EVALUATION OF SOME ADAPTATION METHODS FOR THE TREATMENT OF WHEAT PRODUCTIVITY REDUCTION UNDER EXPECTED CLIMATIC CHANGE
Submitted By Eman Mahmoud Ahmed Hussein;
Abstract
Four field experiments were carried out on two crops, wheat (Sakha 93)and quinoa at an experimental field on Kom-Hamada, El-Behara Governorate during the two seasons of 2010/2011 and 2011/2012 using three plant growth promoting namely Ascobeen, Potassien and their mixture in three sowing dates (15/11, 15/12 and15/1),and four N levels of 0, 40, 80 and 120 kg N/fed to evaluate some adaptation methods to treat wheat productivity reduction under expected climatic change and using quinoa as supporter plant for wheat.The strip plot design in three replications was used and the sowing dates were assigned in the horizontal strips while nitrogen fertilizer levels were arranged in the vertical strips.
This study aimed to determine the optimum date, evaluate the effect of spraying plant growth promoting, using Quinoa plant as a supporter crop for wheat and using some multivariate analysis as a statistical tools for determine the important components (characters) that affecting wheat plants to escape from the adverse effects of climatic change especially at the grain filling period.
Wheat Experiments:
The results showed that the optimum sowing date for wheat was 15/11 in both seasons where the highest yield being16.66 and 16.54 ard/fedwas obtained 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 bothseasons. SprayingAscobeen significantly increased yield of grain by 18.74% and 19.17% in the first and second seasons, respectively compared to the control treatment. Similarly, spraying a mixture ofAscobeen 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.
Increasing N level to 40, 80 and120 kg N/ fed increased grain yield by 27.62%, 46.23% and 43.85% in the first season and 17.90%, 30.82% and 40.23 in the second season. All kind of interactions significantly affected grain yield of wheat in both seasons of the study.
Result of simple correlation coefficients cleared that environmental variables effect as indirect contribution in yield because it's highly significant relation in yield components than yield. So, we could be recommending breeders to breed among yield components and environmental variables.
Multiple regression analysis present that all studied variables recorded (R2) equal 75% from the total variation in yield meaning that this study included most efficient variables. On the other hand the adjusted R2 was 71.7 % that nearest to unadjusted R2 indicating that the sample size was suitable and these estimates were of highly accurate.
The stepwise multiple regression analysis results indicated that the most important contributing variable plant length followed by spike length from yield components as well as the most efficient variable of climatic variables was humidity followed by effect of temperature, So, we could be recommended that these variable were the limit variable in wheat plant yield.
Also, step wise equation scored 73.9 % with removing only number of spikelet's/spike comparing with multiple regression that scored 75% from total variation in yield that led us to applying factor analysis.
Factor analysis grouping twelve studied variables into three main factors as the efficient of contributed in the total variation in the dependent structure, namely environmental factor (30.66%), yield factor (20.04%) and humidity factor (13.69%) with total variation 73.40 %. These factors accented the highest communality h2for each variable in each factor and its total communality h2for all factors.
Results of all used statistical tools close to be similar with others about other finding indicating that these models were using in right direction and it estimates were more accurate.
Quinoa Experiments:
The results indicated thatquinoa 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 can be mid- December. 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.
This study aimed to determine the optimum date, evaluate the effect of spraying plant growth promoting, using Quinoa plant as a supporter crop for wheat and using some multivariate analysis as a statistical tools for determine the important components (characters) that affecting wheat plants to escape from the adverse effects of climatic change especially at the grain filling period.
Wheat Experiments:
The results showed that the optimum sowing date for wheat was 15/11 in both seasons where the highest yield being16.66 and 16.54 ard/fedwas obtained 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 bothseasons. SprayingAscobeen significantly increased yield of grain by 18.74% and 19.17% in the first and second seasons, respectively compared to the control treatment. Similarly, spraying a mixture ofAscobeen 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.
Increasing N level to 40, 80 and120 kg N/ fed increased grain yield by 27.62%, 46.23% and 43.85% in the first season and 17.90%, 30.82% and 40.23 in the second season. All kind of interactions significantly affected grain yield of wheat in both seasons of the study.
Result of simple correlation coefficients cleared that environmental variables effect as indirect contribution in yield because it's highly significant relation in yield components than yield. So, we could be recommending breeders to breed among yield components and environmental variables.
Multiple regression analysis present that all studied variables recorded (R2) equal 75% from the total variation in yield meaning that this study included most efficient variables. On the other hand the adjusted R2 was 71.7 % that nearest to unadjusted R2 indicating that the sample size was suitable and these estimates were of highly accurate.
The stepwise multiple regression analysis results indicated that the most important contributing variable plant length followed by spike length from yield components as well as the most efficient variable of climatic variables was humidity followed by effect of temperature, So, we could be recommended that these variable were the limit variable in wheat plant yield.
Also, step wise equation scored 73.9 % with removing only number of spikelet's/spike comparing with multiple regression that scored 75% from total variation in yield that led us to applying factor analysis.
Factor analysis grouping twelve studied variables into three main factors as the efficient of contributed in the total variation in the dependent structure, namely environmental factor (30.66%), yield factor (20.04%) and humidity factor (13.69%) with total variation 73.40 %. These factors accented the highest communality h2for each variable in each factor and its total communality h2for all factors.
Results of all used statistical tools close to be similar with others about other finding indicating that these models were using in right direction and it estimates were more accurate.
Quinoa Experiments:
The results indicated thatquinoa 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 can be mid- December. 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.
Other data
| Title | EVALUATION OF SOME ADAPTATION METHODS FOR THE TREATMENT OF WHEAT PRODUCTIVITY REDUCTION UNDER EXPECTED CLIMATIC CHANGE | Other Titles | تقييم بعض طرق التكيف لمعالجة انخفاض إنتاجية القمح فى ظل التغيرات المناخية المتوقعة | Authors | Submitted By Eman Mahmoud Ahmed Hussein | Keywords | Wheat – Quinoa – Climate Chang – Adaptation | Issue Date | 2014 |
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