Abstract:Rice is one of the most applied irrigation waters applied plant among cultivated plants since it is grown in water or in saturated soil conditions in a significant portion of the growing season. This study was aimed to determine the effects of rice grown on water use, development, yield and some yield parameters using of both separately and jointly water retention barriers and subsurface drip irrigation methods in Enez, Edirne, Turkey in 2017. Methods and Results: Four different treatments were applied: pondin… Show more
“…In fact, these amounts are almost double the water required by the paddy rice plant, even in ponding irrigation. Demirel et al, (2020) used 2,444 mm of water for paddy rice in their study in Edirne, where they applied the ponding method in a controlled study. Therefore, an excessive amount of water consumption is observed in paddy rice production.…”
Section: Classification Mapsmentioning
confidence: 99%
“…They observed a decrease in yield while significant water was saved with alternative methods. Demirel et al, (2020) investigated the performance of the subsurface drip irrigation method and again the water retention barrier. It has been stated that up to 50% water can be saved with the subsurface drip irrigation method, and up to 69% water can be saved if this method is used together with the water retention barrier.…”
Paddy rice irrigation takes an important role in water consumption. Therefore, the savings to be made in paddy rice irrigation will have significant impacts. In the sustainable use of water resources, both the irrigation methods and the methods to be used in the planning of water resources are critical. Hence, the use of drip irrigation should be expanded. On the other hand, the use of modern satellite technologies and machine learning models should be used while planning irrigation. In this study, Google Earth Engine (GEE), which is a cloud-based image processing platform was employed in the calculation of paddy rice cultivation areas. Random Forest (RF) and Support Vector Machines (SVM) machine learning algorithms were applied. The results showed that RF algorithm can calculate the paddy cultivation areas with an accuracy of 97%. A difference of 27.69 km2 was found between the officially declared cultivation areas and the calculated area. This can yield a miscalculation of water requirement with an error of 33.8, 38.1 and 155 million m3, in subsurface drip irrigation, drip irrigation and basin irrigation methods, respectively. Results showed that accurate calculation of paddy rice cultivation areas and drip irrigation will both minimize this error and allow 4 times more area to be irrigated.
“…In fact, these amounts are almost double the water required by the paddy rice plant, even in ponding irrigation. Demirel et al, (2020) used 2,444 mm of water for paddy rice in their study in Edirne, where they applied the ponding method in a controlled study. Therefore, an excessive amount of water consumption is observed in paddy rice production.…”
Section: Classification Mapsmentioning
confidence: 99%
“…They observed a decrease in yield while significant water was saved with alternative methods. Demirel et al, (2020) investigated the performance of the subsurface drip irrigation method and again the water retention barrier. It has been stated that up to 50% water can be saved with the subsurface drip irrigation method, and up to 69% water can be saved if this method is used together with the water retention barrier.…”
Paddy rice irrigation takes an important role in water consumption. Therefore, the savings to be made in paddy rice irrigation will have significant impacts. In the sustainable use of water resources, both the irrigation methods and the methods to be used in the planning of water resources are critical. Hence, the use of drip irrigation should be expanded. On the other hand, the use of modern satellite technologies and machine learning models should be used while planning irrigation. In this study, Google Earth Engine (GEE), which is a cloud-based image processing platform was employed in the calculation of paddy rice cultivation areas. Random Forest (RF) and Support Vector Machines (SVM) machine learning algorithms were applied. The results showed that RF algorithm can calculate the paddy cultivation areas with an accuracy of 97%. A difference of 27.69 km2 was found between the officially declared cultivation areas and the calculated area. This can yield a miscalculation of water requirement with an error of 33.8, 38.1 and 155 million m3, in subsurface drip irrigation, drip irrigation and basin irrigation methods, respectively. Results showed that accurate calculation of paddy rice cultivation areas and drip irrigation will both minimize this error and allow 4 times more area to be irrigated.
Nowadays, as the pressure of drought on water resources increases, new approaches regarding paddy rice irrigation, which has the highest water consumption rate, need to be taken into consideration.One of these approaches is the drip irrigation system, which saves significant amount of water in paddy rice irrigation.. In this study, 4 different irrigation subjects (25%, 50%, 75% and 100%) in drip irrigation system for paddy rice in Edirne were examined in TAGEM-SUET. As a result of the study, the evapotranspiration of paddy rice during the production season was calculated as 692.83 mm, the amount of irrigation water was calculated as 162-486 mm in drip irrigation. In the model, it was measured that the optimum drip irrigation program would not cause a decrease in efficiency despite saving 38% of water compared to the ponding method. It is thought that TAGEM-Suet can be a good tool for irrigation planning and management of paddy rice, depending on climatic conditions.
Bu araştırma çeltik yetiştiriciliğinde farklı sulama sistem ve düzeylerinin klorofil içeriğine etkisinin belirlenmesi amacıyla 2019-2020 yıllarında Alata Bahçe Kültürleri Araştırma Enstitüsü Müdürlüğü Tarsus Toprak ve Su Kaynakları Lokasyonu’nda yürütülmüştür. Deneme iki sulama yöntemi ana parselleri (yüzeyaltı (YA) ve yüzeyüstü (YÜ)), alt parselleri üç sulama düzeyi bitki pan katsayısı değerlerine göre (I1: Class A-pan (Ep) x 1.00; I2: Ep x 1.25 and I3: Ep x 1.50) ve kontrol parseli tava sulama (TS) yöntemi olarak oluşturulmuştur. Araştırmada sulama yöntemi ve katsayılarının verim üzerine etkisi istatistiksel olarak önemli bulunmuştur (P
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