Two field experiments were conducted during the spring season 2020 in Karbala governorate to study the effect of irrigation systems, irrigation intervals, biofertilizers and polymers on some characteristics of vegetative growth and potato production. The results showed that there were significant differences in the values of the average plant height due to the effect of the double interference between the irrigation system and the improvers, The height of potato plant under any irrigation system was superior when adding conditioners compared to the control treatment, as it reached 48.56, 58.00 and 64.33cm when adding polymer, biofertilizer, and polymers+ biofertilizers, respectively compared with the control treatment of 44.64cm in the surface drip irrigation system. While it was 51.74, 58.19 and 64.67cm in the treatments of adding polymer, biofertilizers and polymers+ biofertilizers, respectively, compared with the control treatment 41.51cm under the sprinkler irrigation system, Also, there were significant differences in the values of average root lengths as a result of the effect of the double interference between the irrigation system and the improvers, as the potato root lengths under any irrigation system excelled when adding the improvers compared to the control treatment, It reached 36.29, 41.94 and 49.37cm when adding polymer, biofertilizers and polymers+ biofertilizers, respectively, compared with the control treatment 31.28cm at surface drip irrigation system. While it was 35.94, 40.69 and 48.14cm when adding polymer, biofertilizer, and polymers + biofertilizer, respectively, compared with the control treatment of 30.33cm at the sprinkler irrigation system, There were significant differences in the values of the total yield as a result of the effect of the double interference between the irrigation system and the conditioners, as the total yield of potato tubers under the two irrigation systems exceeded when adding the improvers compared to the control treatment, It reached 25.31, 28.86 and 36.90 μg ha when adding polymer, biofertilizers and polymers + biofertilizers, respectively, compared with the control treatment 17.21 μg ha at the surface drip irrigation system.
The middle Cenomanian – early Turonian Mishrif Formation, a major carbonate reservoir unit in southern Iraq, was studied using cuttings and core samples and wireline logs (gamma‐ray, density and sonic) from 66 wells at 15 oilfields. Depositional facies ranging from deep marine to tidal flat were recorded. Microfacies interpretations together with wireline log interpretations show that the formation is composed of transgressive and regressive hemicycles. The regressive hemicycles are interpreted to indicate the progradation of rudist lithosomes (highstand systems tract deposits) towards distal basinal locations such as the Kumait, Luhais and Abu Amood oilfield areas. Transgressive hemicycles (transgressive systems tract deposits)
... Show MoreThis paper presents the ability to use cheap adsorbent (corn leaf) for the removal of Malachite Green (MG) dye from its aqueous solution. A batch mode was used to study several factors, dye concentration (50-150) ppm, adsorbent dosage (0.5-2.5) g/L, contact time (1-4) day, pH (2-10), and temperature (30-60) The results indicated that the removal efficiency increases with the increase of adsorbent dosage and contact time, while inversely proportional to the increase in pH and temperature. An SEM device characterized the adsorbent corn leaves. The adsorption's resulting data were in agreement with Freundlich isotherm according to the regression analysis, and the kinetics data followed pseudo-first-or
... Show MoreImage classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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