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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In This Paper, some semi- parametric spatial models were estimated, these models are, the semi – parametric spatial error model (SPSEM), which suffer from the problem of spatial errors dependence, and the semi – parametric spatial auto regressive model (SPSAR). Where the method of maximum likelihood was used in estimating the parameter of spatial error ( λ ) in the model (SPSEM), estimated the parameter of spatial dependence ( ρ ) in the model ( SPSAR ), and using the non-parametric method in estimating the smoothing function m(x) for these two models, these non-parametric methods are; the local linear estimator (LLE) which require finding the smoo
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