This research examines the impact of treated and untreated wastewater on eggplant cultivation, focusing on the aroma profile and phenolic composition. The results demonstrate significant alterations in dry matter content, pH levels, and total phenolic compounds in eggplant varieties irrigated with wastewater, compared to tap water. Regarding total phenolic content, the highest result was 120.14 mg kg–1 in the Kemer variety irrigated with wastewater. The amount of water-soluble dry matter in eggplant varieties irrigated with physical treatment (WW1), physical + biological treatment, and municipal (tap) water (MW) was found to vary between 7.80% and 5.20%. The aromatic analysis identifies variations in volatile compounds with higher concentrations of specific components, such as farnesene <(E, E)- alpha-> (30.60%), benzene (22.05%), and non-(2E)-enal (26.89%), under different qualities of water irrigation treatments. An increase in the purification level of irrigation water increased the percentage of farnesene <(E, E)-, alpha->, ranging from 15.13% (Aydın Siyahi, WW1) to 30.60% (Kemer, MW). The results underline the importance of sustainable water management practices and highlight the need to quality of irrigation water in agricultural areas to ensure soil health, environmental sustainability and food safety.

Accurate identification of cattle is essential for monitoring ownership, controlling production supply, preventing disease, and ensuring animal welfare. Despite the widespread use of ear tag-based techniques in livestock farm management, large-scale farms encounter challenges in identifying individual cattle. The process of identifying individual animals can be hindered by ear tags that fall off, and the ability to identify them over a long period of time becomes impossible when tags are missing. A dataset was generated by capturing images of cattle in their native environment to tackle this issue. The dataset was divided into three segments: training, validation, and testing. The dataset consisted of 15 000 records, each pertaining to a distinct bovine specimen from a total of 30 different cattle. To identify specific cattle faces in this study, deep learning algorithms such as InceptionResNetV2, MobileNetV2, DenseNet201, Xception, and NasNetLarge were utilized. The DenseNet201 algorithm attained a peak test accuracy of 99.53% and a validation accuracy of 99.83%. Additionally, this study introduces a novel approach that integrates advanced image processing techniques with deep learning, providing a robust framework that can potentially be applied to other domains of animal identification, thus enhancing overall farm management and biosecurity.

Effects of Different Storage Conditions on the Nutritional Qualities of Barley and Maize

In recent years, treating and reusing polluted water for agricultural irrigation has become essential to ensuring water and food sustainability. In addition to the factors affecting human health in vegetables and fruits irrigated with treated wastewater, factors affecting consumer preferences, such as flavor and phenolic compounds, should also be examined. This study investigates the effect of treated wastewater irrigation on the aromatic compounds and phenolic composition of capia pepper, which holds a significant position and is extensively used in various food products in the food industry. Drip irrigation with treated and untreated wastewater from the Kalecik Wastewater Treatment Plant was applied to two pepper varieties in the Kalecik district of Ankara, Türkiye. This research found that wastewater irrigation impacted certain aroma components, including para-dichlorobenzene, alpha-cubebene, hexanoate, alpha-farnesene, limonene, isoamyl butyrate, squalene, and alpha-copaene, which contribute to the distinct aroma and fragrance of capia peppers. Total phenolic content, pH, and soluble solids were found to be high in peppers irrigated with wastewater, and it was observed that these parameters increased as the treatment levels of the wastewater decreased. The highest results were obtained in capia peppers irrigated with wastewater. Results indicate that heavy metal levels in peppers align with permissible limits, confirming the usability of both water sources. In the face of global water scarcity and the challenge of feeding an ever-growing population, studies like this offer valuable insights into sustainable and well-informed agricultural practices.

Susurluk Basin is among the basins that may be most affected by drought risk due to its agricultural, economic, and natural resources. In this study, regional hydrological drought risk models were developed for water supply systems in the Susurluk Basin, Turkey. Twenty-four flow observation sites with 25 years or more of data showing natural flow characteristics as much as possible were converted into daily flow data with Q₇, Q₁₅, Q₃₀, and Q₆₀ low-flow indexes. Regionalization was carried out by two-stage multivariate cluster and principal component analysis using the basins’ physical and hydrological characteristics and low-flow statistics, and two homogeneous regions were obtained due to the discordancy, heterogeneity, and goodness of fit tests, which are L-moment approaches. Regional models were performed with ordinary and principal component regression techniques using the physical and hydrological characteristics of the watersheds and regional low-flow frequency analysis. The cross-validation procedure results for ungauged basins show that ordinary regression models are more effective in the lowland first region. In contrast, principal component regression models are more suitable for the mountainous second region. This study’s findings, which are a first for the Susurluk Basin, will have important results in terms of agricultural water management in the region and will help the water authority in water allocation. To investigate whether human impact and climate change impact the prediction of hydrological drought, we recommend seasonal non-stationary frequency analysis with the addition of useful empirical hydrological drought indexes.