Seasonal Variation of Cumulative CO₂ Emission from a Vertisol Under Apricot Orchard in Semi-Arid Southeast Turkey

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• 作者：G. YILMAZ ; ^{gyilmaz@harran.edu.tr}
• 关键词：global warming ; meteorological data ; plowing ; soil temperature ; stepwise regression
• 刊名：Pedosphere
• 出版年：2012
• 期刊代码：16_10020160
• 类别：abs
• 出版时间：June, 2012
• 卷：22
• 期：3
• 页码：322-332
• 文件大小：387 K

摘要

Understanding the factors affecting the CO₂ emission from agricultural practices is crucial for global warming. A study was performed in an apricot orchard field in the experimental farm of the Harran University, Southeast Turkey, to i) quantify weekly and seasonal variations of the CO₂ emissions from a Vertisol under apricot orchard; ii) evaluate the difference in CO₂ emission between the area under trees and rows; and iii) assess the relationships between the amounts of CO₂ emissions and environmental parameters for better use and management of the soils from the view point of carbon balance and flux in a semi-arid environment under drip irrigation. Soil CO₂ emission measurements were performed during May 2008 and May 2010, from both under tree crowns (CO₂-UC) and between tree rows (CO₂-BR), on a weekly basis in southeast Turkey with a semi-arid climate. CO₂ emissions were statistically correlated with weather and soil parameters such as air temperature, relative humidity, rainfall, soil water content, and soil temperature at various depths from 5 to 100 cm. The weekly emissions ranged from 82 to 1110 kg CO₂ ha^鈭? week^鈭? and from 96 to 782 kg CO₂ ha^鈭? week^鈭? in CO₂-UC and CO₂-BR, respectively. Increase in CO₂ emission in the second year was due to increases in mean air and soil temperatures. The weekly and monthly cumulative CO₂ emissions were positively correlated with the air and soil temperatures. Multiple linear regression analysis explained 35%and 83%variations in average weekly and monthly CO₂ emissions, by using meteorological data. Including the interaction effects of meteorological parameters in regression equations nearly doubled the variance explained by the regression models. According to stepwise regression analysis, soil and air temperatures were found to have the most significant impact on the temporal variability of the soil CO₂ emission.