Two years field experiment was carried out at Agricultural Fields, College of Agriculture, Baghdad University, Al-Jadriya during 2014-2015 and 2015-2016 to determine the effect of salinity of irrigation water on growth and grain yield of three oat cultivars. The experiments were laid out according to randomized complete blocks design having split plot arrangements with two factors; first factor included three oat cultivars (Shifaa, Hamel and Pimula) while the second factor included three levels of salinity of irrigation water (3, 6 and 9 dS.m-1 ) in addition to the control (river water with salinity level of 1.164 dS.m-1 ) with three replicates. Results revealed a significant effect of salinity of irrigation water on all studied traits. Mean values of studied traits decreased with increasing the salinity of irrigation water to more than 3 dS m-1 which didn’t differ from control (irrigation with river water) significantly in all studied traits, whereas salinity level of 9 dS.m-1 gave the lowest number of tillers (457 and 422 tiller), number of panicles (376 and 355 panicle), number of grains per panicle (28.54 and 25.97 grain), weight of 1000 grain (26.77 and 26.48 g), grain yield (3.96 and 3.71 t.h-1 ), biological yield (17.53 and 16.43 t.h-1 ), plant growth rate (12.30 and 11.55 g.m-2 ), and harvest index (22.52% and 23.08%) for both seasons, respectively. The results of statistical analysis showed significant differences among the three studied cultivars, where plants of Shifaa and Hamel gave highest number of tillers, biological yield, plant growth rate and grain yield for both seasons. It is concluded that salinity stress had a negative effect on plant growth stages, which may reflected negatively on grain yield and its components. Keywords: Avena sativa, NaCl, dS m-1 , oat yield components.
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Accurate prediction of river water quality parameters is essential for environmental protection and sustainable agricultural resource management. This study presents a novel framework for estimating potential salinity in river water in arid and semi‐arid regions by integrating a kernel extreme learning machine (KELM) with a boosted salp swarm algorithm based on differential evolution (KELM‐BSSADE). A dataset of 336 samples, including bicarbonate, calcium, pH, total dissolved solids and sodium adsorption ratio, was collected from the Idenak station in Iran and was used for the modelling. Results demonstrated that KELM‐BSSADE outperformed models such as deep random vector funct
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The process of adding volaticle oil to minced poultry meat led to significant increase (P<0.01)in moisture, prot
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