.001), a significant effect of genotype (P<0.05), and a significant genotype & times; cluster interaction (P<0.01), on plant growth and P and N Use Efficiencies. Shoot Dry Weight, P and N use efficiencies differ significantly among clusters and genotypes within the same cluster, while AMF colonization didn't differ between genotypes. These parameters were significantly higher in the clusters 2 and 3 characterized by fine textured and alkaline soils and situated at low altitudes. Nodulation occurred only in the cluster 1 with coarse textured soils, neutral pH and high altitudes. The genotype C performs better in clusters 1 and 2, while T performed better in cluster 3. Interestingly, Dj showed intermediate performances between C and T, across all clusters. Integrating such knowledge into breeding programs could strengthen common bean resilience and productivity in phosphorus-deficient agro-ecosystems.
The increasing presence of microplastics (MPs) and heavy metals (HMs) in aquatic ecosystems poses a growing concern due to their potential ecotoxicological effects. While the individual toxicity of MPs and HMs has been widely investigated, limited attention has been given to their combined effects on aquatic macrophytes. In this study, we aimed to evaluate the single and combined impacts of two types of MPs [polypropylene (PP) and acrylonitrile butadiene styrene (ABS)] at concentrations of 25, 50, and 100 mg L⁻¹, along with three typical HMs (Zn²⁺, Cu²⁺, and Ni²⁺), on the growth, biochemical components, and antioxidant activity of the model macrophyte Lemna gibba under laboratory conditions over a 7-day exposure period. The results revealed that both contaminants alone negatively impacted growth and biochemical performance, but the combined application caused a more pronounced decrease, suggesting a synergistic inhibitory effect on plant metabolism. The simultaneous application of ABS-MP with nickel, copper, and zinc resulted in more pronounced adverse effects on L. gibba growth parameters, photosynthetic pigments, and carbohydrate content compared to single-pollutant exposures. Co-application of copper and nickel induced pronounced oxidative stress in plant tissues, as evidenced by increased malondialdehyde levels. Furthermore, significant reductions were observed in total protein, total phenolic, and total flavonoid content across all treatments. Conversely, total antioxidant activity showed variable results dependent on the specific contaminant and concentration applied. These findings provide preliminary evidence that co-occurring MPs and HMs may exert additive or synergistic stress effects on aquatic macrophytes. In particular, the comparative evaluation of ABS and PP microplastics, along with Cu, Zn, and Ni treatments, highlights polymer- and metal-specific toxicity patterns and integrated antioxidant response profiles that have not been previously reported for L. gibba.
Insect pests pose a significant threat to agricultural productivity, making early and accurate identification essential for effective pest management. This study proposes a novel deep learning-based classification framework for multi-class pest recognition from field images. The proposed approach enhances discriminative region representation by integrating a patch embedding module, a Vision Transformer backbone, and a learnable spatial attention mask. This hybrid design enables the model to focus on critical visual cues without requiring segmentation-based preprocessing. The attention mask, learned via convolutional layers, is pooled and directly applied to Transformer-encoded patch tokens to refine spatial feature emphasis. Positional embeddings are further employed to preserve spatial context within the tokenized image representation. Experimental evaluations conducted on publicly available pest datasets with 5, 9, and 12 classes demonstrate the effectiveness and robustness of the proposed framework. The model achieves accuracies of 99.67%, 99.52%, and 97.00% on the Pest5, Pest9, and Pest12 datasets, respectively, indicating strong generalization across varying classification complexities. To enhance model transparency and reliability, visual interpretability is provided through Grad-CAM and attention heatmap visualizations that reveal the model’s focus regions. Additionally, t-SNE-based feature visualization illustrates clear separability in the learned embedding space. The proposed framework shows strong potential for practical deployment in smart agriculture and precision pest monitoring systems.
This study aimed to investigate the chemical and nutritional characteristics of bee bread (BB) samples collected from nine provinces in T & uuml;rkiye. The protein and fat contents of the BB samples ranged from 17.06% to 25.43%, and from 3.7% to 6.2%. Total carbohydrate levels and energy values varied between 70.58-77.89 g/100g DW and 407.51-422.16 kcal/100g DW, respectively. Ash and moisture contents ranged from 2.15% to 3.18%, and from 16.01% to 20.83%, respectively. Water activity of BB samples changed from 0.58 to 0.63, pH from 4.81 to 5.15, and free acidity from 279.96 to 437.32 mEq/kg. The total phenolic contents (TPC) of samples ranged from 7.26 to 12.73 mg gallic acid equivalents (GAE) per g of dry weight (DW), while their total flavonoid contents (TFC) varied between 1.31 and 5.89 mg quercetin equivalents (QE) per g DW. Antioxidant activities were determined using 2,2 '-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays, indicating high radical scavenging capacity in all samples. While phenolic compounds investigated except caffeic acid, syringic acid, and quercetin were detected in all samples, pcoumaric acid (3.26-11.23 mu g/g DW), andphydroxybenzoic acid (2.93-5.14 mu g/g DW) were the most dominant, followed by t-cinnamic acid (0.20-0.85 mu g/g DW) (P<0.05). The fatty acid composition showed regional variability. The sample from Kayseri had the highest TPC value, while antioxidant activity was highest in the samples from Istanbul and Manisa (P<0.05). The samples from Isparta and Kayseri included all phenolic compounds, while the samples from Manisa and K & uuml;tahya showed higher unsaturated fatty acid contents. The samples from Denizli had the highest fat and energy values, while the samples from Istanbul, Isparta, Erzurum, and K & uuml;tahya had the highest protein contents (P<0.05). It has been determined that the chemical and nutritional properties of BBs vary significantly depending on the harvest region and have the potential to be used as a functional food ingredient.
Efficient agricultural water management is essential to address global water scarcity. Soil moisture serves as a key indicator for irrigation scheduling, crop yield forecasting, and hydrological modeling. However, traditional in-situ measurements are costly, labor-intensive, and spatially limited. This study develops a machine learning framework for predicting soil moisture at multiple depths (20, 40, and 80 cm) using readily available environmental variables. Türkiye was selected as the study area due to its pronounced climatic and pedological heterogeneity, providing an ideal testbed for training robust models. Seven machine learning algorithms were systematically evaluated using data from 201 meteorological stations spanning 2016–2024. Extreme Gradient Boosting (XGBoost) outperformed all alternatives following hyperparameter optimization, achieving strong accuracy across all depths (R² = 0.74, 0.69, and 0.66 at 20, 40, and 80 cm, respectively). Feature importance analysis indicated a depth-dependent shift in the controls on soil moisture. Near the surface (20 cm), short-term meteorological variability and seasonal dynamics were the primary drivers, whereas deeper layers were increasingly regulated by stable soil hydraulic properties, with clay dominating at intermediate depths, and large-scale spatial gradients represented by latitude and elevation. This pattern reflects a transition from weather-driven processes at shallow depths to long-term regulation by soil structure and regional climate patterns in the subsurface. The model maintained robust performance across diverse environmental conditions, with seasonal accuracy varying by nearly 10% between winter and autumn. Spatial analysis revealed regional variations in controlling factors: soil properties (particularly organic carbon) dominated in the northern regions, while their relative importance decreased southward where climatic and temporal variables contributed more substantially, reflecting heightened sensitivity of moisture dynamics to meteorological fluctuations. The framework provides a scalable, cost-effective solution for soil moisture monitoring in data-scarce regions, supporting irrigation optimization, drought early warning, and sustainable water governance.
This study focuses on the spatially identified structural character of the landscape in the case of Bart & imath;n province in the Western Black Sea Region of T & uuml;rkiye. In the first stage, Landscape Character Types (LCTs), which are the integrated expression of reclassified climate, geology, geomorphography and landscape pattern components, were created at two levels as L1-Regional and L2-Subregional (L1: 157 types, 4018 units; L2: 449 types, 7757 units, respectively). In the second stage, the raster data for the L1-LCTs in the study area were transformed into {1(present), 0(absent)} values and PCA scores. Clustering algorithms, including twostep and k-means methods, were applied to L1-LCTs data, and clustering structures were compared across different cluster numbers. The optimal number of clusters was identified based on Silhouette index values, and the optimal cluster-based performance metrics were calculated accordingly. In this context, the analysis proceeded with 13 clusters, which achieved the highest average Silhouette index (0.231), and the resulting clustering structure demonstrated an accuracy rate of 82.8% and an overall (average) F1-score of 81.5%, supporting the validity of the classification. In the third stage, landscape diversity, landscape density, naturalness ratio, cluster ratio, relative landscape richness, average nearest neighbor distance and Shannon diversity metrics were calculated using FRAGSTATS based on the L2-LCTs included in the 13 clusters. When the clusters are examined, clusters 6 and 9, which constitute 24.67% and 17.35% of the study area respectively, have high normalized landscape naturalness rate (0.99 and 1.00 respectively) and normalized landscape diversity values (0.67 and 0.65). This study provides a robust framework for identifying spatially distinct natural and agricultural systems through structural landscape analysis. By integrating spatial typology with quantitative metrics, the method enables a consistent and diagnostic understanding of landscape variation, which can guide planning, management, and protection decisions.
With this study conducted to identify the problems affecting the productivity of exporting processing plants; export data, export problems of the processing plants having permission to seafood export to the European Union and published on the official website of The Ministry of Agriculture and Forestry in 2018, and the effects of export problems on their performances and suggestions to these problems were determined. The questionnaire could be applied to approximately 65% of the companies and empirical dimension of the subject was discussed. The obtained data was analyzed using statistical programs and interpreted. In conclusion of the evaluations, it has been revealed that 49.3% of the processing plants are medium-sized, according to Data Envelopment Analysis results, 15 enterprises, corresponding to 20.5% of the total enterprises participating in our survey, operate 100% efficiently. It has been determined that the problems identified can be experienced by enterprises in all classes. Since our understanding of quality in export is to obtain products in accordance with the criteria of the countries we export to, enterprises produce in EU standards in accordance with the legislation. There is a foreign trade surplus in the seafood export sector which reduces the country's overall foreign trade deficit. It has been determined that companies have problems such as lack of raw materials, inability to find raw materials on time, price imbalance caused by competition affects production and costs are high. It has been identified that tax policies of import country limits exports, they have difficulties to find space especially in air cargo and others. The problems of the border control points stress exporters out, the existence of bureaucratic difficulties should be resolved. It has been foreseen that organization is essential, product diversity should be increased, authorities should be more active, sector-specific loans and incentives should be created.
