Günümüzde sıkı çevre düzenlemeleri, sürdürülebilirlik politikaları ve artan tüketici sağlığı bilinci, sanayileri atık yönetimi konusunda daha sorumlu davranmaya yönlendirmektedir. Süt endüstrisinin yüksek organik içeriğe sahip bir yan ürünü olan peyniraltı suyu (PAS), geleneksel olarak çevresel bir atık olarak değerlendirilse de biyoteknoloji, enerji üretimi ve gıda sanayiinde yenilikçi uygulamalarla değerlendirilmeye başlanmıştır. PAS’ın yüksek biyokimyasal oksijen ihtiyacı (BOİ) ve kimyasal oksijen ihtiyacı (KOİ) nedeniyle doğrudan çevreye deşarj edilmesi ciddi kirlilik sorunlarına yol açmaktadır. Bu kapsamda, sürdürülebilir PAS yönetimi için biyoteknolojik ve çevre dostu çözümler geliştirilmiştir. Bu çalışma, PAS’ın atık yönetimi süreçlerine entegrasyonunu sağlayan teknolojik yenilikleri araştırmaktadır. Özellikle biyogaz, biyoetanol ve biyoplastik üretimi gibi enerji geri kazanım yöntemleri, PAS’ın ekonomik değerini artırırken çevresel etkilerini azaltmaktadır. Ayrıca PAS, fonksiyonel gıda üretimi ve yenilebilir film kaplamaları gibi uygulamalarda kullanılarak katma değerli ürünlere dönüştürülebilmektedir. Bunun yanı sıra, PAS’ın biyoteknolojik yöntemlerle değerlendirilmesi, sürdürülebilir gıda üretimi ve ekonomik sürdürülebilirlik açısından önemli bir potansiyel sunmaktadır. Bu çalışmanın temel hedefleri; süt sektörü atıklarının çevresel etkilerini değerlendirmek, PAS’ın besinsel ve sağlık açısından sunduğu çok fonksiyonlu faydaları incelemek ve PAS’ın çeşitli gıda ve biyoteknolojik ürünlere entegrasyonunu sağlayan sürdürülebilir yaklaşımları tartışarak endüstriyel uygulamalara katkılarını vurgulamaktır. Bu doğrultuda, PAS’ın değerlendirilmesine yönelik yenilikçi yaklaşımlar ele alınarak, çevresel, ekonomik ve endüstriyel avantajları kapsamlı bir şekilde analiz edilmiştir.

Dünya nüfus artışının tarımsal üretime etkileri ile bitki hastalıklarının bu üretim üzerindeki olumsuz etkileri, günümüzde giderek daha fazla önem kazanmaktadır. Her yıl, küresel ürün kayıplarının %20-40’ı hastalıklardan meydana gelmektedir. Tarımsal üretimde verim ve kaliteyi artırmak için hastalık etmenlerinin hızlı ve doğru bir şekilde tanısı gereklidir. Özellikle Pythium cinsi oomiset’ler, bitkilerin köklerinde ve farklı dokularında önemli zararlara yol açarak ekonomik kayıplara neden olmaktadır. Morfolojik tanı yöntemleri zaman alıcı ve araştırmacılar için zorluklar içerirken, moleküler teknikler (örneğin, Real-time PCR, LAMP) duyarlılık ve özgüllük sunarak kısmen daha etkili çözümler sağlamaktadır. Sonuç olarak, tarımsal üretim için ciddi sorunlara neden olan Pythium türlerinin etkilerini azaltmak, erken teşhis ve mücadele yöntemlerinin geliştirilmesiyle mümkün olacaktır. Bunun için hem moleküler hem de immünolojik yöntemlerin entegre bir şekilde kullanılması, bitki hastalıklarının kontrolünde daha etkili yöntemler geliştirilmesine yardımcı olacaktır.

Kestane hortumluböceği Curculio elephas (Gyllenhal) (Coleoptera, Curculionidae) ve Kestane içkurdu Cydia splendana (Hübner) (Lep.: Tortricidae) kestane üretiminde kalite ve verim kayıplarına neden olan önemli zararlılardır. Aydın ve İzmir illerinde 2021-2023 yılları arasında yapılan bu çalışmada, hasat olgunluğundaki kestane meyvelerinde C.elephas ve C. splendana zararları incelenmiştir. Bu amaçla farklı yöresel ve tescilli çeşitlerden hasat sonrası meyve örnekleri alınmıştır. Meyvelerdeki genel bulaşıklılık oranı düşük olmasına (Aydın %9,44; İzmir %12,83) karşın bazı bahçelerde zararın %80 seviyelerine çıktığı görülmüştür. İki türün toplam zararı, 2021, 2022 ve 2023 yıllarında sırasıyla Aydın’da %14,58, %5,83, %7,93 ve İzmir’de %18,60, %13,47, %6,43 olmuştur. Zararda C. splendana’nın payı C. elephas’ın payından büyük bulunmuştur. Bahçelerde en yüksek C.elephas ve C. splendana zararı sırasıyla %30 ve %50 olarak saptanmıştır. C. elephas zararı Aydın’da %0,60-6,75, İzmir’de %1,89-9,17; C. splendana zararları Aydın’da %3,58-7,58; İzmir’de %3,00-9,69 arasında gerçekleşmiştir. En fazla zarar Beydağ ve Ödemiş ilçelerinde meydana gelmiş, onları Nazilli ilçesi izlemiştir. Zarar oranı kestane çeşitine göre değişmiştir. En fazla zarar tescilli çeşitler İbrahimbey Işıklar (%28,18) ve Karasu’da (%12,00); yöresel çeşitlerden (genotipler) ise Salman (%80,00), Deli (%39,50) ve Sarıaşı’da (%28,70), en az zarar Sarıaşlama (%8,00) ve Tahtakaraaşı (%8,22) çeşitlerinde belirlenmiştir. Tapanaşı çeşidinde zarar görmüş meyveye rastlanmamıştır.

The study produced milk drink that is high in protein and energy from soy beans and tiger nuts blends as well as to assess the beverage qualitatively and customer acceptability using response surface methodology (RSM). The principal raw materials used were soybeans and tiger nut. They were processed into milk at different formulations without using chemical preservative. Sensory evaluation, pH, titratable acidity as well as proximate and mineral compositions were determined. The results showed that the minimum and maximum colour score varied from 4.4 to 5.0. The quadratic model for colour was found to be significant (p<0.05) in tiger nut inclusion. The model equation obtained for colour = 4.50 + 0.048*A + 0.098*B + 0.019*A2 + 0.12*B2 + 0.18*A*B where A = Soybeans, B = Tiger nut. Mouth feel model’s F-value of 7.35 indicates that it was significant (p<0.05). Model terms are considered important when the “Probability > F” value was less than 0.05. The model for quadratic equation for mouth feel was 7.290. +5.00 -0.091*A -0.19*B -0.14* A2 -0.44* B2 +0.45* A*B. The model for the quadratic equation for Taste = +3.90 +0.030*A +0.020* B +0.21*A2 +0.056*B2 +0.075*A*B adequate precision = 3.65, corrected R2 = 0.29, R2 = 0.59 and the predicted R2 = -1.94. It was observed the pH values decreases with storage days from day 0 to day 8 for samples A, I and Z.  On day 12, the pH values increased for all the samples except for sample M.  The Titratable acid increased as the pH decreased. On the 0 day, sample A had value of 20.8 while sample M was 23.33. However, sample I increased from 0, 4, 8 and 12 days (20.19, 20.20, 22.47 and 23.73) respectively. Sample M had the highest protein content (3.52%), followed by sample A (3.21%), sample I (3.01%) and sample Z (2.02%). Sample Z had the lowest fiber content (0.30%), next was sample I (O.43 %), sample A (0.51%), and sample M (0.54%). The range of moisture contents was 81.03 to 82.04%.  Conclusively, optimizing soybeans at 100g/ml with 40 g/ml of tiger nut could help to improve the sensory attributes such as colour, taste, mouth feel, flavor and overall acceptance. The formulation could influence shelf stability of product, bridge protein in-balance and reduction in malnutrition level.

Promoting rooftop farming using suitable grow bag media is of utmost importance in densely populated cities like Lalitpur to sustain the human race. To assess the productivity and other economic characteristics of bell peppers, an experiment was conducted from Feb 2022 to Jun 2022 in different grow bag media on the premises of Vegetable Crop Development Centre, Khumaltar, Lalitpur. The experiment was conducted in Randomized Complete Block Design (RCBD), with four replications and five treatments with growing media: Coco peat: vermicompost (1:1), Soil: sand: FYM (Farmyard Manure) (1:1:1), Soil: Sand: FYM: Vermicompost (1.25:0.75:0.5:0.5), Soil: vermicompost (1:1), and Soil: ash: FYM (1:0.5:1) on the cultivar California Wonder. Growth parameters like plant height (47.865 cm), number of leaves (50.6), number of branches/plant (9.5), and stem diameter, and yield parameters like total fruit weight (1693.93 gm), average fruit weight (84.68 gm), number of fruits (42.75), fruit diameter (6.37 cm), and fruit length (7.22 cm) were observed during the experiment. The media consisting of T3 (Soil: Sand: FYM: Vermicompost (1.25:0.75:0.5:0.5)) proved to be statistically superior over the rest of the combinations for almost all aspects under investigation, whereas comparatively poor performance was observed in Coco peat: vermicompost (1:1) media. This study suggests that the grow bag media of Soil: Sand: FYM: vermicompost (1.25:0.75:0.5:0.5) gave the highest gross return and net return, with the highest B: C ratio (2.08) observed in the grow bag media of Soil: Sand: FYM (1:1:1).

The objective of this study was to evaluate the effects of palm oil, African walnut oil and their blends on hematological, inflammatory, and some oxidative stress markers in high fat diet (HFD) obese-induced Wistar rats. Obesity was induced for 60 days and treated for 28 days using edible oils [palm oil, African walnut oil, palm oil: African walnut oil (50:50) and palm oil : African walnut oil (60:40)] and orlistat (10 mg/Kg). Thereafter the animals were sacrificed, blood was collected for hematological studies and the preparation of the serum, while the organs harvested were used to prepare organ homogenates. Serum and organ homogenates were used for the evaluation of inflammation and oxidative stress markers. Results showed that the oils utilized were confirmed to be of high quality through their good stability indices (peroxide value: 2.52-3.87meq O2/Kg; p-anisidine value: 8.24-12.33, TOTOX value: 13.37-19.46,). Looking at the haematological study, animals that received the HFD presented the lowest (p<0.05) hematocrit and Platelet. PO:WO (50:50 and 60:40) significantly (p<0.05) decreased the granulocytes concentration in the blood of rats. PO:WO (50:50) significantly (p<0.05) increased the lymphocyte concentration while 100% PO increased the mid-size white blood cells level in the animals. Serum levels of inflammation markers were higher (p<0.05) in the negative control group (354.44-385.82 pg/mL) compared to the other groups (147.22-271.55 pg/mL). The analysis of oxidative stress parameters revealed that the administered oils and orlistat generally exhibited good protections compared to the normal and negative control groups, which might be due to the presence of omega-3 fatty acids and bioactives such as β-carotene and vitamin E which have good antioxidant and anti-inflammatory properties. It can be concluded that these oils have a role in protecting against obesity through their effects on oxidative stress, hematology, and inflammatory cytokines.

Developments in Geographic Information Systems and Remote Sensing (RS) technologies and innovative approaches emerging in deep learning (DL) supported analysis methods have an important place in disaster research as in every field. Convolutional neural networks such as Mask RCNN, U-NET, one of the deep learning methods for disaster damage impact assessment and classification, have started to show successful results. However, high-resolution geospatial imagery and drones provide faster and more accurate detection of structural damage.  In this study, damaged building detection was performed using Göktürk-1 satellite images from 6 February 2023 using Mask-RCNN architecture. In this study, deep learning methods were used to detect the collapsed buildings in the city of Malatya during the 6 February 2023 earthquakes. The study aims to emphasize the significance of GIS and remote sensing for the timely and efficient evaluation of building damage after a disaster. Considering this, high quality images of Malatya city before and after the earthquake were analyzed and data sets were created by masking using Mask RCNN deep learning method through ArcGIS Pro 3.4.0 software. According to the results of the research, it quickly detected damaged buildings with an accuracy rate of 70% according to satellite images after the earthquake. As a result, GIS and deep learning models were used to detect and map the initial damage after the earthquake.

Drought is regarded as one of the most significant abiotic constraints to agricultural crop output worldwide. Drought in the spring and early summer, which coincides with important reproductive stages, severely limits lupin yield in Mediterranean climate zones. The purpose of this study was to determine how different drought treatments affected seed germination and initial seedling growth in Lupinus albus L. (white or field lupin). Seed germination parameters and initial seedling growth traits were tested against five levels of drought stress induced with Polyethylene glycol-6000 (PEG6000) at concentrations of 0, 4, 8, 12, and 16%. An experiment with four replications was conducted using a completely randomized design. The results revealed that the negative effect of drought stress started at 4% (-0.03 MPa or -0.3 bar) treatment for the initial seedling growth stage; whereas, it started at 12% (-0.2 MPa or -2 bar) treatment for the germination stage. Therefore, it was determined that L. albus was more sensitive to drought stress at the initial seedling growth stage than at the germination stage. However, it was observed that the growth parameters were more sensitive in shoot growth than in root growth to drought stress. There will be a sharp loss of yield in soils with levels of drought stress imposed by 12% PEG6000 (-0.2 MPa-moderate drought-) and beyond. Therefore, it is likely that L. albus has low drought tolerance.

Okra (Abelmoschus esculentus) cultivation in regions like Ramechhap, Bagmati Province, Nepal faces challenges in improving yield and economic returns due to suboptimal agronomic practices. To address this issue, a field experiment was conducted to assess the effects of pinching and micronutrient sprays (zinc and boron) on growth, yield, and economic returns of okra. The study aimed to evaluate the impact of pinching and zinc-boron foliar application on key growth parameters, including the number of leaves, fruit length, number of fruits per plant, and total yield. Pinching significantly increased the number of leaves and fruits per plant, as well as the total yield. The application of 0.25% Zn + 0.25% B resulted in the highest fruit length, fruit weight, and total yield. The combined treatment of pinching and Zn-B spray (P1M3) recorded the highest fruit length (11.13 cm), number of fruits per plant (21.91), and yield (17.68 t ha-1). Economic analysis revealed that this combination resulted in the highest net return (NRs. 774,869.35 ha-1) and the highest benefit-cost ratio (B:C = 1.95), indicating that it could significantly improve farm income for smallholder farmers in the region. These results demonstrate the potential of integrated agronomic practices, including pinching and micronutrient application, to enhance both productivity and profitability in okra cultivation.

Agricultural lands are known not only as agricultural production areas but also as areas with high income expectations as an investment tool. In Turkey, recent fluctuations in economic indicators such as the euro, dollar, and gold, along with increasing investment demand, have caused agricultural land prices to rise uncontrollably. Controlling land price increases is important for preventing the misuse of agricultural lands. The sustainable management of agricultural lands and price stability are closely related to the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 and 15, “Sustainable Cities and Communities” and “Life on Land.” In this context, accurately predicting prices is important for minimizing price fluctuations in agricultural lands for investors and landowners and supporting sustainable development. In general, the Multiple Linear Regression (MLR) model is considered one of the effective traditional methods for predicting real estate prices. However, depending on the data, more reliable results can be obtained than with powerful deep learning models such as the Extreme Gradient Boosting (XGBoost) algorithm, which exhibits superior prediction performance. This study aims to compare the MLR and XGBoost algorithms to predict agricultural land prices in villages located in the central district of Çanakkale and to examine daily fluctuations in economic indicators such as the dollar, gold, and euro. The results showed that XGBoost (R2 = 0.66) has an advantage in terms of coefficient of determination values compared to MLR (R2 = 0.01). Accurate price prediction for agricultural lands will help control fluctuations in land prices. Additionally, it will support farmers and investors in making informed decisions for a sustainable agricultural economy.