Soil respiration under different land uses in Eastern China

中国东部不同土地利用方式下的土壤呼吸作用

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Abstract

Land-use change has a crucial influence on soil respiration, which further affects soil nutrient availability and carbon stock. We monitored soil respiration rates under different land-use types (tea gardens with three production levels, adjacent woodland, and a vegetable field) in Eastern China at weekly intervals over a year using the dynamic closed chamber method. The relationship between soil respiration and environmental factors was also evaluated. The soil respiration rate exhibited a remarkable single peak that was highest in July/August and lowest in January. The annual cumulative respiration flux increased by 25.6% and 20.9% in the tea garden with high production (HP) and the vegetable field (VF), respectively, relative to woodland (WL). However, no significant differences were observed between tea gardens with medium production (MP), low production (LP), WL, and VF. Soil respiration rates were significantly and positively correlated with organic carbon, total nitrogen, and available phosphorous content. Each site displayed a significant exponential relationship between soil respiration and soil temperature measured at 5 cm depth, which explained 84-98% of the variation in soil respiration. The model with a combination of soil temperature and moisture was better at predicting the temporal variation of soil respiration rate than the single temperature model for all sites. Q10 was 2.40, 2.00, and 1.86-1.98 for VF, WL, and tea gardens, respectively, indicating that converting WL to VF increased and converting to tea gardens decreased the sensitivity of soil respiration to temperature. The equation of the multiple linear regression showed that identical factors, including soil organic carbon (SOC), soil water content (SWC), pH, and water soluble aluminum (WSAl), drove the changes in soil respiration and Q10 after conversion of land use. Temporal variations of soil respiration were mainly controlled by soil temperature, whereas spatial variations were influenced by SOC, SWC, pH, and WSAl.

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