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

被引:8
作者
Yilmaz, G. [1 ]
机构
[1] Harran Univ, Dept Environm Engn, TR-63100 Sanliurfa, Turkey
关键词
global warming; meteorological data; plowing; soil temperature; stepwise regression; CARBON-DIOXIDE EMISSION; SOIL RESPIRATION; SODA-LIME; TILLAGE; EFFLUX; FLUXES; SEQUESTRATION; IRRIGATION; RATES;
D O I
10.1016/S1002-0160(12)60019-8
中图分类号
S15 [土壤学];
学科分类号
0903 ; 090301 ;
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 1 110 kg CO2 ha(-1) week(-1) and from 96 to 782 kg CO2 ha(-1) week(-1) 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.
引用
收藏
页码:322 / 332
页数:11
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