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 conduc
ted 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.
This research aimed to determine the economic scales of production and
different product values, and the difference between the true sizes and its
parallel economic areas in the midregion of Jordan. The econometrics analysis
was used for the cost
functions of sheep, goats, and cows production in
midregion of Jordan. The data were analyzed by linear regression using
suitable economic models.
Total production functions of sheep were determined, and the average
productivity was derived to determine the economic scales in order to minimize
the average productivity cost of sheep, goats and cows. For the sheep, the
product value was about JD 16649.59 for 419 sheep, with average cost of one
JD is 0.522 JD. Marginal cost function was derived from the total production
cost function, with the equality marginal cost function to parallel price in order
to reach the scale of maximize profit. The production value was 18251.49 JD
for 460 sheep and average JD cost of 0.56. The present productivity scale was
157sheep which was 262 sheep lower than the productivity scales for economic
efficiency and 303 sheep lower than that for maximize profit.
الكفاءة الاقتصادية
cost function
Regression
الانحدار الخطي
دالة التكاليف الكلية
دالة التكاليف المتوسطة
دالة التكاليف الحدية
السعة الإنتاجية
تعظيم الربحية
منحنى التكاليف الكلية
منحنى التكاليف المتوسطة
منحنى التكاليف الحدية
التحليل الاقتصادي القياسي
Marginal cost
Productivity cost
Maximize profit
Curve cost
Analysis of econometrics
المناطق المطرية
المزيد..
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
sq
uares (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.
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 can
not 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.
في الآونة الأخيرة حدث تضخم للمعلومات على شكل أخبار ومقالات مختلفة وجزء كبير من هذه البيانات يكون بشكل غير منظم