Koruyucu tarım, girdileri koruyarak doğal kaynaklar üzerindeki etkiyi en aza indirip, tarımsalişlemleri kolaylaştırdığından dolayı faydalı ve sürdürülebilir bir üretim tekniğidir. Koruyucu tarım tekniği; verimi,ekonomik geri dönüşleri ve gıda güvenliğini artırabilmek için günümüzde kullanılan uygulamalardan biridir.Koruyucu tarımın ilkeleri; sıfır toprak işleme, kalıcı toprak örtüsü ve bitki münavebesidir. Bunlara ilaveten, koruyucutarım uygulamaları, toprakta karbon birikimi ve daha az gaz yayılımı aracılığıyla iklim düzenlemesi üzerinde etkiliolmaktadır. Ayrıca; suyun etkili bir şekilde kullanımını sağladığından dolayı bu yöntem; toprağın fiziksel, kimyasalve biyolojik kalite kriterlerinin de gelişmesine yardımcı olur. Son yıllarda Türkiye’de yoğun tarımsal üretimsonucu ülke topraklarının yapısında ciddi derecede bozulmalar başlamıştır. Bu nedenle ülke genelinde hem toprağıkoruyacak hem de optimum üretim yapılabilecek üretim tekniklerinin uygulamaya konulması gerekmektedir. Buaraştırmanın amacı; Türkiye’deki koruyucu tarımın önemini vurgulamaktadır. Buna ilaveten koruyucu tarımın ülkeçapında yaygınlaşması için gerekli olan önlemler ve ülke stratejileri de detaylı bir şekilde tartışılmıştır.

The study focuses on the experimental investigation of the impact of using coconut oil (CO) as a phase-change material (PCM) for heat storage on the root-zone temperature within a greenhouse in Adana, Türkiye. The study examines the efficacy of PCM as latent heat-storage material and predicts root-zone temperature using three machine learning algorithms. The dataset used in the analysis consists of 2658 data at hourly resolution with six variables from February to April in 2022. A greenhouse with PCM shows a remarkable increase in both ambient (0.9–4.1 °C) and root-zone temperatures (1.1–1.6 °C) especially during the periods without sunlight compared to a conventional greenhouse. Machine learning algorithms used in this study include Multivariate Adaptive Regression Splines (MARS), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). Hyperparameter tuning was performed for all three models to control model complexity, flexibility, learning rate, and regularization level, thereby preventing overfitting and underfitting. Among these algorithms, R² values for testing data listed from largest to smallest are MARS (0.95), SVR (0.96), and XGBoost (0.97), respectively. The results emphasize the potential of machine learning approaches for applying thermal energy storage systems to agricultural greenhouses. In addition, it provides insight into a net-zero energy greenhouse approach by storing heat in a bio-based PCM, alongside its implementation and operational procedures.

In this research effect of different soil types (normal and saline), farmyard manure norms (2 ton/ha - 4 ton/ha), manure application techniques (surface and subsurface) and soil temperature levels (20-25°C, 25-30°C, 30-35°C, 35-40°C, 40-45°C and 45-50°C) were examined of the soil CO2 flux on the pots at the laboratory conditions. According to obtained results, soil type (ST), manure norm (MN), manure application technique (MAT) and soil temperature (T) values changed CO2 flux. CO2 flux value of saline soil condition smaller than the normal soil condition. As an expected result, increased the manure amount increased the CO2 flux from soil to atmosphere. However, CO2 flux on the condition that subsurface manure application was less than surface manure application. CO2 flux values at the high soil temperatures were more than low soil temperature conditions. According to the interaction (T*ST, T*MN and T*MAT) results were not statistically significant. Soil CO2 flux were affected by gradually increasing of temperature.

Color of dried edible material is one of the most essential considerations for consumers when making product purchases. The similarity between the color of the dried product and the color of the fresh product increases their marketability. The primary objective of drying operations is to dry products with minimal energy consumption, to prevent the growth of microorganisms, preserve their flavor and nutritional value, to make the moisture content suitable for storage, and to ensure that the color change after drying closely resembles that of the fresh product. This study seeks to dry Oyster mushrooms (Pleurotus spp.) at different drying power levels (90 W, 180 W, 360 W, and 600 W) using the microwave drying method of thin slices and to determine the color changes of the dried mushrooms. As a result of the trials carried out in the laboratory, it was discovered that the increase in drying power caused the samples to dry faster. At the end of the drying period, the colors of the mushrooms became darker than the initial levels. The highest change in brightness was recorded during the drying process at a power of 90 W. In the investigation, it was determined that the drying time was longer and the color change of the dried product was greater when the power was 90 W. Based on this, it can be stated that the color changes of the product will exhibit less deformation if the desired humidity level is obtained quickly during the drying process using the thin slice microwave drying method. However, this should just be viewed as a suggestion for minimizing color change. Numerous criteria, including color change, drying duration, nutrient content, and shelf life, should be assessed concurrently while determining the drying process.