The dependence of soil CO2 efflux on temperature

Fang, Changming; Moncrieff, John

doi:10.1016/s0038-0717(00)00125-5

Cited by 731 publications

(487 citation statements)

References 31 publications

Supporting

Mentioning

392

Contrasting

Unclassified

Order By: Relevance

“…It has been suggested that this function is a better and unbiased expression of the relationship between respiration and soil temperature than the standard Arrhenius function (Fang and Moncrieff 2001) for fitting measured soil respiration rates and soil temperature data. Temperature sensitivity, as expressed by Q 10 values, was then calculated by comparing respiration rates at 5°C above and below the site specific mean annual temperature (T 5 ), as described in Zimmermann et al (2010):…”

Section: Discussionmentioning

confidence: 99%

Different effects of warming and cooling on the decomposition of soil organic matter in warm–temperate oak forests: a reciprocal translocation experiment

et al. 2014

View full text Add to dashboard Cite

A reciprocal soil monolith-transfer experiment was conducted along an altitude gradient to investigate the effect of climate change on soil carbon (C) processes in two warm-temperate oak forests in Baotianman Nature Reserve, Henan Province, China. Microclimate conditions, soil surface CO 2 flux, and labile organic C were measured for in-situ and transferred soils at both high and low-elevation sites. The soil temperature at 5 cm depth was, on average, 3.27°C warmer at the low-elevation site than at the high-elevation site. Net CO 2 flux (911 g C m -2 13 months -1 , 4.7 % of total C) of soil monoliths transferred from the high to the low-elevation site (simulating warming) was substantially (44 %) greater than for high-elevation soil monoliths incubated in situ (633 g C m -2 13 months -1 , 3.3 % of total C) during 13 months of incubation. Increased extractable organic C (K 2 SO 4 -C) supply with warming partly explained the increase of soil CO 2 flux. Simulated warming also significantly increased the temperature sensitivity (Q 10 values) of soil organic matter decomposition. The positive linear relationship between microbial metabolic quotient (qCO 2 ) and Q 10 suggests a connection between microbial population

show abstract

Section: Discussionmentioning

confidence: 99%

Different effects of warming and cooling on the decomposition of soil organic matter in warm–temperate oak forests: a reciprocal translocation experiment

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Exponential regressions have often been used to describe the relationship between SR and soil temperature at seasonal scale when soil moisture or other factors are not limiting (Buchmann 2000;Janssens and Pilegaard 2003;Reth et al 2004). It is clear that SR responds positively to temperature in a number of systems (Davidson et al 2000;Fang and Moncrieff 2001;Richard et al 2004;Wiseman and Seiler 2004). Increasing temperatures can activate dormant microbes and increase microbial species richness, which potentially broadened the mineralizable carbon pools , thus promoting microbial respiration.…”

Section: Discussionmentioning

confidence: 99%

“…These empirical models typically use soil temperature (Fang et al 1998;Buchmann 2000;Janssens and Pilegaard 2003), soil moisture (Davidson et al 2000;Epron et al 2004;Sotta et al 2004) as well as their interaction (Tufekcioglu et al 2001;Lee et al 2002;Tang and Baldocchi, 2005) for large-scale SR estimates. For example, previous investigators proposed several different functions to describe the relationship between SR and soil temperature, including linear or sinusoidal regressions (Fan et al 1995;O'Connell et al 2003;Chimner 2004); exponential equations (Davidson et al 1998;Buchmann 2000;Sá nchez et al 2003;Reth et al 2004); Arrhenius equations (Lloyd and Taylor 1994;Thierron and Laudelout 1996); power models (Fang and Moncrieff 2001); and logistic models (Rodeghiero and Cescatti 2005). However, a number of field studies have clearly shown that none of these models appears to be consistently better than the others.…”

Section: Introductionmentioning

confidence: 99%

“…Besides soil temperature and soil moisture, root biomass, net primary productivity (NPP), litter inputs, microbial populations, root nitrogen concentrations, soil texture, substrate quantity and quality have all been shown to have effects on SR Buchmann 2000;Fang and Moncrieff 2001;Sá nchez et al 2003;Dilustro et al 2005). Moreover, SR is not a pure physiological response to soil temperature, but an integration of several confounding ecosystem processes (Janssens and Pilegaard 2003).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Soil temperature and biotic factors drive the seasonal variation of soil respiration in a maize (Zea mays L.) agricultural ecosystem

Han

Zhou

et al. 2006

Plant Soil

View full text Add to dashboard Cite

The diurnal and seasonal variation of soil respiration (SR) and their driving environmental factors were studied in a maize ecosystem during the growing season 2005. The diurnal variation of SR showed asymmetric patterns, with the minimum occurring around early morning and the maximum around 13:00 h. SR fluctuated greatly during the growing season. The mean SR rate was 3.16 lmol CO 2 m -2 s -1 , with a maximum of 4.87 lmol CO 2 m -2 s -1 on July 28 and a minimum of 1.32 lmol CO 2 m -2 s -1 on May 4. During the diurnal variation of SR, there was a significant exponential relationship between SR and soil temperature (T) at 10 cm depth: SR ¼ ae bT . At a seasonal scale, the coefficient a and b fluctuated because the biomass (B) increased a, and the net primary productivity (NPP) of maize markedly increased b of the exponential equation. Based on this, we developed the equationto estimate the magnitude of SR and to simulate its temporal variation during the growth season of maize. Most of the temporal variability (93%) in SR could be explained by the variations in soil temperature, biomass and NPP of maize. This model clearly demonstrated that soil temperature, biomass and NPP of maize combined to drive the seasonal variation of SR during the growing season. However, only taking into account the influence of soil temperature on SR, an exponential equation over-or underestimated the magnitude of SR and resulted in an erroneous representation of the seasonal variation in SR. Our results highlighted the importance of biotic factors for the estimation of SR during the growing season. It is suggested that the models of SR on agricultural sites should not only take into account the influence of soil temperature, but also incorporate biotic factors as they affect SR during the growing season.

show abstract

“…To obtain the estimated crop transpiration and CO 2 assimilation from the measured values, the observed canopy CO 2 exchange was partitioned into soil respiration (R e ) and net CO 2 assimilation (A g ), while the observed evapotranspiration of soil-crop system was partitioned into soil evaporation and crop transpiration. Soil respiration was estimated by a simple equation [35] :…”

Section: Measurementmentioning

confidence: 99%

Modeling the crop transpiration using an optimality-based approach

Lei

Yang

Schymanski

et al. 2008

Sci. China Ser. E-Technol. Sci.

View full text Add to dashboard Cite

Evapotranspiration constitutes more than 80% of the long-term water balance in Northern China. In this area, crop transpiration due to large areas of agriculture and irrigation is responsible for the majority of evapotranspiration. A model for crop transpiration is therefore essential for estimating the agricultural water consumption and understanding its feedback to the environment. However, most existing hydrological models usually calculate transpiration by relying on parameter calibration against local observations, and do not take into account crop feedback to the ambient environment. This study presents an optimality-based ecohydrology model that couples an ecological hypothesis, the photosynthetic process, stomatal movement, water balance, root water uptake and crop senescence, with the aim of predicting crop characteristics, CO 2 assimilation and water balance based only on given meteorological data. Field experiments were conducted in the Weishan Irrigation District of Northern China to evaluate performance of the model. Agreement between simulation and measurement was achieved for CO 2 assimilation, evapotranspiration and soil moisture content. The vegetation optimality was proven valid for crops and the model was applicable for both C 3 and C 4 plants. Due to the simple scheme of the optimality-based approach as well as its capability for modeling dynamic interactions between crops and the water cycle without prior vegetation information, this methodology is potentially useful to couple with the distributed hydrological model for application at the watershed scale.crop transpiration, CO 2 assimilation, optimality, feedback, ecohydrological model

show abstract

The dependence of soil CO2 efflux on temperature

Cited by 731 publications

References 31 publications

Different effects of warming and cooling on the decomposition of soil organic matter in warm–temperate oak forests: a reciprocal translocation experiment

Different effects of warming and cooling on the decomposition of soil organic matter in warm–temperate oak forests: a reciprocal translocation experiment

Soil temperature and biotic factors drive the seasonal variation of soil respiration in a maize (Zea mays L.) agricultural ecosystem

Modeling the crop transpiration using an optimality-based approach

Contact Info

Product

Resources

About