الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
1- Wheat experiments:
Wheat is the most important cereal crop in Egypt; Exposure
to climate change is adverse effects. One of its adverse effects is
warmer temperatures and increasing episodes of very hot
weather. The adverse effects especially temperature is the most
primary factor led to development of wheat growth, and
consequently influence yield. So, the main goal of this study was
using, different ways for escaping from adverse effects of climate
change on wheat plant as follows:
1- Determine the optimum date to make wheat plant at the
grain filling period avoid adverse effects of climate change
especially high temperature.
2- Spraying plant growth promoting namely Ascobeen and
Potassien and a mixture of them.
3- Using some multivariate analyses as statistical tools for
determine the important components (characters) of yield that
are affected by the adverse effects of climate change.
4- Using Quinoa plant as a supporter crop for wheat under the
adverse effects of climate change under Egyptian conditions.
Analysis of variance results:
The results of this study showed that yield ,1000 grains
weight (g), spike weight (g), spike length (cm), number of
spiklets/spike and plant height (cm))responded to the change in
the sowing dates and it were more affected by heat stress from
date to date during grain filling period that positively reflected on
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the yield in early date.
The optimum sowing dates avoid plant at the grain filling
period from heat and adverse effects of climate change were
15/11 in both seasons, as well as the highest yield being16.66
and 16.54 ard/fed was obtained on the early date for the two
seasons, respectively.
Spraying Ascobeen alone or mixed with Potassien
increased the tolerance of wheat plants against heat stress
during grain filling period in both seasons. Spraying Ascobeen
alone significantly increased yield of grainsby 18.74% and
19.17% in the first and second seasons compared to the control
treatment, respectively. Similarly, spraying a mixture of
Ascobeen and Potassien significantly increased grain yield
by14.25% and 15.56% compared to unsprayed plots in the two
seasons, respectively. Applying Potassien alone to wheat plants
ranked third.
Generally using Plant growth promoting contributed
significantly by increasing wheat yield and its components under
adverse climate conditions and reduced impact of thermal stress
on plants especially during grain filling period in both seasons.
The increase in grain yield is mainly due to the beneficial
effect of N on growth and yield components such as 1000 grains
weight (g), spike weight (g), spike length (cm) number of
spiklets/spike and plant height (cm). Also, a good supply of N
increased the vegetative growth and grain filling periods which,
in turn, positively affect grain yield.
The results indicated that the interaction among sowing
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dates, plant growth promoting and N Fertilizer levels affected
significantly each of grain yield/fed, weight of 1000 grain, spike
weight, number of spiklets/spike and plant length in the first and
second seasons, respectively. On the other hand, results
showed that spike length was not significantly affected by this
kind of interaction.
Simple correlation analysis results:
Results of simple correlation cleared highly positive
correlation between yield and each of, weight of 1000 grains X1
(0.548), Spike length X3 (0.518), number of spiklets/spike X4
(0.330) and plant length X5 (0.454). These results meaning that
the yield components were related to the yield and it is logical
relation.
Results also, showed highly positive significant
relationship between weight of 1000 grains X1and spike weight
X2 (0.506), spike length X3 (0.477), number of spiklets/spike X4
(0.665) and plant length X5 (0.454), meaning that these
components were related to others. On the other hand, highly
negative significant relation were found between weight of 1000
grains X1 and maximum temp X7 (-0.579) and sum maximum
temp (-0.552) clearing that these components were related to
others and they are more affected by the environmental
variables.
Negative and highly significant relation was found among
pant length X5 and minimum temp X6, sum mini temp X9 and
wind speed X11 with values being - 0.543, - 0.497 and - 0.367,
respectively.
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Generally, simple correlation coefficient indicating that
environmental variables had indirect effect on yield because of it
is highly significant relation on yield components. So, we could
recommend breeders to select among yield components and
environmental variables.
Multiple linear regression analysis results:
The results multiple linear regression analysis showed
that all studied variables recorded relative contribution (R2) equal
to 75% of the total variation of yield meaning that this study
included most efficient variables. On the other hand, the adjusted
R2 was 71.7 % that is nearest to unadjusted R2 indicating that
the sample size was suitable and these estimates were more
accurate.
Stepwise multiple linear regression analysis results:
Stepwise analysis eliminate number of spiklets/spike X4
compared with full model equation and it introduced a new
improving equation that could be predicted the suitable yield
which is suitable with any value of Xi of accepted variable. The
accepted variables recorded relative contribution (R2) equal to
73.9% in the total variation of yield which is close to be equal of
(R2) recorded by full model meaning that this equation included
most efficient variables.
Stepwise also, recorded relative contribution (R2) for each
accepted variable called R2 change. The highest contributed
variable was plant length that scrod (20.6%) followed by humidity
that scrod (16.6%), spike length (7%), minimum temperature
(6.6%), sum minimum temperature (5.9%), sum maximum
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temperature (5.4%), maximum temperature (4.8%), spike weight
(3.1%), wind speed (1.9%) and weight of 1000 grains that scrod
1.8%, respectively. So, we could recommend that these
variables were the limiting variables in wheat plant yield.
Finally, results of stepwise removed only No. of
spiklets/spike compared with multiple regression and their R2
close to be similar that led us to apply factor analysis.
Factor analysis results:
Factor analysis was applied to establish the dependent
relationship between the studied variables of wheat. So, it
grouped the studied eleven variables into three main factors.
Factor one contained four variables namely: environmental factor
accounted for 30.668% of the total variation in the dependence
structure.
So it could be recommended that the environmental
variable were in factor one which reflects its importance for the
wheat crop meaning that any change in these variables affects
grain yield of wheat especially during the grain filling period.
Factor two included five variables which accounted for
29.046% of the total variability of the dependence structure. We
could call this factor the yield factor. The third factor had two
variables i.e. Humidity and wind speed and it contributed at
13.694% of the total variance of the dependence structure and it
is called wind and humidity factor.
Generally the factor 1 (environmental factor) had a
highest effect on grain filling period and, in turn, on yield. So, the
use of factor analysis by plant breeders has the potential of
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increasing the comprehension of the causal relationships of
variables and can help to determine the nature and sequence of
characters to be selected in a breeding program to increase the
yield of wheat plants and prolong the grain filling period to avoid
the adverse effect of climate change. Also, factor analysis in the
current study cleared that the estimated communalities were
adequate for conclusion where the three factors together
accounted for 73.408% of the total variability in the dependence
structure.
Finally results of ANOVA, simple correlation coefficients,
multiple linear regression, stepwise multiple linear regression
and factor analysis close to be similar for detecting the effect of
climatic change among dates, plant growth promoting and effect
of N level on yield of wheat plants that helping agronomist and
plant breeders for avoiding wheat plant from the expected
adverse climatic change especially during grain filing period.
2- Quinoa experiment:
The results of quinoa experiment indicated that grain
yield; flower height and flower branches were significantly
affected by the seasons. On the other hand, plant height was not
significantly affected by the seasons.
Results for quinoa yield indicate this yield significantly
responded to the change in climate across the two seasons as
well as the change in the sowing dates. The early sowing date of
15- November is the suitable date for helping quinoa plants to
grow under the Egyptian conditions and the second possible
sowing date could be mid- December.
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Grain yield kg /fed, plant height (cm), flower height (cm)
and number of branches / flower were significantly affected by N
fertilizer levels over both seasons of the study. Increasing N
level to 40 and 80 kg N per fed increased grain yield by 48.15%
and 93.43% compared to the control over the two seasons,
respectively. All kind of interactions significantly affected grain
yield of quinoa in both seasons of the study.
3- Recommendations
The current study used different ways to avoid the adverse
effect of climate change on wheat plant .The results showed the
following recommendations.
The early sowing date (15 Nov) was the best sowing date
to help wheat plants to escape from heat stress especially during
grain filling period.
The interaction between foliar plant growth promoting and
N levels could be recommended to give highest values of yield
during the two seasons. Ascobeen treatment with 80 kg N was
the best, to obtain optimum yield with lower cost for producing
wheat yield.
The previous results indicate that using Ascobeen from 10
to 25 % provides the recommended amount of nitrogen
fertilization followed by spraying mixture of Ascobeen and
Potassien.
So, we could recommend that Ascobeen treatment was the
suitable combination with recommended dose of nitrogen (80 kg
n/fed) during sowing dates to increase the yield of wheat plants
and prolong the grain filling period to escaping from heat stress
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compared with other rates of nitrogen.
Simple correlation, multiple linear regression, stepwise
regression and factor analysis explains the multivariate structure.
So, we could recommend breeders to select among yield
components and environmental variables under expected
climatic change as a result of this study.
Successful planting of quinoa under Egyptian condition,
significant show the effect of the interaction among seasons,
sowing dates and N fertilizer levels on the studied characters
namely: grain yield (kg/fed), plant height (cm),flower height
(cm),and Number of branches per flower.
Highest grain yield (kg/fed), plant height (cm), flower height
(cm), and No. of branches per flower were obtained in the
second season by sowing on 15 November and applying 80 Kg
N/fed.