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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 110 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 111 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. 112 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 113 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 114 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. 115 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 116 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. |