Exclusion of plant input affects the temperature sensitivity of soil carbon decomposition

doi:10.21203/rs.3.rs-1553047/v1

PDF

Research Article

Exclusion of plant input affects the temperature sensitivity of soil carbon decomposition

https://doi.org/10.21203/rs.3.rs-1553047/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Plant residue input plays a vital role in cropland carbon (C) balance, which can be greatly affected by climate change. Our knowledge of the effect of plant residue on soil respiration is crucial for evaluating C exchange between the atmosphere and terrestrial ecosystems, but large uncertainties remain in the effect of plant input on the temperature sensitivity (Q₁₀) of soil organic carbon (SOC) decomposition in cropland ecosystems. Here, we tested the effect of long-term exclusion of plant input on the Q₁₀ of SOC decomposition by incubating soils collected from two bare fallow (including a Mollisol and an Alfisol with different climate and land-use history) and adjacent old field plots at 10°C and 20°C for 815 days. The ‘equal-time’, ‘equal-C’, ‘one-pool model’ and ‘two-pool model’ methods were used to evaluate the Q₁₀ of SOC decomposition. Results indicated that in the early stage of incubation, exclusion of plant input increased the Q₁₀ (Alfisol) or had minor effect on the Q₁₀ (Mollisol), which may be related to the interactions between substrate quality decrease (increase of chemical stability) and clay mineral suppression on microbial decomposition. However, in the later stage of incubation, plant residue removal decreased the Q₁₀ values in both soils possibly due to the limitation of substrate availability on microbial decomposition. Overal, the role of plant input in the temperature sensitivity of SOC decomposition should be considered when predicting SOC stocks in a future warmer world.

decomposition

Q10

soil warming

substrate quality

substrate availability

bare fallow

Soil organic matter (SOM) stores three times more carbon (C) than the atmosphere (Jobbágy and Jackson, 2000) and even a small change in soil organic carbon (SOC) stock can significantly affect the atmospheric CO₂ concentration (Kirschbaum, 2000). Factors affecting SOC decomposition include abiotic (e.g., temperature and moisture) and biotic (e.g., substrates from plants, microbes and animals). The relationships between SOC decomposition and temperature have been studied by many scientists previously (Crowther et al., 2016; Knorr et al., 2005; Trumbore and Czimczik, 2008). But there is still uncertainty about the direction and magnitude of SOC responses to climate change (Conant et al., 2011; Bradford et al., 2016). Some studies showed that rising temperatures greatly stimulated SOC decomposition and resulted in an increase in net SOC loss to the atmosphere (Conant et al., 2008b; Hopkins et al., 2012). Meanwhile, the temperature sensitivity (Q₁₀, the proportional change in SOC decomposition with a 10 ℃ increase in temperature) of SOC decomposition may also be altered due to the changes in substrate availability, nutrient cycling and microbial community composition (Gershenson et al., 2009; Zhu and Cheng, 2011; Karhu et al., 2014; Jia et al., 2019; Zhang et al., 2021).

Plant-derived organic input (plant residues, root exudates, etc.) to soil is an important source of substrates that support the growth and activity of heterotrophic microbes in the soil (Hooker et al., 2008). Long-term exclusion of plant input would reduce the quality and quantity of SOM, as well as alter the microbial activity and community structure (Kemmitt et al., 2008; Nunan et al., 2015; Paterson et al., 2011) and thus influence the direction and magnitude of SOC decomposition in response to warming (Gershenson et al., 2009; Gong et al., 2019). Some studies showed that exclusion of plant input increases the temperature sensitivity of SOC decomposition (Gong et al., 2019; Lefèvre et al., 2014) because SOC is increasingly dominated by the recalcitrant organic compounds due to the continuous depletion of the chemical active substrates (Farina et al., 2021; Franko and Merbach, 2017). This observation supports the carbon-quality-temperature (CQT) hypothesis, which suggests that the Q₁₀ of SOC decomposition depends on the SOC quality (Wagai et al., 2013). The theoretical relationship between SOC decomposition rate and temperature has been described using the Arrhenius function (Lützow and Kögel-Knabner, 2009; Sierra, 2012). However, the application of Arrhenius function is limited under conditions of low substrate availability (Lützow and Kögel-Knabner, 2009). Soil microbial communities have been shown to be substrate-limited (Li et al., 2020; Liu et al., 2006; Pold et al., 2017). The confounding effects of substrate quality and substrate availability may bias the estimate of the temperature sensitivity of SOC decomposition (Erhagen et al., 2015). Some studies indicated that plant/litter manipulation increased the temperature sensitivity of SOC decomposition, which is attributed to the stimulation of microbial growth and activation of dormant microbes by labile substrate addition (Thiessen et al., 2013; Zhu and Cheng, 2011). This observation implies the limitation of substrate availability on the response of microbial decomposition of SOC to temperature increase. The theoretical relationship between SOC decomposition rate and substrate availability has been described using the Michaelis–Menten model (Davidson et al., 2012). The modification of substrate availability on the temperature sensitivity of SOC decomposition can be attributed to the cancelling effect between K_m (Michaelis–Menten constant, representing the affinity of enzymes for the substrates) and V_max (the maximal rate of enzymatic activity at a given temperature) (Lützow and Kögel-Knabner, 2009; Gershenson et al., 2009). To the best of our knowledge, few studies have considered the comprehensive effect of substrate quality and substrate availability in studying the effect of plant input removal on the temperature sensitivity of SOC decomposition.

In order to better understand the temperature sensitivity of SOC decomposition, four different methods have been used in published reports so far: the total mass loss (Thornley and Cannell, 2001; Li et al., 2020); the time required for a given percentage of mass loss (Conant et al., 2008a); the first-order kinetic one-pool model, and the first-order multi-pool model (Reichstein et al., 2000; Rey and Jarvis, 2006; Liang et al., 2015). The first method relies on simultaneous CO₂ emission rates measured at different temperatures (Karhu et al., 2014; Lin et al., 2015). Compared with other methods, this method not only is easier to calculate, but also better reflects the Q₁₀ for soil system at specific time. However, this method is likely to underestimate the Q₁₀ after the initial stage of incubation. This is because the rate of soil respiration typically declines and this decline is faster at high than low temperature, leading to differences in the quality and amount of soil C respired at the same point of incubation time (Conant et al., 2008a). The second method assumes that during incubation the lability of the remaining SOC decreases as labile substrate is lost (Conant et al., 2008a; Zang et al., 2020). Thus it is possible to separate diverse C pools and subsequently estimate their Q₁₀ during the incubation (Lin et al., 2015). The third and fourth methods use one- and multi-pool models to fit the rate of SOC decomposition at each temperature and calculate the Q₁₀ (Reichstein et al., 2000; Rey and Jarvis, 2006). The one-pool model method assumes the soil as a single C pool, reflecting the Q₁₀ of the potential mineralized pool, but fails to provide information on the recalcitrant C pool. The multi-pool models (e.g. two- and three-pool models) clearly describe the dynamics of temperature sensitivity with the changes in SOC compounds (Liang et al., 2015). However, these multi-pool models have large uncertainty in their parameters (Weihermüller et al., 2018; Tang and Riley, 2020). These four methods are complementary in assessing the temperature sensitivity of either total SOC or distinct pools (Lin et al., 2015; Reichstein et al., 2000; Zang et al., 2020). However, empirical studies using different methods to investigate the temperature sensitivity of total or various SOC pools under environmental change remain scarce.

In this study, we investigate the influence of long-term exclusion of plant input on the temperature sensitivity of SOC decomposition by incubating soils from old field and bare fallow plots at 10 ℃ and 20 ℃ for 815 days. We aim: (1) to estimate the temperature sensitivity of SOC decomposition by comparing the Q₁₀ values by using four methods; and (2) to investigate the effect of long-term exclusion of plant input on the temperature sensitivity of SOC decomposition.

2.1. Soil collection

Soils were collected from two bare fallow plots and two abandoned farm plots (old field) of the Hailun Agricultural experimental station (47°26′N, 126°38′E, and 240 m above sea level) and the Shenyang Agricultural experimental station (41˚32′N, 122˚23′E, and 50 m above sea level) of Chinese Academy of Sciences (China). These two experimental stations were respectively located in Heilongjiang and Liaoning province, Northeast China. The soils from Hailun and Shenyang were classified as Mollisol and Alfisol, respectively (Soil Survey Staff, 2010). Soils were taken from the top 20 cm in September 2012. At the time of soil collection for this study, these two bare fallow plots had been maintained continuously free of vegetation by frequent hand weeding for 23 years (1990–2012), while these two old field plots had remained continuous fresh organic input for 23 years (1990–2012) with freely growing weeds. More details of the sites and plots have been published previously (Zhang et al. 2017). The protocol used for soil sampling was similar to the one described by Zhang et al. (2021). Briefly, within each plot, soils were collected from five randomly chosen points, and combined into single, composite samples. Therefore, we obtained four composite soil samples which were relatively homogenous and representative of each plot. All of the soil samples were brought to the laboratory and sieved through 2 mm screen immediately, with fine roots, plant debris and visible stones being removed. Physical and chemical properties of these four soils are given in Table 1. All soils were air-dried and stored at room temperature (20 ℃) before lab incubation.

Table 1

Properties of the four soils. Values are given as mean ± se (n = 3 for all analyses). Different letters represent a significant difference between the bare fallow soil and the old field soil within each soil type (P < 0.05).
Property	Mollisol		Alfisol
Property	Bare fallow	Old field	Bare fallow	Old field
pH(1:2 soil: water)	6.01 ± 0.03 a	6.17 ± 0.02 a	6.10 ± 0.12 a	6.01 ± 0.18 a
Clay (%)	33.8 ± 1.5 a	35.2 ± 0.6 a	10.1 ± 1.8 a	12.6 ± 0.8 a
SOC content (g kg^− 1)	21.81 ± 0.07 a	35.55 ± 0.11 b	11.44 ± 0.14 a	16.60 ± 0.05 b
Total N (g kg^− 1)	1.78 ± 0.01 a	2.65 ± 0.02 b	0.94 ± 0.00 a	1.37 ± 0.01 b
C/N	12.3 ± 0.1 a	13.5 ± 0.1 b	12.2 ± 0.1 a	12.1 ± 0.1 a

2.2. Soil incubation

We put 200 g of air-dried and homogenized soil into each polypropylene column (5 cm in diameter, 25 cm in height). Both ends of each column were tightly plugged with one-hole rubber stopper connected to the flexible plastic ventilation tubing. To avoid anaerobic condition, fresh air was pumped through each column via automatic timer-controlled aeration for one hour in each 4-hour interval during the entire incubation experiment. All soil columns were rewetted to soil moisture of 60% of water holding capacity (WHC) and incubated for 815 days. Distilled water was added at regular intervals (about one week) to maintain the water content in the columns. The soils were incubated at two temperatures (10°C and 20°C) in processor-controlled incubators (SHELLAB LI20-2, Sheldon Manufacturing Inc., USA, with a temperature control accuracy and evenness of ± 0.05°C) based on the ranges of temperatures experienced in the field at the two sites. Moreover, there was little activity of soil enzymes below 10°C (Rey and Jarvis, 2006), thus we adopted temperatures above 10°C. The experiment included two soil types (Mollisol and Alfisol), two land-use types (bare fallow and old field) and two temperature levels (10°C and 20°C). There were four replicates for each treatment combination and 32 soil columns in total.

2.3. CO₂ trapping

We measured soil respiration at day 8, 14, 28, 45, 60, 75, 90, 128, 144, 158, 187, 202, 240, 263, 299, 330, 360, 410, 480, 550, 585, 630, 670, 702, 756 and 815, respectively. The protocol used for CO₂ trapping followed that of Lin et al. (2015). Briefly, we used an air pump to force ambient air through a soda-lime column to producing CO₂-free air. The CO₂-free air entered the manifold, from which individual tube led to individual sample inflow tube. Before CO₂ trapping, initial CO₂ in each soil column was removed by ventilating soil column with CO₂-free air at a constant flow rate of 60 ± 2 mL min^− 1. After 2 hours, a CO₂ trapping bottle containing 12 mL 0.5 M NaOH solution was used to trap the subsequent CO₂ produced inside the soil columns for 24 h or longer (if needed). Blanks were also trapped to correct for inorganic C from NaOH stock solution and sample handling. After CO₂ trapping, the NaOH solution was transferred into head space bottle with sealed cap. An aliquot of each NaOH solution was analyzed for total inorganic C using multi N/C® 2000 TOC analyzer (Analytik Jena, Germany).

2.4. Calculations and statistical analysis

The temperature sensitivity (Q₁₀) of SOC decomposition was calculated using four major methods.

‘Equal-time’ method. The Q₁₀ values based on CO₂ efflux at 10 ℃ and 20 ℃ were estimated as follows (Lin et al., 2015):

Q₁₀ (t) = C_cum (T_h) / C_cum (T_l) (1)

where C_cum (T_h) and C_cum (T_l) are the cumulative amount of mineralized C at a higher and low temperature.

‘Equal-C’ method. We calculated the temperature sensitivity by comparing the time required to mineralize the same amount of SOM. In general, samples at low temperature need more time to respire an equal amount of SOC than at higher temperature. Therefore, by dividing the time taken at low temperature (t_Tl) by the time taken at a higher temperature (t_Th), we obtained the temperature sensitivity which was defined as Q_10 − q (Conant et al., 2008a):

Q_10 − q = (t_Tl / t_Th)^{(10/(Th−Tl))} (2)

where t_Tl and t_Th are the time required to mineralize the amount of soil C at low (T_l) and a higher (T_h) temperature, respectively.

‘One-pool model’ method. First, the cumulative mineralized C of each soil sample and temperature was fitted individually to the first-order one-pool model:

C_cum (t) = C₀ × (1- e^− k×t) (3)

where C_cum (t) is the cumulative C mineralized until time t (mg C g^− 1 initial C), C₀ is the ‘potential’ mineralizable C (mg C g^− 1 initial C), k is the decomposition rate constant for decomposition of C (day^− 1), and t is time (day). After then, the Q₁₀ by the ‘first-order one-pool fitting’ method was calculated as the ratio of k at 20°C to k at 10°C for SOC decomposition (Lin et al., 2015; Zang et al., 2020).

‘Two-pool model’ method. A similar procedure to ‘One-pool model’ method was followed:

C_cum (t) = C₁ × (1- e^− k1×t) + C₂ × (1‒ e^− k2×t) (4)

where C_cum (t) is the cumulative C mineralized up until time t (mg C g^− 1 initial C), C₁ is the mass of organic labile C fraction (mg C g^− 1 initial C), C₂ is the mass of more resistant, recalcitrant C fraction (mg C g^− 1 initial C), and k₁, k₂ are the first-order kinetic decomposition rate constants for the labile and more recalcitrant C fractions, respectively (day^− 1). C₁ + C₂ is equal to the total amount of initial C. We forced the following constraints: that (a) k₁ was larger than k₂, and (b) that k₁ was larger than 0. The values of the parameters C₁, k₁ and k₂ of each soil were obtained at the higher temperature (T = 20°C). Then we fitted the same model to soil respiration at 10°C, assuming C₁ was not affected by incubation temperature (Rey and Jarvis, 2006). Then the Q₁₀ values of both labile and recalcitrant C by the ‘first-order two-pool fitting’ method were calculated as the ratio of k at 20°C to k at 10°C for SOC decomposition (Reichstein et al., 2005; Rey and Jarvis, 2006).

We investigated differences in C pool sizes between soil types with a one-way ANOVA. We tested the main effects of land-use type and temperature, and their interaction on the pool-specific decomposition rate with two-way ANOVA, followed by Tukey HSD test if the ANOVA result was significant (P < 0.05). The differences among the Q₁₀ values estimated by ‘equal-time’ method was tested by two-way ANOVA when considering land-use type (bare fallow vs. old field), soil sampling date and their interactions. The differences among the Q₁₀ values estimated by ‘equal-C’ method was tested by two-way ANOVA when considering land-use type, the amount of soil C respired and their interactions. The differences among the Q₁₀ values estimated by ‘one-pool model’ method was tested by one-way ANOVA. The differences among the Q₁₀ values estimated by ‘two-pool model’ method was tested by two-way ANOVA when considering land-use type, the stability of SOC and their interactions. All curves were fitted using nonlinear curve fit procedures using OriginPro 8.5 (Originlab Origin, Northampton, USA). The goodness of fit relative to the number of model parameters was evaluated by AIC (Burnham and Anderson, 2004). The model with a smaller AIC value is more parsimonious (Saffron et al., 2006). All statistical analyses were carried out using SPSS (Version 20, SPSS IBM Corp., Armonk, New York, NY).

3.1. Soil respiration

The cumulative C respired (C respired per g of initial C) over the incubation period (815 days) varied significantly with temperature in all soils. In the meantime, the amount of cumulative C mineralization and the shape of these time series also varied considerably between sites (Fig. 1). The total amount of C respired at 20°C over the incubation period ranged from 95.2 (Mollisol) to 170.4 (Alfisol) mg C per g of initial C. Both the one-pool and two-pool models fitted the incubation data well (Fig. 1). In both ‘one-pool’ and ‘two-pool’ models, there was no consistent difference of pool size (Table 2) and pool-specific decomposition rate (Table 3) between land-use types in these two sites. As estimated by the ‘two-pool’ model, the labile C pool size was small (< 5% of total C) in all soils, and the contribution of the labile pool to CO₂ emission reduced quickly with the incubation progress, while the contribution of the recalcitrant pool increased (Fig. 1).

Table 2

Coefficients obtained from fitting the cumulative carbon mineralization data at 20 ℃. P values, R² values and Akaike information criterion (AIC) values in the one-pool and two-pool models simulated with the same soil incubation data. Different letters indicate significant differences between soil land-use types within each soil type by Tukey HSD test (P < 0.05).
Soils	One-pool				Two-pool
Soils	C₀ (mg g^− 1 initial C)	P	R²	AIC	C₁ (mg g^− 1 initial C)	C₂ (mg g^− 1 initial C)	P	R²	AIC
Mollisol
Old field	105.1 ± 2.5 a	< 0.001	0.987	66.0	25.0 ± 0.8 b	975.0 ± 0.8 a	< 0.001	0.998	23.4
Bare fallow	121.6 ± 3.3 b	< 0.001	0.991	64.0	19.6 ± 0.9 a	980.4 ± 0.9 b	< 0.001	0.999	13.7
Alfisol
Old field	221.6 ± 4.6 a	< 0.001	0.995	68.9	37.9 ± 2.7 a	962.1 ± 2.7 a	< 0.001	0.998	56.3
Bare fallow	212.0 ± 3.7 a	< 0.001	0.992	83.0	33.6 ± 1.4 a	966.4 ± 1.4 a	< 0.001	0.999	34.1

Table 3

Pool-specific decomposition rates obtained from the one-pool model and two-pool model (Sanford and Smith, 1972). k₀ is the decomposition rate (day^− 1) for the potential mineralizable C, and k₁ and k₂ are the decomposition rates for the labile and recalcitrant C pools, respectively (day^− 1) (Andrén and Paustian, 1987). Letters indicate significant differences within each soil type by Tukey HSD test (P < 0.05).
Soil	Temperature (℃)	One-pool	Two-pool
Soil	Temperature (℃)	k₀ (×10^− 3)	k₁ (×10^− 2)	k₂ (×10^− 5)
Mollisol
Old field	10	0.90 ± 0.03 a	0.67 ± 0.03 a	2.82 ± 0.16 a
	20	2.38 ± 0.03 c	1.27 ± 0.08 bc	9.31 ± 0.27 b
Bare fallow	10	0.79 ± 0.03 a	0.85 ± 0.08 ab	3.60 ± 0.01 a
	20	1.82 ± 0.07 b	1.30 ± 0.13 c	10.3 ± 0.18 c
ANOVA (P-values)
Land-use type (L)		< 0.001	0.314	0.002
Temperature (T)		< 0.001	< 0.001	< 0.001
L × T		< 0.001	0.484	0.590
Alfisol
Old field	10	0.70 ± 0.01 a	0.62 ± 0.03 a	6.24 ± 0.19 a
	20	1.76 ± 0.01 c	0.93 ± 0.08 b	19.0 ± 0.31 c
Bare fallow	10	0.79 ± 0.03 b	0.66 ± 0.05 ab	7.74 ± 0.49 b
	20	1.88 ± 0.02 d	1.36 ± 0.09 c	19.4 ± 0.29 c
ANOVA (P-values)
Land-use type (L)		< 0.001	0.008	0.012
Temperature (T)		< 0.001	< 0.001	< 0.001
L × T		0.453	0.016	0.103

3.2. Temperature sensitivity of SOC decomposition calculated based on four methods

In general, the Q₁₀ calculated by ‘equal-time’ method showed an increase over the first 202 days despite the initial decrease and relative stability thereafter (Fig. 2). However, this increase of Q₁₀ was less obvious in the bare fallow-treated Alfisol. In the initial stage of incubation, the Q₁₀ was higher in the bare fallow soil than that in the old field soil, and this stage lasted for 28 days in the Mollisol and 202 days in the Alfisol. Afterwards, the Q₁₀ was lower in the bare fallow soil than that in the old field soil.

The Q₁₀ of each 1% of mineralized SOC was calculated based on the ‘equal-C’ method. During the incubation period, the amount of mineralized SOC in the Alfisol was about 2-fold of that in the Mollisol. In general, for both old field and bare fallow soils, Q₁₀ tended to increase during incubation as more soil C was mineralized (Fig. 3). Whereas, within the bare fallow soil, Q₁₀ increased to a peak value until the mineralized C equivalent to about 3% (Mollisol) or 4% (Alfisol) of total soil C, and began to decline thereafter. Within the old field soil, Q₁₀ continued to increase (Alfisol) or stabilize (Mollisol) after the first peak value. In the initial stage of soil C mineralization, the Q₁₀ was higher in the bare fallow soil than in the old field soil (Alfisol) or there was no significant difference between these two land-use types (Mollisol). Afterwards, the Q₁₀ was lower in the bare fallow soil than that in the old field soil.

With the initial parameters C₀ (the potential mineralizable C) fixed for each soil and at each temperature, the decay constants k₀ of each soil were fitted for each soil and at each temperature based on the one-pool model. For each soil, k₀ was higher at 20°C than at 10°C (Table 3). The Q₁₀ of the old field soil (2.7 for the Mollisol and 2.5 for the Alfisol) was slightly but significantly higher than that of the bare fallow soil (2.3 for the Mollisol and 2.4 for the Alfisol) for both soil types (Fig. 4).

By fitting the two-pool model to the individual cumulative data for each soil and temperature, we obtained the size of both labile and recalcitrant pools and decomposition rates k₁ and k₂ (Tables 2 & 3). For both C pools of each soil, the decomposition rate was higher at 20°C than at 10°C. Long-term exclusion of plant input decreased Q₁₀ of the recalcitrant C pool in both Mollisol and Alfisol, but had different effects on Q₁₀ of the labile C in these two soil types: exclusion of plant input had minor effect on Q₁₀ of the labile C in the Mollisol, but significantly increased Q₁₀ of the labile C in the Alfisol (Fig. 5).

4.1. Temperature sensitivity of SOC decomposition—comparison of four methods

In this study, we used four different methods to calculate the Q₁₀ values of SOC decomposition in the bare fallow soil which had been deprived of plant input for 23 years and the adjacent old field soil with continuous plant input. Both the ‘equal-time’ and ‘one-pool model’ methods reflected Q₁₀ of a specific soil system. The Q₁₀ calculated by the ‘equal-time’ method was obtained by comparing the cumulative flux of C at both higher and low temperatures at the same time after incubation started (Lin et al., 2015), while the Q₁₀ calculated by the ‘one-pool model’ method was obtained by comparing the k values (respiration rate, obtained by inversely modeling soil respiration) at both higher and low temperatures (Rey and Jarvis, 2006). The ‘equal-time’ method considered the rate of SOC decomposition at a specific time and obtained the dynamics of the Q₁₀ values with time, but it ignored the role of SOC quality in shaping the temperature sensitivity (Conant et al., 2008a; Zang et al., 2020). Indeed, the faster exhaustion of the labile fractions of SOC at higher temperature could lead to an underestimation of the temperature sensitivity of SOC decomposition unless the estimate was conducted on C pools of similar qualities (Dalias et al., 2001). Therefore, the Q₁₀ values calculated by the ‘equal-time’ method were relatively lower than those calculated by other methods (Fig. 2). However, the ‘equal-time’ method produced the smallest error especially for long incubation period and the Q₁₀ calculated by the ‘equal-time’ method was closer to the outcomes of SOM mineralization in a warming world (Fig. 2; Zang et al., 2020). The ‘one-pool model’ assumes the soil as a single C pool (Rey and Jarvis, 2006). Through the ‘one-pool model’ method, we obtained the decomposition rate and Q₁₀ of the potentially mineralizable pool. The decay rate and Q₁₀ value calculated by the ‘one-pool model’ method were higher than those of the labile C but lower than those of the recalcitrant C calculated by the ‘two-pool model’ method. It is mainly because the potentially mineralizable pool in the ‘one-pool model’ conceptually amounts to the sum of labile C and part of the recalcitrant C pool in the two-pool model (Table 2). A shortcoming of the one-pool model is that it cannot separate the labile and recalcitrant C pools (Rey and Jarvis, 2006). But compared with the multi-pool models, the ‘one-pool model’ is simpler for modeling and calculation. It does not have the problems of over-fitting and the ill-posed problem that generally exists in the multi-pool models (Weihermüller et al., 2018; Tang and Riley, 2020).

Both the ‘two-pool model’ and ‘equal-C’ methods considered that SOM is composed of various pools with different turnover times (Rey and Jarvis, 2006; Conant et al., 2008b; Lin et al., 2015). Through the ‘two-pool model’ method, we obtained the decomposition rate and Q₁₀ of both the labile and recalcitrant C pools. The ‘two-pool model’ resulted in a better fitting of CO₂ efflux than the ‘one-pool model’ (Fig. 1; Table 2). However, multi-pool models (e.g. two-pool model and three-pool model) primarily relied on curve fitting of CO₂ efflux, and doubts may arise from the model assumptions (Weihermüller et al., 2018; Tang and Riley, 2020). Further, the ‘two-pool model’ directly separated total SOM into labile and recalcitrant C pools, thus cannot give further dynamic information about various intermediate and recalcitrant C pools (Zang et al., 2020). The ‘equal-C’ method provided more information on the Q₁₀ of each individual pool (Conant et al., 2008a). It is assumed that changes in decomposition rates of SOC during incubation are driven primarily by changes in the recalcitrance of SOC being decomposed and SOC mineralized later will be from relatively more recalcitrant C pools (Conant et al., 2008a). Compared with the other methods, the ‘equal-C’ method better describes the dynamics of the temperature sensitivity of SOC decomposition with the changes in SOM compounds.

All the above four methods of calculating the temperature sensitivity of SOC decomposition depend on the duration of incubation (Zang et al., 2020). To the best of our knowledge, most of the recent work investigating SOC decomposition and the response of decomposition rate to temperature has been based on laboratory incubations and has focused on short-term outcomes, largely overlooking the Q₁₀ of the relatively stabilized C fraction due to the limitation of duration (Conant et al., 2011). Our long-term incubation provides an opportunity to assess the Q₁₀ of the relatively stabilized C pool directly based on the respiration data. Furthermore, sieving soil before incubation experiments may bias the estimate of the Q₁₀ values due to the destruction of the macro-aggregates (Kan et al., 2022). But the overestimate of SOC decomposition may be gradually decreased over time (Zhang and Yu, 2020). Therefore, a long-term incubation over the annual time scale may help alleviate the influence of soil sieving on the estimate of Q₁₀ values.

Overall, these four methods are complementary for estimating the effect of long-term exclusion of plant input on the temperature sensitivity of either total SOC or various pools. The ‘equal-time’ method produces the smallest uncertainty in calculating Q₁₀ and more clearly reflects the temperature sensitivity of a specific soil system, while the ‘equal-C’ method may be more powerful in estimating the temperature sensitivity of separate C pools over longer incubation time.

4.2. Temperature sensitivity of SOC after long-term exclusion of plant input

Despite the large differences of plant input between the bare fallow and old field treatments, all four soils contain various components with different turnover times (Tables 2 & 3). In the ‘two-pool’ model, there was no consistent difference of pool size (Table 2) and pool-specific decomposition rate (Table 3) between the bare fallow and old field treatments in these two sites, suggesting that exclusion of plant input does not necessarily alter soil C decomposability. Our estimate was consistent with previous observation (Salomé et al., 2010). Our previous study suggested that the constancy in soil texture and the similarity in soil pH and microbial community structure likely caused the similarity in soil C decomposition between the bare fallow and the old field treatments (Zhang et al. 2021). In contrast to the tendency in soil C decomposition, the temperature sensitivity of soil C varied significantly between the bare fallow and old field treatments (Figs. 2, 3, 4 & 5). The ‘equal-time’, ‘equal-C’, and ‘two-pool model’ methods showed that long-term exclusion of plant input decreased the temperature sensitivity of the relatively stabilized SOC, but increased or had minor effect on the temperature sensitivity of the labile SOC which was dependent on soil type (Fig. 2, 3, & 5). The contrasting effects of long-term exclusion of plant input on the temperature sensitivity of soil C pools can be explained by a shift in the factors driving Q₁₀ values, from a limitation by enzymatic potential at the beginning to limitation by substrate availability later in the incubation (Lützow and Kögel-Knabner, 2009; Cusack et al., 2010). In both bare fallow and old field treated soils, the labile SOC fraction was respired quickly during the early stage of the incubation, while the relatively stabilized SOC pool dominated microbial substrates during the later stage of the incubation (Fig. 1). At the early stage of the incubation, substrate availability was not limiting to soil respiration and maximum enzyme potential was the limiting factor (Cusack et al., 2010). Soil respiration at this stage could be well described by an Arrhenius function and the temperature sensitivity depended primarily on the enzymatic temperature response (Lützow and Kögel-Knabner, 2009). Compared with the old field soil, the soil under bare fallow is progressively depleted in chemical active substrates (e.g. sugars, amino acids and organic acids derived from root exudates) and experiences a relative enrichment in SOC with higher activation energy (Lefèvre et al., 2014; Paterson et al., 2011). According to the Arrhenius function, the higher the activation energy the higher the relative temperature sensitivity (Davidson and Janssens, 2006; Sierra, 2012). The effect of long-term exclusion of plant input on the Q₁₀ value of labile SOC in this study supports the substrate quality—temperature sensitivity theory, which suggests that the Q₁₀ of SOC decomposition depends on the SOC quality (Wagai et al., 2013). However, the frequently observed temperature sensitivity of SOC decomposition is determined by factors other than intrinsic molecular recalcitrance (Kleber, 2010; Reynolds et al., 2017; Vaughn and Torn, 2019). Compared with the Alfisol, removal of plant input has minor effect on Q₁₀ values of the labile C fraction in the Mollisol (Fig. 5). We suggest that this difference in the Q₁₀ values may be largely explained by the differences in clay content between these two soil types. Frøseth and Bleken (2015) have shown that soil clay may reduce the diffusion of enzymes and other solutes through its high specific surface, which tends to affect the decomposition rate. In our study, the high soil clay content in the Mollisol may mask the effects of substrate quality on the temperature sensitivity of SOC decomposition.

Later in the incubation, the observed soil respiration is dominated by the properties of relatively stabilized SOC pools (Fig. 1). During this stage, the disparity in substrate availability of the relatively stabilized pools between the bare fallow and old field treated soils became apparent (Nunan et al., 2015). It has been suggested that the microbial community is substrate-limited (Li et al., 2020; Liu et al., 2006; Pold et al., 2017). Thus the application of Arrhenius kinetics in describing soil respiration is limited under conditions of low substrate availability (Davidson et al., 2012; Lützow and Kögel-Knabner, 2009). According to Michaelis–Menten kinetics, soil respiration rates are further modified by substrate concentrations ([S]) and affinities of the enzymes for the substrates (Km) (Lützow and Kögel-Knabner, 2009). As both Km and the maximal activity of respiratory enzymes (V_max) are temperature dependent, the temperature sensitivity in the bare fallow soil would be underestimated due to the canceling effect of Km on the apparent Q₁₀ value under conditions of low substrate availability (Larionova et al., 2007; Eberwein et al., 2015; Erhagen et al., 2015; Li et al., 2020). This can explain the significant decrease of Q₁₀ values after 3 ~ 4% of soil C was respired in the bare fallow soil as estimated by the ‘equal-C’ method. However, the estimated Q₁₀ continuously increased in the old field soil as more C was respired (Fig. 3). It is mainly because the canceling effect of Km on the apparent Q₁₀ value is less important under conditions of higher substrate availability, thus soils with higher indigenous substrate availability tend to produce Q₁₀ values close to their intrinsic Q₁₀ values (Gershenson et al., 2009). Our observation of the dynamics of Q₁₀ values in the old field soil also supports the substrate quality—temperature sensitivity theory as the soil is progressively enriched in SOC with higher activation energy during incubation. Moreover, the labile C pool is minute (< 5%, Table 2) compared to the relatively stabilized C (including intermediate and recalcitrant C pools) and would be rapidly exhausted (Davidson and Janssens, 2006). The observed Q₁₀ values as estimated by the ‘one-pool model’ method was dominated by the properties of the relatively stabilized SOC pools (Table. 2). Therefore, the Q₁₀ estimated by the ‘one-pool model’ method was lower in the bare fallow soil than in the old field soil due to substrate limitation on the expression of temperature sensitivity in the bare fallow soil (Davidson et al., 2012). Our observation that the effect of long-term exclusion of plant input on the Q₁₀ values is time dependent reconciles the roles of substrate quality and substrate availability in determining the temperature sensitivity of SOC decomposition. That is, decreases in substrate quality are predicted to result in increased temperature sensitivity (Bosatta and Ågren, 1999), while decreases in substrate availability contribute to decreased temperature sensitivity according to model predictions by Ågren and Wetterstedt (2007). Furthermore, the time-dependency of the effect of plant input removal on Q₁₀ values also highlights the importance of long-term incubation in assessing the temperature sensitivity of SOC decomposition. Therefore, longer temporal scales coupled with determination of dynamic variables such as substrate quality, substrate availability and microbial metabolism are recommended for future studies. It should be noted that our findings are based on data from only two sites with two long-term land-use treatments (old field and bare fallow). Future research should aim to use multiple sites or land-use types from network experiments and conduct incubations at a larger range of temperature to test the generality of these findings.

Our study showed that long-term exclusion of plant input had minor effect on the tendency of soil C decomposition, but significantly affected the temperature sensitivity of soil C decomposition. The four methods are complementary for estimating the Q₁₀ of SOC decomposition. The ‘equal-time’ and ‘one-pool model’ methods consider the whole incubation period. The ‘one-pool model’ method represents the temperature sensitivity of the potentially mineralizable pool, while the ‘equal-time’ method reflects the Q₁₀ for the whole soil system at specific time. The ‘equal-C’ method is more powerful in reflecting the Q₁₀ for individual C pools, while the ‘two-pool model’ method better describes the SOC decomposition dynamics and reflects the temperature sensitivity of labile and recalcitrant SOC pools. Our study also indicated that in the early stage of incubation, exclusion of plant input increased or had minor effect on Q₁₀, depending on soil type. However, in the later stage of incubation, exclusion of plant input might, in some way, decrease Q₁₀ due to the limitation of substrate availability on soil respiration. Our results highlight the role of plant input in determing the response of SOC decomposition to temperature. More attention should be paid to the influence of plant input on the temperature sensitivity of SOC decomposition in future experimental and modeling studies of SOC decomposition.

C – carbon; N – nitrogen; C/N – the ratio of carbon to nitrogen; SOC – soil organic carbon; SOM – soil organic matter; Q₁₀ – temperature sensitivity; CQT – carbon-quality-temperature; WHC – water-holding capacity

Acknowledgements

This work was supported by the Natural Science Foundation of Zhejiang Province (LQ20D030001) and the National Natural Science Foundation of China (32101385 and 42141006).

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Ågren GI, Wetterstedt JÃM (2007) What determines the temperature response of soil organic matter decomposition? Soil Biol Biochem 39:1794–1798
Andrén O, Paustian K (1987) Barley Straw Decomposition in the Field: A Comparison of Models. Ecology 68:1190–1200
Barré P, Eglin T, Christensen BT, Ciais P, Houot S, Kätterer T, van Oort F, Peylin P, Poulton PR, Romanenkov V, Chenu C (2010) Quantifying and isolating stable soil organic carbon using long-term bare fallow experiments. Biogeosciences 7:3839–3850
Bosatta E, Ågren GI (1999) Soil organic matter quality interpreted thermodynamically. Soil Biol Biochem 31:1889–1891
Bradford MA, Wieder WR, Bonan GB, Fierer N, Raymond PA, Crowther TW (2016) Managing uncertainty in soil carbon feedbacks to climate change. Nat Clim Change 6:751–758
Burnham KP, Anderson DR (2004) Multimodel inference understanding AIC and BIC in model selection. Sociol Methods Res 33:261–304
Conant RT, Drijber RA, Haddix ML, Parton WJ, Paul EA, Plante AF, Six J, Steinweg JM (2008a) Sensitivity of organic matter decomposition to warming varies with its quality. Glob Change Biol 14:868–877
Conant RT, Ryan MG, Ågren GI, Birge HE, Davidson EA, Eliasson PE, Evans SE, Frey SD, Giardina CP, Hopkins FM, Hyvönen R, Kirschbaum MUF, Lavallee JM, Leifeld J, Parton WJ, Megan Steinweg J, Wallenstein MD, Martin Wetterstedt J, Bradford MA (2011) Temperature and soil organic matter decomposition rates - synthesis of current knowledge and a way forward. Glob Change Biol 17:3392–3404
Conant RT, Steinweg JM, Haddix ML, Paul EA, Plante AF, Six J (2008b) Experimental warming shows that decomposition temperature sensitivity increases with soil organic matter recalcitrance. Ecology 89:2384–2391
Crowther TW, Todd-Brown KEO, Rowe CW, Wieder WR, Carey JC, Machmuller MB, Snoek BL, Fang S, Zhou G, Allison SD, Blair JM, Bridgham SD, Burton AJ, Carrillo Y, Reich PB, Clark JS, Classen AT, Dijkstra FA, Elberling B, Emmett BA, Estiarte M, Frey SD, Guo J, Harte J, Jiang L, Johnson BR, Kröel-Dulay G, Larsen KS, Laudon H, Lavallee JM, Luo Y, Lupascu M, Ma LN, Marhan S, Michelsen A, Mohan J, Niu S, Pendall E, Peñuelas J, Pfeifer-Meister L, Poll C, Reinsch S, Reynolds LL, Schmidt IK, Sistla S, Sokol NW, Templer PH, Treseder KK, Welker JM, Bradford MA (2016) Quantifying global soil carbon losses in response to warming. Nature 540:104–108
Cusack DF, Torn MS, Mcdowell WH, Silver WL (2010) The response of heterotrophic activity and carbon cycling to nitrogen additions and warming in two tropical soils. Glob Change Biol 16:2555–2572
Dalias P, Anderson JM, Bottner P, Coûteaux MM (2001) Temperature responses of carbon mineralization in conifer forest soils from different regional climates incubated under standard laboratory conditions. Glob Change Biol 7:181–192
Davidson EA, Janssens IA (2006) Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440:165–173
Davidson EA, Samanta S, Caramori SS, Savage K (2012) The Dual Arrhenius and Michaelis–Menten kinetics model for decomposition of soil organic matter at hourly to seasonal time scales. Glob Change Biol 18:371–384
Eberwein JR, Oikawa PY, Allsman LA, Jenerette GD (2015) Carbon availability regulates soil respiration response to nitrogen and temperature. Soil Biol Biochem 88:158–164
Erhagen B, Ilstedt U, Nilsson MB (2015) Temperature sensitivity of heterotrophic soil CO₂ production increases with increasing carbon substrate uptake rate. Soil Biol Biochem 80:45–52
Farina R, Sándor R, Abdalla M, Álvaro-Fuentes J, Bechini L, Bolinder MA, Brilli L, Chenu C, Clivot H, Massimiliano DAM, Bene CD, Bellocchi G (2021) Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare fallow soils. Glob Change Biol 27:904–928
Franko U, Merbach I (2017) Modelling soil organic matter dynamics on a bare fallow Chernozem soil in Central Germany. Geoderma 303:93–98
Frøseth RB, Bleken MA (2015) Effect of low temperature and soil type on the decomposition rate of soil organic carbon and clover leaves, and related priming effect. Soil Biol Biochem 80:156–166
Gershenson A, Bader NE, Cheng W (2009) Effects of substrate availability on the temperature sensitivity of soil organic matter decomposition. Glob Change Biol 15:176–183
Gong C, Song C, Zhang D, Zhang J (2019) Litter manipulation strongly affects CO₂ emissions and temperature sensitivity in a temperate freshwater marsh of northeastern China. Ecol Ind 97:410–418
Hooker TD, Stark JM, Norton U, Leffler AJ, Peek M, Ryel R (2008) Distribution of ecosystem C and N within contrasting vegetation types in a semiarid rangeland in the Great Basin, USA. Biogeochemistry 90:291–308
Hopkins FM, Torn MS, Trumbore SE (2012) Warming accelerates decomposition of decades-old carbon in forest soils. Proc Natl Acad Sci USA 109:E1753–E1761
Jia J, Cao Z, Liu C, Zhang Z, Lin L, Wang Y, Haghipour N, Bao H, Wacker L, Dittmar T, Simpson MJ, Yang H, Crowther TW, Eglinton TI, He J, Feng X (2019) Climate warming alters subsoil but not topsoil C dynamics. Glob Change Biol 25:4383–4393
Jobbágy EG, Jackson RB (2000) The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol Appl 10:423–436
Kan Z-R, Liu W-X, Liu W-S, He C, Bohoussou NY, Dang YP, Zhao X, Zhang H-L (2022) Sieving soil before incubation experiments overestimates carbon mineralization but underestimates temperature sensitivity. Sci Total Environ 806:150962
Karhu K, Auffret MD, Dungait JAJ, Hopkins DW, Prosser JI, Singh BK, Subke J-A, Wookey PA, Ågren GI, Sebastià M-T, Gouriveau F, Bergkvist G, Meir P, Nottingham AT, Salinas N, Hartley IP (2014) Temperature sensitivity of soil respiration rates enhanced by microbial community response. Nature 513:81–84
Kemmitt SJ, Lanyon CV, Waite IS, Wen Q, Addiscott TM, Bird NRA, O’Donnell AG, Brookes PC (2008) Mineralization of native soil organic matter is not regulated by the size, activity or composition of the soil microbial biomass-a new perspective. Soil Biol Biochem 40:61–73
Kirschbaum MF (2000) Will changes in soil organic carbon act as a positive or negative feedback on global warming? Biogeochemistry 48:21–51
Kleber M (2010) What is recalcitrant soil organic matter? Environ Chem 7:320–332
Knorr W, Prentice IC, House JI, Holland EA (2005) Long-term sensitivity of soil carbon turnover to warming. Nature 433:298–301
Larionova AA, Yevdokimov IV, Bykhovets SS (2007) Temperature response of soil respiration is dependent on concentration of readily decomposable C. Biogeosciences 4:1073–1081
Lefèvre R, Barré P, Moyano FE, Christensen BT, Bardoux G, Eglin T, Girardin C, Houot S, Kätterer T, Van Oort F, Chenu C (2014) Higher temperature sensitivity for stable than for labile soil organic carbon- Evidence from incubations of long-term bare fallow soils. Glob Change Biol 20:633–640
Li X, Xie J, Zhang Q, Lyu M, Xiong X, Liu X, Lin T, Yang Y (2020) Substrate availability and soil microbes drive temperature sensitivity of soil organic carbon mineralization to warming along an elevation gradient in subtropical Asia. Geoderma 364:114198
Liang J, Li D, Shi Z, Tiedje JM, Zhou J, Schuur EAG, Konstantinidis KT, Luo Y (2015) Methods for estimating temperature sensitivity of soil organic matter based on incubation data: A comparative evaluation. Soil Biol Biochem 80:127–135
Lin J, Zhu B, Cheng W (2015) Decadally cycling soil carbon is more sensitive to warming than faster-cycling soil carbon. Glob Change Biol 21:4602–4612
Liu HS, Li LH, Han XG, Huang JH, Sun JX, Wang HY (2006) Respiratory substrate availability plays a crucial role in the response of soil respiration to environmental factors. Appl Soil Ecol 32:284–292
Lützow MV, Kögel-Knabner I (2009) Temperature sensitivity of soil organic matter decomposition—what do we know? Biol Fertil Soils 46:1–15
Nunan N, Lerch TZ, Pouteau V, Mora P, Changey F, Kätterer T, Giusti-Miller S, Herrmann AM (2015) Metabolising old soil carbon: Simply a matter of simple organic matter? Soil Biol Biochem 88:128–136
Paterson E, Sim A, Osborne SM, Murray PJ (2011) Long-term exclusion of plant-inputs to soil reduces the functional capacity of microbial communities to mineralise recalcitrant root-derived carbon sources. Soil Biol Biochem 43:1873–1880
Pold G, Grandy AS, Melillo JM, DeAngelis KM (2017) Changes in substrate availability drive carbon cycle response to chronic warming. Soil Biol Biochem 110:68–78
Reichstein M, Bednorz F, Broll G, Kätterer T (2000) Temperature dependence of carbon mineralisation: conclusions from a long-term incubation of subalpine soil samples. Soil Biol Biochem 32:947–958
Reichstein M, Kätterer T, Andrén O, Ciais P, Schulze E-D, Cramer W, Papale D, Valentini R (2005) Does the temperature sensitivity of decomposition vary with soil organic matter quality? Biogeosciences Discuss 2:737–747
Rey A, Jarvis P (2006) Modelling the effect of temperature on carbon mineralization rates across a network of European forest sites (FORCAST). Glob Change Biol 12:1894–1908
Reynolds LL, Lajtha K, Bowden RD, Johnson BR, Bridgham SD (2017) The carbon quality-temperature hypothesis does not consistently predict temperature sensitivity of soil organic matter mineralization in soils from two manipulative ecosystem experiments. Biogeochemistry 136:249–260
Saffron CM, Park J-H, Dale BE, Voice TC (2006) Kinetics of contaminant desorption from soil: comparison of model formulations using the akaike information criterion. Environ Sci Technol 40:7662–7667
Salomé C, Nunan N, Pouteau V, Lerch TZ, Chenu C (2010) Carbon dynamics in topsoil and in subsoil may be controlled by different regulatory mechanisms. Glob Change Biol 16:416–426
Sanford G, Smith SJ (1972) Nitrogen mineralization potential of soils. Soil Science Society of America Proceedings 36: 465–472
Sierra C (2012) Temperature sensitivity of organic matter decomposition in the Arrhenius equation: some theoretical considerations. Biogeochemistry 108:1–15
Soil Survey Staff (2010) Keys to Soil Taxonomy. eleventh ed. USDA–Natural Resources Conservation Service, Washington, DC, USA
Tang J, Riley WJ (2020) Linear two-pool models are insufficient to infer soil organic matter decomposition temperature sensitivity from incubations. Biogeochemistry 149:251–261
Thiessen S, Gleixner G, Wutzler T, Reichstein M (2013) Both priming and temperature sensitivity of soil organic matter decomposition depend on microbial biomass – An incubation study. Soil Biol Biochem 57:739–748
Thornley JHM, Cannell MGR (2001) Soil carbon storage response to temperature: an hypothesis. Ann Botany 87:591–598
Trumbore SE, Czimczik CI (2008) An uncertain future for soil carbon. Science 321:1455–1456
Vaughn LJS, Torn MS (2019) ¹⁴C evidence that millennial and fast-cycling soil carbon are equally sensitive to warming. Nat Clim Change 9:467–471
Wagai R, Kishimoto-Mo AW, Yonemura S, Shirato Y, Hiradate S, Yagasaki Y (2013) Linking temperature sensitivity of soil organic matter decomposition to its molecular structure, accessibility, and microbial physiology. Glob Change Biol 19:1114–1125
Weihermüller L, Neuser A, Herbst M, Vereecken H (2018) Problems associated to kinetic fitting of incubation data. Soil Biol Biochem 120:260–271
Zang H, Blagodatskaya E, Wen Y, Shi L, Cheng F, Chen H, Zhao B, Zhang F, Fan M, Kuzyakov Y (2020) Temperature sensitivity of soil organic matter mineralization decreases with long-term N fertilization: Evidence from four Q₁₀ estimation approaches. Land Degrad Dev 31:683–693
Zhang X, Han X, Yu W, Wang P, Cheng W (2017) Priming effects on labile and stable soil organic carbon decomposition: Pulse dynamics over two years. PLoS ONE 12:e0184978
Zhang X, Yu F (2020) Physical disturbance accelerates carbon loss through increasing labile carbon release. Plant Soil and Environment 66:584–589
Zhang X, Zhu B, Yu F, Cheng W (2021) Plant inputs mediate the linkage between soil carbon and net nitrogen mineralization. Sci Total Environ 790:148208
Zhu B, Cheng W (2011) Rhizosphere priming effect increases the temperature sensitivity of soil organic matter decomposition. Glob Change Biol 17:2172–2183

PDF

Reviews received at journal
19 Apr, 2022
Reviewers invited by journal
19 Apr, 2022
Editor invited by journal
13 Apr, 2022
Editor assigned by journal
13 Apr, 2022
First submitted to journal
09 Apr, 2022

You are reading this latest preprint version

Exclusion of plant input affects the temperature sensitivity of soil carbon decomposition

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Soil collection

2.2. Soil incubation

2.3. CO₂ trapping

2.4. Calculations and statistical analysis

3. Results

3.1. Soil respiration

3.2. Temperature sensitivity of SOC decomposition calculated based on four methods

4. Discussion

4.1. Temperature sensitivity of SOC decomposition—comparison of four methods

4.2. Temperature sensitivity of SOC after long-term exclusion of plant input

5. Conclusions

Abbreviations

Declarations

Acknowledgements

References

Status:

Version 1

Exclusion of plant input affects the temperature sensitivity of soil carbon decomposition

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Soil collection

2.2. Soil incubation

2.3. CO2 trapping

2.4. Calculations and statistical analysis

3. Results

3.1. Soil respiration

3.2. Temperature sensitivity of SOC decomposition calculated based on four methods

4. Discussion

4.1. Temperature sensitivity of SOC decomposition—comparison of four methods

4.2. Temperature sensitivity of SOC after long-term exclusion of plant input

5. Conclusions

Abbreviations

Declarations

Acknowledgements

References

Status:

Version 1

2.3. CO₂ trapping