Bluefin trevally (BFT, Caranx melampygus) were studied in Bongao, Tawi-Tawi, for parasite infestations and prevalence at different body weights (300 - 500 g, 500 - 1500 g, and 1500 - 3000 g). Twenty-seven samples were collected for parasitic investigation. Out of this, nine fish with 300 - 500 g body weight, nine fish with 500 - 1500 g body weight, and nine fish with 1500-3000 g body weight were obtained from the fishermen at the wet market landing site. Results revealed that a total of 46 parasites were recorded in six BFT fish weighing 300 g to 500 g, 44 parasites were recorded in eight BFT fish weighing 500 g to 1500 g, and 101 parasites were recorded in eight BFT fish weighing 1500 g to 3000 g. Based on the ANOVA analysis, the prevalence, abundance, and intensity of parasite infestations in BFT species with different body weights were not significantly different (p>0.05). Moreover, it was found that four species of parasites (Argulus spp., Caligus spp., Cymothoa spp., and Pulchrascaris spp.) were present in the heart, intestines, mouth, and operculum of different body weights of BFT fish, of which the majority were found in the gills, mainly Caligus spp. parasite. Thus, the results of this study indicate no significant differences in parasite infestation of BFT, C. melampygus, between individual body weights and host locations in Bongao, Tawi-Tawi.

The preliminary identification of plant diseases plays a predominant role in preventing loss of production. The laboratory identification process of plant diseases is time-consuming and could not be conducted in the countryside, where experiment facilities are rarely found. This paper shows a deep learning approach to confine the infection area and identify the diseases by using images of their leaves. Deep learning works well with large amounts of data. So we can increase the accuracy and reduce the loss by engrossing a plethora of data. However, it will not increase the efficiency of the models.  In this paper, we use several cutting-edge deep learning models, such as MobileNet, ResNet, and EfficientNet, along with Faster R-CNN and SSD on a small dataset. The dataset contains 2366 images of 27 types. The dataset was taken in a real environment. The data augmentation technique cannot be used with a small dataset. All state-of-the-art deep learning model are trained as a baseline to work on the efficiency of the models. We experiment with the best performer for computation cost. So, to increase the efficiency of the model we implement cyclic learning rate which performs 53.81% map@.50 on best performer EfficientDet. It also lessens the variance, which suggests that cyclic learning not only works as a learning rate but also functions as a data augmentation.  In the future, we will apply this learning rate to a dataset containing a large number of plant disease collection images, where different types of data augmentation can be used to not only increase the images but also decrease the generalization loss. Farmers can predict plant diseases more accurately using this system.

In this research, it was tried to determine the male and female flower yield in young clonal seed orchard (YCSO) with the expectation that the flowering variation would be decreased among years, clones and ramets thanks to aging. The measurements were made on five ramets for each of 30 clones in four years in a Scots pine (Pinus sylvestris L.) seed orchard. The orchard, originated from the Araç-Dereyayla seed stand in Kastamonu, was established in 1995 by using two years-old grafts in Kastamonu, located northwestern Black Sea region of Türkiye. The examined characters were number of male (NMF), and female flowers (NFF). When the four-year of data from the YCSO was evaluated, mean values from NMF and NFF among the clones were significant and variation of the ramets was high. The coefficient of variation (Cv) from the NMF of the clones gradually decreased with the subsequent years (Cv2006=147.54%, Cv2010=59.04%), whereas the Cv in the NFF did not show any decrease with the same years. In addition, there were significant differences among the years as to the NMF, and NFF. Over the four years, the NMF was significantly lower than that of the NFF, and even in some years the NFF was doubled compared with the NMF (NMF2006=65.9; NMF2007=314.29; NMF2008=427.85; NMF2010=115.73 & NFF2006=123.80; NFF2007=604.68; NFF2008=394.62; NFF2010=196.72). Abundant flowering periods seen in natural stands were also observed in the male and female flowers of the clonal seed orchards. For the studied seed orchard high variation both among the clones and the ramets indicates the high selection capacity in the breeding programs. The bigger variation among the ramets confirm that the genotypes have responded against the heterogeneity of growing area in seed orchard or the ramets have not reached the optimum flowering period. These results have shown the importance of the practices which increase the flowering yield and effectiveness of fertilization in YCSO.

Striped venus clam fishing has been carried out in the Black Sea coasts of Türkiye for a long time about 30 fishing vessel. In this fishery, which is maintained with hydraulic dredges, it cannot be said that administrative legislation has been fully developed. It is not entirely clear on what basis the fishing quota is in the fishery bans that change and develop day by day. Further standardization is required for selectivity of sieves. In this study, data covering 4 fishing seasons of a fishing vessel were standardized and processed. Catch per unit effort (CPUE) in terms of kilogram/minutes was calculated.  Fishing activities according to season were carried out at Şile stations in 2017-2018, and in Sakarya in 2018-2019 and 2019-2020, at Şile in the last season of 2020-2021. It was found out that the CPUE data was in a continuous downward trend within each season and in the following seasons. This situation reveals the necessity of questioning and rethinking the current daily catch quota (10 tons per day). In addition, the high proportion of waste and small-sized individuals in marketed catch is a sign of a selectivity problem. Therefore, stock determination, selectivity and daily catch quota applications of striped venus clam fishery should be rearranged.

Forest ecosystems are one of  the most important systems in capturing atmospheric carbon dioxide (CO2) and play an essential role in mitigating climate change. Forest ecosystems provide biodiversity, soil conservation, recreation areas, wildlife habitat, nutrient cycling, carbon storage and oxygen production. According to the Climate Change Framework Convention, determining and reporting the amount of carbon accumulation in forest ecosystem is of great importance. Different methods such as allometric equations, carbon expansion factor and remote sensing methods are used to determine aboveground carbon (AGC) storage. Especially with the developing technology, remote sensing techniques are used intensively to determine AGC storage. Normalized Difference Vegetation Index (NDVI) are commonly used, especially in determining the amount of AGC storage. The aim of this study is to estimate AGC storage according to different methods and to investigate whether there are statistical differences in AGC storage estimation of these approaches. The research was carried out in pure Calabrian pine (Pinus brutia Ten.) stands in the Burmahanyayla planning unit in Antalya province in the Mediterranean region of Türkiye. Within the scope of the study, the amount of AGC storage was calculated by using the inventory study conducted in 2022 and the Landsat 9 satellite data. A paired sample t-test was used to statistically examine the difference in the quantity of AGC storage based on inventory data and remote sensing data using SPSS 23.0. The results reveal that there was no statistically significant difference in the AGC storage values between the two approaches in all sample plots.