Assessment of AquaCrop and CropWat Models in the Simulation of Deficit Irrigation Impact on Cotton Crop

This study was carried out to compare the performance of the FAO AquaCrop and CropWat models in simulating the effects of deficit irrigation on cotton crop. The models were calibrated using data from the 2007 growing season of a field study conducted to assess deficit irrigation effects on cotton, whereas the models were validated by comparing their outputs for yield and water use (ETc) with the measured values of the two variables in the 2008 and 2009. The relationship between measured and predicted values of yield and ETc revealed that the AquaCrop was better than CropWat in predicting water stress impact on yield and ETc. The linear regression equation for AquaCrop had a small intercept and its slope was very close to unity. The index of agreement (d) was close to one for both models, except its value for ETc in the 2009 year. Both models could reproduce the general trend of the changes in soil water content in the different irrigation levels. Accordingly, the use of AquaCrop instead of CropWat should be encouraged for management and planning of irrigation, since it is a practitioner type model keeps a good balance between output accuracy and simplicity.

Analysis of Seasonal and Annual Variations in Surface Air Temperature in Syria

This study is concerned with the variations in annual and seasonal surface air temperature in Syria, depending on the data from 12 different meteorological stations in Syria. The analysis of surface temperature trends was performed using Least squares (linear regression) and Moving- averaging filters according to Gaussian low- pass filter. Fast Fourier Transformation was used for the analysis of periodicity for the annual mean surface temperature. The results of linear regression showed that the general trend of annual and seasonal temperature in all stations was positive except Latakya. The results of annual and seasonal temperature, fluctuations revealed the existence of important warming period in all stations starting from 1993-1994 for the average of annual and winter temperature while summer, autumn and spring temperatures averages were above the mean during the study period. Periodicities analysis showed that the surface air temperature seems to be affected by solar cycle and quasi- biennial oscillation as well as the El-nino southern oscillation.

The Effect of Both the Size and The Type of Statistical Sampling on The Estimates of Simple Linear Regression Equation Coefficients

These papers aim to study the estimation of the simple linear regression equation coefficients using the least square method at different sample sizes and different sampling methods. And so on, the main goal of this research is to try to determine the optimum size and the best sampling method for these coefficients. We used experimental data for a population consist of 2000 students from different schools all over the country. We had changed the sample size each time and calculate the coefficients and then compare these coefficients for different sample sizes with their coefficients of the real population; and the results have been shown that the estimation of the linear regression equation coefficients are close from the real values of the coefficients of the regression line equation for the population when the sample size closes the value (325). As it turns out that the Stratified random sampling with proportional distribution with class sizes gives the best and most accurate results to estimate linear regression equation with least square method.

Applied Nonparametric Regression Methods as Alternatives to the Linear Regression Methods (Comparative Study)

Linear regression methods impose strong constraints on regression models, especially on the error terms where it assumes that it is independent and follows normal distribution, and this may not be satisfied in many studies, leading to bias that cannot be ignored from the actual model, which affects the credibility of the study. We present in this paper the problem of estimating the regression function using the Nadarya Watson kernel and k- nearest neighbor estimators as alternatives to the parametric linear regression estimators through a simulation study on an imposed model, where we conducted a comparative study between these methods using the statistical programming language R in order to know the best of these estimations. Where the mean squares errors (MSE) was used to determine the best estimate. The results of the simulation study also indicate the effectiveness and efficiency of the nonparametric in the representation of the regression function as compared to linear regression estimators, and indicate the convergence of the performance of these two estimates.