Person: ARSLAN, BİLGE
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Publication Trends and variability in precipitation across turkey: A multimethod statistical analysis(Springer, 2023-09-18) Yetik, Ali Kaan; Arslan, Bilge; Sen, Burak; ARSLAN, BİLGE; Bursa Uludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği Bölümü; JOP-8553-2023Analyzing trends in precipitation data is crucial for understanding the effects of climate change and making informed decisions about water management and crop patterns. The objective of the presented study was to investigate precipitation trends, analyze temporal and spatial variations and identify potential change points in Turkey throughout the period from 1980 to 2019. Precipitation data were analyzed for both regional and 81 meteorological stations in Turkey on a monthly, seasonal, and annual basis. Spearman rank correlation and Mann-Kendall tests were utilized to detect possible trends and Sen's slope test to estimate the magnitude of change throughout the entire time series. The average precipitation amount of Turkey was determined 639.2 mm between the years 1980 and 2019. While Central Anatolian and Eastern Anatolian regions had below 639.2 mm, other regions were above. The range of seasonal precipitation values were found for winter 128.7-320.8 mm, 108.9-260.0 mm for spring, 43.9-109.3 mm for summer, and 79.7-238.4 mm for autumn. The analysis of the data revealed no significant increase or decrease in annual values on a regional basis, with the greatest change on a seasonal basis being observed in the winter. The 40-year trends of annual precipitation data belonging to 81 stations were decreasing in 23 provinces and increasing in 58 provinces, and 11 of them (14% of the total) were found to be statistically significant. Moreover, November was found to be a month of particular significance in terms of precipitation changes across the country, with a decrease observed in 80 out of 81 provinces. Spatial distribution analysis showed that the magnitude of variation in precipitation decreased as one moved from the southern to the northern regions of the country.Publication Effects of full and deficit irrigation on the growth and quality of cool-season turfgrasses under subsurface drip irrigation(Elsevier, 2023-11-22) BİLGİLİ, UĞUR; ARSLAN, BİLGE; CANDOĞAN, BURAK NAZMİ; Arslan, Bilge; Büyükcangaz, Hakan; BÜYÜKCANGAZ, HAKAN; Kumraltekin, Emir Doğan; KUŞÇU, HAYRETTİN; Kuşçu, Hayrettin; Yönter, Fikret; YÖNTER, FİKRET; Bursa Uludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği.; Bursa Uludağ Üniversitesi/Ziraat Fakültesi.; AGD-4084-2022Irrigation is necessary in terms of achieving high-quality turfgrass. However, it is evident that more efficient and cost effective irrigation methods should be adapted for the sake of water conservation strategies since water consumption is high during turf irrigation. The objective of this study is to determine the effects of full and deficit irrigation with subsurface drip irrigation on growth and quality of perennial ryegrass (Lolium perenne L.) and tall fescue (Festuca arundinaceae Schreb) in a sub-humid climate of Turkey. Three irrigation treatments [I1 (full irrigation), I2 (mild water deficit, 75%), and I3 (moderate water deficit, 50%) were applied to the plants. Turf color and quality of each plot were rated visually, and clipping yields were collected. The amount of irrigation applied varied between 457.3 - 833.9 mm and 356.7 - 710.1 mm and seasonal crop evapotranspiration (ETa) values ranged from 578.4 to 1053.0 mm for tall fescue and 551.2 to 1044.0 mm for perennial ryegrass in 2018 and 2019, respectively. Among the species, tall fescue showed higher visual color, quality ratings and clipping yields. In cases where it does not matter for one or two weeks to be below the acceptable quality (<6) minimum annual irrigation amounts required to maintain quality was 689.1 mm for perennial ryegrass and 578.4 for tall fescue. The results have suggested that acceptable visual color and quality are sustainable in the tall fescue with mild water deficit application in regions where irrigation water is limited in sub-humid climates. Studies on water-saving strategies to turfgrass in regions where the climates varies from year to year are lacking in literature.Publication Determination of pipe diameters for pressurized irrigation systems using linear programming and artificial neural networks(Ankara Üniversitesi, 2023-01-01) Kurtulmuş, Ezgi; Kurtulmuş, Ferhat; Kuşcu, Hayrettin; Arslan, Bilge; Demir, Ali Osman; KURTULMUŞ, EZGİ; KURTULMUŞ, FERHAT; KUŞÇU, HAYRETTİN; ARSLAN, BİLGE; DEMİR, ALİ OSMAN; Bursa Uludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği Bölümü.; 0000-0001-9600-7685; AAH-4682-2021; AAH-2936-2021; R-8053-2016; JOP-8553-2023; JLX-2232-2023Pressurized irrigation systems are widespread among other alternatives in Mediterranean countries. Since the initial investment costs of pressurized irrigation systems are quite high, it is crucial to determine design parameters such as pipe diameter. Most of the current optimization techniques for pipe diameter selection are based on linear, non-linear, and dynamic programming models. The ultimate aim of these techniques is to produce solutions to problems with less cost and computation time. In this study, a novel approach for determining pipe diameter was proposed using Artificial Neural Networks (ANN) as an alternative to existing models. For this purpose, three pressurized irrigation systems were investigated. Different ANN architectures were created and tested using hydrant level parameters of the irrigation systems, such as irrigated area per hydrant, hydrant discharge, pipe length, and hydrant elevation. Different training algorithms, transfer functions, and hidden neuron numbers were tried to determine the best ANN model for each irrigation system. Using multilayer feed-forward ANN architecture, the highest coefficients of determination were found to be 0.97, 0.93, and 0.83 for irrigation systems investigated. It was concluded that pipe diameters could be determined by using artificial neural networks in the planning of pressurized irrigation systems.