Accurate and cost-effective monitoring of soil moisture content is essential for the development of precision agriculture and the optimization of water resource management. While low-frequency acoustic signals are used to detect soil moisture content, models trained with controlled laboratory acoustic data often exhibit significant performance degradation with field data due to their sensitivity to environmental noise, soil heterogeneity, and structural variations. This study proposes a novel framework to enhance model robustness and generalization by integrating a WGAN-GP network data augmentation strategy into the soil moisture content workflow. This framework is designed to generate high-quality and diverse data on different soil types under different moisture conditions, consisting of an augmented dataset with original data. The datasets are handled by different data processing methods to train the Swin-Transformer regression model. We then compare the model performance for different datasets. The baseline method achieves root mean square error (RMSE), mean absolute error, and R2 values of 6.715%, 1.829%, and 0.711 for the field test set. In contrast, the proposed generative adversarial network-augmented method reduces the field test RMSE value to 4.852% and improves the R2 value to 0.837, representing a 28% reduction in RMSE and confirming that the method proposed in this study is more robust at handling data on different soil types.
The carob tree (Ceratonia siliqua L.) is a drought-tolerant species native to the Mediterranean Basin. It has been cultivated for centuries for its highly nutritious, edible pods. Genetic diversity is one of the key requirements for the effective management and utilization of plant genetic resources. In this study, we evaluated the genetic diversity and population structure of 169 seminatural carob individuals from Lebanon, Spain, and Morocco using nine expressed sequence tag-simple sequence repeat (EST-SSR) markers. A total of eight polymorphic EST-SSR loci produced 43 alleles, with Cesi_187 and Cesi_1187 identified as the most informative markers. Results of AMOVA and SAMOVA revealed that most (>80%) of the genetic variation occurred within populations, while less than 20% was attributed to variation among populations. STRUCTURE analysis indicated three genetic clusters corresponding to the sampled countries; however, some individuals from Morocco and Spain exhibited overlapping genetic structures. The PCoA and discriminant analysis of principal components complemented the STRUCTURE results and provided further insight into the genetic differentiation among countries. Our findings may improve the effectiveness of conservation and management strategies and promote the utilization of these carob genetic resources in breeding and reforestation programs.
With the rapid advancement of agricultural modernization and the increasing demand for agricultural products, inefficient logistics distribution has become a major bottleneck in rural supply chains. This study addresses the capacitated vehicle routing problem (CVRP) in agricultural logistics. A genetic algorithm (GA)-based optimization model was proposed to enhance distribution efficiency. The model integrates critical agricultural characteristics, including multidistribution center networks, seasonal delivery schedules, and regional road infrastructure constraints, to minimize both transportation distance and operational costs. Experimental results show that the GA outperforms traditional metaheuristic methods (e.g., particle swarm optimization and simulated annealing), achieving a >5 km reduction in total delivery distance, an 11% decrease in delivery time, and a 5% reduction in path distance compared to conventional planning approaches. Notably, the hybrid GA-CVRP framework converges faster and achieves higher cost efficiency, with empirical tests validating its ability to optimize route planning under complex rural conditions. This research provides a robust, data-driven solution for agricultural enterprises to enhance supply chain resilience, reduce carbon footprints, and support sustainable rural development. By bridging AI-driven optimization and agricultural logistics practices, the study offers practical insights for deploying intelligent routing systems in global rural contexts.
Sewage sludge management represents a major challenge in Europe, carrying significant environmental and legal implications that require strong regulatory enforcement. Here, we explore the legislative measures, technological developments, and environmental impacts of sewage sludge management in Central Europe (Czechia, Slovakia, Germany, Austria, and Poland). Sludge management is regulated at the EU level through a series of directives (e.g., Council Directive 86/278/EEC, which forms the basis of national regulatory frameworks). However, national adaptations vary considerably: Croatia and Slovenia have invested in sludge phosphorus-recovery systems, while Poland and Slovakia have focused on the agricultural use of sludge as a fertilizer. Several countries, including Czechia, have extended compliance timelines for technological upgrades and tightened their microbial and heavy-metal standards beyond the EU requirements. However, persistent issues remain, such as contamination with microplastics, pharmaceuticals, and heavy metals in sludge recycling, which will require advanced treatment technologies and the introduction of new legal standards. Therefore, the management of sewage sludge as a source of fertilizer and raw materials, based on the principle of minimizing risks to human and animal health and the environment, has the potential to play an important role in implementing the circular-economy approach.
Genome editing has emerged as a transformative tool to improve crop productivity, resilience, and nutritional quality, helping address global food insecurity. Among gene-editing platforms, clustered regularly interspaced short palindromic repeats (CRISPR)/ Cas systems (notably CRISPR-Cas9) offer simple, efficient, and scalable methods for targeted modifications, including knockouts, base edits, and precise “search-and-replace” prime edits. In plants, recent advances such as improved base editors, optimized prime-editing platforms, and DNA-free delivery methods have expanded the scope of edits achievable while reducing off-target effects and regulatory concerns. This review summarizes CRISPR principles, highlights technical breakthroughs and crop applications that mitigate biotic and abiotic stresses, and outlines practical challenges and future directions necessary for responsible deployment in agriculture.
Trigonella foenum-graecum L. (fenugreek, TFG) and Asparagus racemosus Willd. (shatavari, AR) have been extensively used in Ayurvedic, Unani, and traditional folk medicine to support women's health. TFG seeds are traditionally employed as galactagogues and hormonal modulators, while AR roots are valued as rejuvenating tonics with phytoestrogenic activity, prescribed for menopausal complaints and reproductive well-being. Both plants also exhibit antioxidant and hepatoprotective effects, suggesting their potential as safe, nonhormonal alternatives to conventional hormone replacement therapy. This study investigated the dose-dependent effects of TFG and AR, administered individually or in combination, on vasomotor symptoms, hormonal balance, oxidative stress, and neuroendocrine markers in an ovariectomized (OVX) rat model of menopause. Adult female rats underwent bilateral ovariectomy and were randomized into eight groups: Control, OVX, OVX+TFG (30 and 60 mg/kg), OVX+AR (30 and 60 mg/kg), and OVX+TFG/AR combinations. Treatments were administered orally for 8 days. Tail, core, and skin temperatures were recorded to assess thermoregulation, while blood and tissue samples were analyzed for hormone levels, oxidative stress, and molecular markers. OVX rats showed classical menopausal alterations, including increased follicle-stimulating hormone, luteinizing hormone, cortisol, and malondialdehyde, and decreased 17 beta-estradiol, progesterone, dopamine, and antioxidant enzymes, along with dysregulated neuroendocrine markers. All treatments partially reversed these changes, while the high-dose combination (TFG/AR 60 mg/kg each) demonstrated the most pronounced effects, normalizing hormone levels, reducing hot-flash-like temperature fluctuations, restoring antioxidant activity, downregulating neuroendocrine markers (c-FOS, GnRH, Kisspeptin, NKB, and TRPV1) expression, and elevating brain-derived neurotrophic factor levels (p < 0.05). High-dose combined supplementation of TFG and AR effectively alleviates vasomotor disturbances, restores hormonal and oxidative balance, and modulates neuroendocrine pathways in OVX rats. These findings validate their traditional use and highlight their potential as safe phytoestrogen-based, nonhormonal alternatives for the management of menopausal symptoms, warranting further clinical evaluation.
In this study, it was aimed to predict T & uuml;rkiye's furniture exports for the period from January 2010 to May 2025 using machine learning methods based on macroeconomic indicators. The dataset consisted of 10 economic variables, including the furniture industry production index, capacity utilization rate, import volume, total exports, real effective exchange rate, producer price index, money supply, and oil prices, with 185 observations per variable and a total of 2035 data points. During the data preprocessing phase, no missing or outlier values were detected, and the data were divided into 70% training and 30% testing subsets. Decision tree, random forest, and deep learning models were developed using the software RapidMiner Studio, and hyperparameter optimization was performed through the grid search method. Model performances were evaluated using root mean square deviation, mean absolute error, mean absolute percentage error, and R2 metrics. The results indicated that all models predicted furniture exports with high accuracy. The best performance was achieved by the random forest model, with R2 = 0.977 and MAPE = 5.28% in the testing phase. Furthermore, the variable importance analysis based on the random forest model revealed that the furniture industry production index and capacity utilization rate were the most significant determinants of exports, highlighting the production-driven nature of the sector. These findings demonstrate that machine learning methods can be effectively used in forecasting economic indicators and that T & uuml;rkiye's furniture exports can be reliably predicted through data-driven approaches.
Fluvisols develop in river valleys through the accumulation of alluvial sediments. They are characterised by high fertility and are extensively used for agriculture. This study aimed to identify the core microbiome of fluvisols in the Vistula Valley and to examine its relationship with the basic physicochemical properties of the soils. Six types of fluvisol (very light, light, medium, and heavy) from four locations in Lublin province were analysed, with samples collected in 2018 and 2022. The microbiome structure was determined by sequencing the V3-V4 region of the 16S rRNA gene and was compared with soil parameters such as pH, electrical conductivity, organic matter, nitrogen, carbon, and metal contents. A core microbiome, dominated by Acidobacteria_Gp6 (ASV_018) and Rhizobiales (ASV_001), was identified in all samples. Together with Actinobacteria and Proteobacteria, these taxa perform key ecological functions, including nutrient cycling, supporting plant growth, and maintaining soil ecosystem stability. The results confirm the hypothesis that specific bacterial groups within the core fluvisol microbiome contribute to its high quality and agricultural suitability. The data provide a basis for further research into the functional role of the floodplain soil microbiome and its resilience to periodic flooding.
To address the shortcomings of conventional agricultural statistical and monitoring methods on a regional scale, this paper proposes the use of artificial intelligence-driven remote sensing data for analysing crop growth patterns and the economic benefits of grain. Winter wheat data from three stations in a single province from 2011 to 2020 was used, with the leaf area index (LAI) used as the key crop growth indicator value. The simulated annealing algorithm was used to assimilate the LAI and remote sensing leaf area index (MODIS-LAI), simulated by the World Food Study (WOFOST) simulation model, to carry out simulation analysis of winter wheat. The R2 between the simulated and measured values was above 0.70 for 10-year seedling, flowering, and mature stages, and a series of indicators were used to evaluate the effectiveness of the model simulation. The root mean square error (RMSE) suggests a better performance of the simulated seedling stage than the flowering and mature stages, and the difference between the measured and simulated winter wheat yields at the three stations from 2011 to 2020 is relatively small. The simulation results of the model can be further used for the analysis and application of assimilating MODIS-LAI for yield estimation, improving the accuracy of yield prediction of the model.
This two-year field experiment evaluated the effects of different organic fertilizers on olive oil quality of the Gemlik cultivar grown under semiarid conditions in Mardin, T & uuml;rkiye. Treatments of olive pomace (P1: 4, P2: 8, P3: 12 kg tree-1), leonardite (L1: 1.5, L2: 3.0, L3: 4.5 kg tree-1), and vermicompost (SG1: 3, SG2: 6, SG3: 9 kg tree-1) were applied in a randomized complete block design with three replicates. Oils were analyzed for free acidity (FA), peroxide value, and phenolic profile using high-pressure liquid chromatography. FA ranged from 0.27% to 0.42% (as oleic acid), remaining well below the International Olive Council extra virgin limit; the highest mean value occurred in L2, whereas lower means were found in P2, P3, L3, and SG2. Mean total phenolic content was 307 mg L-1, and several treatments (P2, L1, SG3, P3) exceeded this average. Two-way analysis of variance revealed significant effects of treatment, year, and their interaction on multiple parameters, indicating the influence of both fertilization and climatic variability. The findings highlight that quality assessment based solely on FA may overlook treatment-related differences, suggesting that phenolic and oxidative indices should also be considered. As the study was limited to a single cultivar, site, and two growing seasons, further research across diverse genotypes and environments is required before broader policy implications are drawn.
Chromium (Cr6+) toxicity is a significant environmental stress factor that severely hampers plant growth and productivity. This study aimed to evaluate whether the combined application of phosphorus fertilizer and melatonin could alleviate chromium-induced stress in maize (Zea mays L.). A foliar spray of melatonin (50 & micro;M) was applied alongside two phosphorus sources-phosphoric acid (PA) and diammonium phosphate (DAP), each at 60% of the recommended rate. Two maize cultivars (FH-1046 and YH-1898) were grown under chromium stress (0, 15, and 30 mg kg-1) to assess treatment effects. The results demonstrated that FH-1046 plants treated with PA + melatonin under noncontaminated conditions showed a significantly higher transpiration rate (44%) compared to YH-1898 under chromium stress. Additionally, the combination of DAP + melatonin under 30 mg kg-1 Cr significantly improved fresh and dry biomass by 20% and 30%, respectively. These findings suggest that foliar-applied melatonin in combination with phosphorus fertilization can enhance physiological performance and biomass accumulation, offering a potential strategy to improve maize resilience and yield under chromium-contaminated soils.
Crop diversification strategies are being put forward to mitigate the negative effects of climate change associated with greenhouse gas emissions, and thus to ensure sustainable food production that is resilient to climate change and to reduce the risks faced by producers. By diversifying their agricultural products producers can insure themselves against the risks associated with climate variations, disease, and pests, contributing to both continuous food production and income flow. The present study investigates the effect of climate change on enterprise income in the Yeni & scedil;ehir district of Bursa province-a region noted for its intensive production of vegetables and other crops. To this end, 352 surveys were conducted in 20 villages that were selected using a simple random sampling method. The obtained data were subjected to a multinomial probit analysis using the R statistical package to determine the factors affecting product diversification, while regression and treatment-effect analyses were conducted to determine the factors affecting business profitability. Multiple regression and multinomial probit analyses allow management production decisions to be understood and interpreted by organizing them based on their multidimensional and integrative nature. It was determined that larger enterprises and those with agricultural insurance were more likely to adopt crop diversification strategies than smaller holdings with no insurance, and that accounting and record-keeping remained generally low. Furthermore, a positive relationship was observed between enterprise income and the age and education level of the enterprise owner, the number of days of foreign labor used, and the number of household members. The results highlighted the need to increase agricultural insurance support, expand land consolidation to enhance farm size, establish an online platform for seasonal labor, and develop loan and grant programs tailored to the demographic profile of local enterprises.
Erwinia amylovora is the causal agent of fire blight, which is known as one of the most destructive diseases of apple. The use of fire blight-resistant cultivars/rootstocks is one of the most effective methods available to manage this disease. We previously selected 199 local apple cultivars that showed no clearly visible symptoms of fire blight on the plants from the coastline of the eastern Black Sea Region in T & uuml;rkiye. The region is characterized by the highest rainfall and humidity levels in the country. In this research, young plants of these genotypes grafted onto MM.106 rootstock were screened for fire blight disease resistance in the greenhouse and under shade cloth outdoors at the same location over 3 consecutive years. The susceptible apple cultivar Royal Gala on MM.106 and the rootstock M.9 were used as controls. The plants were inoculated with three highly virulent E. amylovora strains. The genotype-by-strain interactions were evaluated. The current season's shoot length and the length of the visible necrotic lesions were measured, percent disease severity (DS) was calculated, and the genotypes were grouped into four susceptibility classes. The results showed that 18 genotypes (9.1%) were consistently found to be resistant (R) and/or moderately resistant (MR) to both Ea43b-3 and Ea186A strains in the first 2 years and to the EaBSR14 strain in the 3rd year. DS levels ranged between 0.0% and 25.0% in selected genotypes while Royal Gala and M.9 had 50.7%-70.2% and 84.8%-100%, respectively. Multidimensional scaling analysis distinctly separated all of the R/MR genotypes from the sensitive control genotypes. The data showed high levels of diversity in terms of fire blight resistance among the local apple genotypes in the eastern Black Sea Region. The selected genotypes constitute a valuable genetic source for developing fire blight resistant apple cultivars/rootstocks in breeding studies.
Arbuscular mycorrhizal fungi (AMF), which exist symbiotically with plant roots, affect plant growth, yield, and fruit quality. The effects of AMF vary depending on the cultivar and mycorrhizal type. In this study, the effects of different AMF types on the Rubygem strawberry cultivar, widely grown in T & uuml;rkiye, were examined. Gigaspora margarita and Funneliformis mosseae AMF species were applied to plant roots at seedling planting. Fruit quality criteria, including yield, acidity, soluble solid content, phenolic compounds, organic acids, crown and root development, and the number of daughter plants and stolons, were examined. The highest yield was 431.02 g in G. margarita. Funneliformis mosseae was the AMF species with the highest soluble solid content (11.7%) and titratable acidity (0.85%). Gigaspora margarita was prominent in root development, and F. mosseae was prominent in crown development. The highest amounts of ascorbic acid (30.23 mg 100 g-1) and malic acid (551.38 mg 100 g-1) were recorded in the G. margarita type. This species had the highest amounts of gallic (25.385 mg 100 g-1) and chlorogenic (19.477 mg 100 g-1) acids. As a result of the research, it was concluded that G. margarita was more suitable for the development and quality parameters of the Rubygem strawberry cultivar.
In this study, tyrosinase enzyme was cross-linked with glutaraldehyde to form aggregates on the surface of activated magnetite nanoparticles, without the need for a separate support matrix. For the first time, this novel aggregate was employed for the spectroscopic detection of ascorbic acid (AA) and diazinon, as model food additives and pesticides, respectively. This technique, developed as an alternative to optical sensors, not only eliminates their drawbacks but also facilitates the recovery of cross-linked enzyme aggregates (CLEA) from the reaction medium using the incorporated magnetite nanoparticles. A spectrophotometric technique was used to measure the enzyme activity of the aggregates to assess their reusability and storage stability. Additionally, key parameters, including component ratios (enzyme-to-magnetite ratio, glutaraldehyde concentration) and operational conditions (pH and temperature), were optimized. The detection of AA and diazinon was achieved by quantifying the decrease in enzyme activity. The findings demonstrate that these support-free aggregates provide a highly cost-effective platform for enzyme activity-based analyses. This approach also shows great promise for detecting other analytes that may alter enzyme activity.
The increased reuse of treated wastewater (TWW) for irrigation in arid and semiarid regions raises environmental concerns regarding microplastics (MPs), specifically their capacity to adsorb and transport heavy metals. This study assesses the quantity of MPs and associated metals (Cd, Cr, Cu, Pb, Ni, and Co) in agricultural soils irrigated with TWW in the Konya region of T & uuml;rkiye. To this end, a total of 202 soil samples were collected from TWW-irrigated (180) and nonirrigated control fields (22) at two depths (0-10 and 10-20 cm). MPs were extracted from the samples using a density separation technique, identified microscopically, and analyzed for metal adsorption using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Contamination and risk were assessed using the geoaccumulation index (Igeo), contamination factor (CF), enrichment factor (EF), potential ecological risk index (ER), pollution index (PI), and Nemerow pollution index (NPI). MP abundance was observed to be significantly higher in the TWW-irrigated soil samples (100-840 MP/kg at 0-10 cm; 80-660 MP/kg at 10-20 cm) than in the controls (100-220 and 80-240 MP/kg, respectively) (p < 0.05). Cd (0.41 mg/kg) and Cr (12.46 mg/kg) were the most abundant metals associated with MPs. Igeo and CF indicated moderate to heavy contamination, while the ER values revealed Cd to be the dominant ecological risk factor. EF and PI suggested minor to moderate enrichment with localized Cd and Pb hotspots. NPI values were indicative of high to very high overall heavy-metal pollution. Overall, the results reveal MPs to be both persistent pollutants and vectors, facilitating the vertical migration of metals and posing long-term ecological risks in TWW-irrigated agroecosystems.
This paper integrated multimodal remote sensing (RS) data with deep learning to develop a maize growth analysis and income prediction model based on CNN (Convolutional Neural Network)-Attention and Bi-LSTM (Bidirectional Long Short-Term Memory). Utilsing Landsat 8, Sentinel-2, and Sentinel-1 satellite data, the CNN-Attention network extracts vegetation features such as NDVI (Normalised Difference Vegetation Index), EVI (Enhanced Vegetation Index), and canopy density for corn growth stage identification and prediction. The study combined these features with meteorological, soil, and market data and fed them into a Bi-LSTM model for time series analysis to forecast corn income. The method achieved 98.3% accuracy in growth stage classification, with an RMSE (Root Mean Squared Error) of 0.04 and R2 of 0.94 for canopy coverage prediction under 10-fold cross-validation, and an RMSE of 0.13 and R2 of 0.94 for income prediction, showing high stability over time. Overall, this research supports data-driven precision agriculture through real-time monitoring and reliable economic forecasting.
Drought stress (DS) is a major constraint on optimal plant growth and yield. Agricultural researchers have been actively exploring fertilizers that can enhance crop production under DS conditions. Silicon (Si) application has emerged as a promising strategy for managing DS in various crops. This study investigated the potential of Si soil drenching to mitigate the adverse effects of varying levels of DS (at 100%, 70%, and 40% field capacity) in wheat. Two varieties (Ujala-2016 and FSD-2008) were used to evaluate the influence of Si (2.5 mM) on antioxidant activity, oxidative burst, secondary metabolites, growth, and yield. The results indicated that Si drenching positively affected all growth parameters, yield attributes, antioxidant enzymes, and secondary metabolite production. However, malondialdehyde (MDA) production increased significantly under DS, with the severity of DS and varietal differences influencing its accumulation. The Ujala-2016 variety had superior growth, biological yield, 1000-grain weight, spike length, number of tillers, photosynthetic efficiency, antioxidant levels, osmotic adjustment, and secondary metabolite production when treated with Si. These findings suggest that Ujala-2016 is a more drought-tolerant variety. While increasing DS severity negatively impacted yield, Si treatment can be considered a valuable soil amendment for mitigating the effects of drought and ensuring food security in arid and semiarid regions.
As vital components of urban ecosystems, urban green spaces directly affect soil fertility and plant growth through nitrogen transport dynamics. This study addresses the existing methodological gaps in understanding nitrogen cycling within urban green spaces, particularly the unclear interaction mechanisms between vegetation structure and soil physicochemical characteristics. The study was conducted in the Green Expo Park located in Nanjing, China. Using a stratified sampling approach, physicochemical indicators of soil layers were measured and combined with community parameters-including leaf area index, vegetation coverage, and plant density-to identify key drivers through correlation and multiple regression analyses. The regulatory mechanisms of soil physicochemical properties and vegetation traits on the vertical distribution of nitrate nitrogen and ammonium nitrogen were systematically examined using interaction effect tests. Results indicate that soil nitrate nitrogen content decreases from 12.66 mg/kg to 3.42 mg/kg with increasing depth, primarily influenced by the inhibitory effect of soil bulk density and the promoting effect of leaf area index (beta = 2.32). In contrast, the distribution of ammonium nitrogen is significantly influenced by the interaction between soil moisture content and vegetation coverage (p < 0.05). Vegetation structure plays a significant regulatory role in nitrogen transport, and communities with a high leaf area index effectively enhance soil nitrogen-use efficiency. This study identifies the key driving factors and mechanisms governing nitrogen transport in urban green space soils, providing a theoretical foundation for scientific management and community optimization.
The aim of the present research was to evaluate, for the first time, the effect of ozonated water treatments on the postharvest quality of capia pepper (Capsicum annuum L. 'Kaptan'). The peppers were harvested at the optimum time and transported to the laboratory immediately. Uniform and quality fruit was selected and precooled. After precooling, the fruit was divided into four groups. The first three groups were immersed in ozonated water at three different concentrations (1.0, 2.0 and 3.0 ppm) for 15 min. The last group (control) was immersed in distilled water for 15 min. The peppers were kept at room temperature and humidity for 30 min to remove excess water, then placed in modified atmosphere bags. The fruit was stored at 8 +/- 1 degrees C and 90 +/- 5% relative humidity for 25 days. Various quality attributes and the microbial load of the peppers were analyzed during storage. Although high-dose ozone treatments had negative effects on the decay rate of the peppers, good results were obtained in terms of reducing the microbial load. The lowest concentration (1.0 ppm), was determined as the most effective treatment for reducing weight loss and respiration rate, and preserving fruit firmness and sensory quality. The peppers treated with 1.0 ppm ozonated water could be stored for 20 days at 8 +/- 1 degrees C, while the control treatment was limited to 15 days.
