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Published online 23 May 2006
Published in Soil Sci Soc Am J 70:1121-1128 (2006)
DOI: 10.2136/sssaj2005.0133
© 2006 Soil Science Society of America
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Soil Biology & Biochemistry

Prediction of Gross and Net Nitrogen Mineralization-Immobilization-Turnover from Respiration

Jesper Luxhøia,*, Sander Bruuna, Bo Stenbergb, Tor A. Brelandc and Lars S. Jensena

a Royal Veterinary and Agricultural Univ., Plant and Soil Sci. Lab., Thorvaldsensvej 40, 3. sal, 1871 Frederiksberg C, Denmark
b Division of Precision Agriculture, Swedish University of Agricultural Sciences, P.O. Box 234, SE-532 23 Skara, Sweden
c Department of Horticulture and Crop Science, Agricultural University of Norway, P.O. Box 5022, N-1432 Aas, Norway

* Corresponding author (jelu{at}kvl.dk)


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
Prediction of net N mineralization is required for optimization of the synchronization of N supply with plant N demand. Net N mineralization is the outcome of two concurrent and oppositely directed processes: gross N mineralization and gross N immobilization turnover (MIT). Consequently, an improved understanding of MIT can potentially improve our capability to predict net N mineralization patterns. The aim of the study was to measure MIT and respiration rates of widely differing plant materials and look for relations between them. Eight plant residues with a very wide range in C to N ratios were incorporated into soil and incubated at 16°C. During 2-d intervals (5–6, 25–26, and 55–56 d after incorporation), MIT and respiration rates were determined. The respiration and gross N immobilization rates were correlated (R2 = 0.74), whereas respiration and gross N mineralization rates were less well correlated (R2 = 0.41). The correlation was improved (R2 = 0.89) when only the data from the first incubation period and the C/N ratio of acid detergent solubles (ADS) were taken into account. Assuming that the soil microorganisms have a C use efficiency of 50%, this study showed that the gross N mineralization rate made up only 30% of the total gross litter N decomposition rate (i.e., the remaining 70% being directly assimilated by soil microorganisms in organic form). Net N mineralization rates, derived from the difference between predicted rates of gross N mineralization and gross N immobilization, could explain up to 64% of the variability in measured net N mineralization rates. In conclusion, this study revealed that MIT in the initial phase of decomposition can be derived from data on C mineralization and the C/N ratio of ADS, which can simplify the process of calibrating and validating mechanistic models and thereby improve our capability of predicting net N mineralization.

Abbreviations: ADS, acid detergent solubles • DW, dry weight • MIT, N immobilization turnover • NDS, neutral detergent-solubles • WS, hot-water solubles


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
RETURN of organic materials, such as crop residues, catch crops, and green manures, has a marked influence on carbon (C) and nitrogen (N) turnover in agricultural soils. The synchronization of N supply with plant demand is important for environmental and agronomic reasons (Kumar and Goh, 2000). Consequently, a good knowledge of the C and N dynamics in soil and underlying causes is required to improve the synchronization of N supply with demand.

Accumulated microbial respiration from decomposing plant residues usually follows a hyperbolic pattern. Therefore, Bruun et al. (2005) could predict 75% of the variability in respiration from a very wide range of plant residues by using the average respiration at each sampling occasion of all the 78 residues. In contrast, the temporal dynamics for N are much more diverse, forming variously shaped patterns ranging from net N mineralization to net N immobilization (Trinsoutrot et al., 2000; Jensen et al., 2005). The reason is that net N mineralization is the outcome of two concurrent and oppositely directed processes: gross N mineralization and gross N immobilization turnover (MIT). Consequently, net N mineralization is more difficult to predict than the corresponding respiration. Bruun et al. (2005) were inherently able to predict only 5% of the variability in net N mineralization by the average net N mineralization of the plant residues. Whether net N mineralization or net N immobilization occurs is closely linked with C availability and has been related to C/N ratio of the decomposing organic matter (Paul and Juma, 1981; van Veen et al., 1984). Hence, when the decomposability and C/N ratio of organic amendments (plant residues and animal manures) were included in a mechanistic model, Petersen et al. (2005) obtained descriptions of measured net N mineralization varying from good to acceptable. Consequently, an improved understanding of MIT could potentially further improve our capability to predict the net N mineralization patterns. For example, quantitative data on MIT and their correlation to more easily measured parameters would be useful for calibrating and validating the rates of mechanistic models taking these processes into account.

Gross N immobilization has been found to be correlated to respiration (Hart et al., 1994; Recous et al., 1999; Barrett and Burke, 2000; Bengtsson et al., 2003) because the microbial biomass needs N and energy for growth. Gross N mineralization has also been found to be linked to the respiration in soils (e.g., Hart et al., 1994; Bengtsson et al., 2003). However, Murphy et al. (2003) have suggested that the relationship between gross mineralized N and respiration largely depends on the C/N ratio of the decomposing pool and the microbial C use efficiency (E), as shown in Eq. [1]:

Formula 1[1]
where GM is the gross N mineralization rate, R is the respiration rate, E is the C use efficiency, and Z is the C/N ratio of the degrading pool (Table 1). For soils with a very wide range of C/N ratios, a high correlation between gross N mineralization and respiration is not to be expected. In fact, Booth et al. (2005) analyzed gross N mineralization data from the literature, using log-transformed regression analyses. Booth et al. found a poor relationship (R2 = 0.25) between gross N mineralization and respiration, which partly could be due to the variable soil C/N ratios, which were characteristic for this very diverse data set. Therefore, it is of interest to investigate whether taking C/N ratio into account as described in Eq. [1] (see Murphy et al., 2003) would improve the relationship.


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  		Table 1. Notations, definitions, and units of C and N rates, variables, and parameters used in the equations.

 
The aim of the present study was to verify, for soil incorporated with a very wide range of plant litter qualities, whether (i) gross N immobilization rates could be predicted from the corresponding respiration rates, (ii) gross N mineralization rates could be predicted according to Eq. [1] from the corresponding respiration rates and plant litter C/N ratios, and (iii) net N mineralization rates could be predicted from the difference between predicted rates of gross N mineralization and gross N immobilization.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 REFERENCES
 
Soil and Plant Residue Preparation
The soil used in the experiment was collected from the plow layer (0–20 cm) of an experimental field in Taastrup, Denmark, that used for cereal production. The soil was a sandy loam, with 1.33% total C, 0.18% total N, and pH 7.0 (0.01 CaCl2). No organic fertilizer had been applied within the last 15 yr. The soil was sieved through a 5-mm mesh, adjusted to a water content of 17% (w/w) (equivalent to about 43% of the soil's water holding capacity), and preincubated for 3 wk at 16°C. Eight plant residues from a large archive originating from an associated project (Stenberg et al., 2004) were selected to cover a very wide range of C/N ratios and contents of soluble C (Table 2). The residues were chopped in pieces ≤5 mm in length. They were then homogeneously incorporated into portions of 1 kg (on a dry weight [DW] basis) of the preincubated soil (2% [w/w] on DW basis) in 5-L containers. As described by Jensen et al. (2005), for the soil with incorporation of high C/N-ratio residues (hemp straw, maize plants, and wheat stems), the soil mineral N plus residue N content was adjusted to 33.5 mg N g–1 added C by addition of KNO3 to avoid N-limited decomposition (Recous et al., 1995; Henriksen and Breland, 1999b). Hence, the KNO3 addition was done as follows: hemp straw: 25 mg N g–1 C; maize plants and wheat straw: 17 mg N g–1 C; maize stems, flax plants, and melilot plants: 2 mg N g–1 C. A control soil without residue amendment was also incubated. The control soil and the residue-amended soils with soil mineral N plus residue N contents higher than 33.5 mg N g–1 added C (oil radish leaves and rye leaves) did not receive any KNO3. From each of the nine containers, 12 replicates of 50 g of soil (DW basis) were weighed into incubation vessels, compacted to a volume weight of 1.2 Mg m–3, and incubated at 16°C, which is a common soil temperature in Denmark during the summer.


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  		Table 2. C/N ratios C and N contents in eight plant residues used in the experiment.{dagger}

 
Gross and Net Nitrogen Mineralization-Immobilization
Gross MIT was determined during 2-d intervals: 5–6, 25–26, and 55–56, using a spraying-mixing technique (Luxhøi et al., 2005). For each treatment, triplicate vessels were destructively sampled. The 50 g of soil (DW basis) from each vessel was sprayed with an (NH4)2SO4– solution (10 mg NH4+–N kg–1 soil, with 10 atom% 15N excess) and thoroughly mixed. On average, this made up approximately two thirds of the total NH4+ pool; however, for the rye leaves and oil radish leaves treatments, this only made approximately one tenth of the total NH4+ pool. The soil was divided into two 20-g (DW basis) portions and immediately transferred back into the incubator. After 2 h, one of the 20-g soil portions (t0) was extracted in 80 mL of 1 M KCl for 45 min in an end-over-end shaker and filtered through Advantec filter paper (no. 5C). The other 20-g soil portion was extracted after an additional 48-h incubation (t1). Luxhøi and Jensen (2005) recently showed that soil disturbance did not affect MIT in a similar textured soil. However, they found that soil disturbance increased nitrification. Therefore, this short incubation period (2 d) was chosen to avoid exhaustion of the NH4+ pool, which would compromise the precision of the pool dilution technique (Wessel and Tietema, 1992; Murphy et al., 2003). The pool sizes and 15N abundances of NH4+-N and NO3–-N were determined using a diffusion technique (Brooks et al., 1989) slightly modified according to Sparling et al. (1996). 15N abundances were measured on a mass spectrometer (type 20-20, coupled to an ANCA-SL sample preparation module, both Europa Scientific, Crewe, UK). The soil remaining on the filters and in the extraction bottles was shaken in 40 mL of 1 M KCl for 15 min and centrifuged at 2500 x g (Recous et al., 1999). The supernatant was discarded, and the procedure was repeated four times, which in a preliminary test had been found to remove any extractable mineral N. Remaining organic-N and organic-15N abundance in the soil was measured on the mass-spectrometer as described previously. Gross MIT was calculated according to the principle of isotopic dilution/enrichment (Kirkham and Bartholomew, 1954) using FLUAZ model, version 6 (Mary et al., 1998). The average 15N recovery between t0 and t1 was 85%, indicating that some 15N was lost (e.g., as soluble organic-N, which was not accounted for in our procedure). MIT derived from residue decomposition were calculated as the difference in rates between treatments and control. Net N mineralization rate was derived from the difference between gross rates of mineralization and immobilization.

Microbial Respiration
Triplicate incubation vessels (50 g soil on DW basis) were placed in separate air-tight 1-L glass jars with 20 mL of water and a vial with 0.1 M NaOH solution. The NaOH vials were removed regularly (on Day 3, 7, 11, 18, 26, 34, 43, 53, and 75) during the incubation for determination of CO2 absorption, and replaced with fresh NaOH. Carbon dioxide absorption was determined by titration with HCl and BaCl2, and phenolphthalein was added (Anderson and Ingram, 1993). Microbial respiration derived from residue decomposition was calculated as the difference in CO2 absorption between treatments and control. Because respiration rates were determined regularly at Days 3, 7, 11, 18, 26, 34, 43, 53, and 75 while MIT were determined at time interval 5–6, 25–26, 55–56, respiration rate and MIT were not directly comparable. To obtain respiration rate estimates for the time interval Day 5–6, 25–26, 55–56, a two-pool exponential model (Paul et al., 1999) was fitted to the accumulated CO2 production (Eq. [2]).

Formula 2[2]
where t is the time, C1 and C2 are C fractions of the plant material with corresponding first-order decay rates of k1 and k2, respectively. The respiration rates during the 2-d intervals (5–6, 25–26, and 55–56) were estimated from the slope of the fit at the time intervals (5–6, 25–26, and 55–56).

Chemical Characterization of Plant Residues
The chemical characterization of plant residues was obtained from an associated project (Stenberg et al., 2004). Briefly, stepwise chemical digestion of the plant residues (Goering and van Soest, 1970) was used for determination of neutral detergent-solubles (NDS), acid detergent-solubles (ADS), hemicellulose, cellulose, and lignin. Hot-water solubles (WS) was determined according to TAPPI (1978). Both procedures were slightly modified according to Henriksen and Breland (1999a) to allow determination of C and N in the fractions.

Linear Regression Analysis
Reduced major axis regressions (Mesplé et al., 1996) were used for regressions between respiration rates and gross MIT across all incubation periods, according to Eq. [3] and [4]:

Formula 3[3]

Formula 4[4]
where, GI is the gross N immobilization rate, and a and b are constants representing the regression slopes. Gross N mineralization rates were regressed against the respiration rate divided by the C/N ratio of the plant materials across all incubation periods and for each individual incubation period, according to Eq. [5]:

Formula 5[5]
where c is a constant representing the regression slope. In addition to being applied to the C/N ratio of the total plant material, Eq. 5 was also applied for the C/N ratio of WS, NDS, and ADS. Predicted net N mineralization rate was derived as the difference between the right sides of Eq. [3] and [5] yielding Eq. [6]:

Formula 6[6]
where NMP is the predicted net N mineralization rate. The predicative power of this equation was evaluated by regression against measured net N mineralization according to Eq. [7]:

Formula 7[7]
where NMM is the measured net N mineralization, and d is a constant, representing the regression slope. The C use efficiency can be calculated from Eq. [8], which is derived from Eq. [1] and [5]:

Formula 8[8]

By the so-called "direct route," microorganisms can immobilize N in small organic compounds without the prior release of NH4+ (Barraclough, 1997; Hadas et al., 1992), which would not be detected by the pool dilution method. Saetre and Stark (2005) have suggested that immobilization of organic N can be taken into account by modifying Eq. [1]GoGoGoGoGoGoGo to [9]:

Formula 9[9]
where Iorg is the immobilization of organic N. Equation [9] can be reformulated into Eq. [10], which represents the ratio between gross N mineralization and gross N decomposition:

Formula 10[10]

Statistical Analysis
The data shown are the mean values of three replicates, and error terms represent standard error. Respiration rates were subjected to ANOVA in the GLM procedure using SAS software (SAS software, 1999–2001). The Fluaz model (Mary et al., 1998) was used to calculate MIT. Apart from rate estimates, the Fluaz model also calculates weighted 95% confidence interval of rate estimates, using the experimental variability of the measured parameters. As suggested by Luxhøi and Brockhoff (2004), the MIT data was subjected to weighted ANOVA in the GLM procedure for Least Squares of Means in SAS (SAS software, 1999–2001).


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
REFERENCES
 
For all the eight plant residues, gross N immobilization and respiration rates declined significantly (p < 0.0001) during the experiment (Table 3), indicating a reduction of the easily decomposable compounds in the initial phase of decomposition (Reinertsen et al., 1984; Marstop, 1996; Gibbs and Barraclough, 1998). There was no significant difference between plant residues in gross N immobilization and respiration rates (p = 0.06 and 0.07). Hence, respiration and gross N immobilization rates behaved similarly. Consequently, these rates were linearly related (R2 = 0.74) (Fig. 1a ), as found by other researchers (Hart et al., 1994; Recous et al., 1999; Barrett and Burke, 2000; Bengtsson et al., 2003). For gross N mineralization, a significant (p < 0.05) decline during the experiment was also observed. However, this was mainly caused by the two N-rich plant residues (rye leaves and oil radish leaves), whereas the patterns of plant residues with lower N contents were temporally much less dynamic (Table 3), as observed by Watkins and Barraclough (1996). This different behavior between plant residues was highly significant (p < 0.001). Thus, gross N mineralization and respiration rates behaved differently, with the consequence of low correlation between these rates (R2 = 0.41) (Fig. 1b). Whether net N mineralization or net N immobilization occurs is closely related to the C/N ratio of the decomposing organic matter. Hence, in accordance with Trinsoutrot et al. (2000) and Jensen et al. (2005), net N mineralization rates were not correlated to the respiration rate (Fig. 1c).


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  		Table 3. Rates of gross N mineralization, gross N immobilization, and respiration, presented as the difference between treatments and control. Values in brackets show standard error (n = 3).

 

Figure 1
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  		Fig. 1. (a) Gross N immobilization rate (GI) vs. respiration rate (R). (b) Gross N mineralization rate (GM) vs. respiration rate (R). (c) Net N mineralization rate (NMD) vs. respiration rate (R). (d) gross N mineralization rate vs. respiration rate (R) divided by the C to N ratio (Z) of the plant materials. The regressions are based on data from all three incubation periods. Error bars show standard error (n = 3).

 
The proportion of gross mineralized N to respiration depends largely on the C/N ratio of the decomposing pool and the microbial C use efficiency (see Eq. [1]) (Murphy et al., 2003; Saetre and Stark, 2005). Accordingly, the regression was improved (R2 = 0.65) when gross N mineralization rates were regressed on rates estimated from the respiration rates divided by the C/N ratios of the decomposing plant residues (Fig. 1d). The improved correlation was mainly caused by data from measurements at Day 5 to 6. Data from measurements at Day 25 to 26, and 55 to 56 were clustered in the lower left-hand corner of Fig. 1d and thereby did not contribute much to the slope of the regression line. This is likely because the regression includes different sources of variation. One source of variation is the effect of plant material with different qualities. Another source of variation is the incubation time, which may change the quality of the plant materials over time. A third source of variation may raise from N that is immobilized in the initial phase of decomposition and is being re-mineralized in the later stages of decomposition (Sørensen and Amato, 2002) due to decay of the microbial biomass. Hence, as shown in Fig. 2a , the regression between GM and R/Z was markedly improved (R = 0.82) when only data from the first incubation period were included in the regression.


Figure 2
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  		Fig. 2. Gross N mineralization rate (GM) vs. respiration rate (R) divided by the C/N ratio (Z) of various plant residue constituents. (a) C/N ratio of total plant residue. (b) C/N ratio of water solubles (WS). (c) C/N ratio of neutral detergent solubles (NDS) and C/N ratio of acid detergent solubles (ADS). The regressions are based on data from the incubation period Day 5 to 6. Error bars show standard error (n = 3).

 
Decomposition of the more resistant parts of the plant residues (e.g., lignin and cellulose) may not contribute much to the overall decomposition, at least not in the initial phase of decomposition (Swift et al., 1979; Paul et al., 1999; Jensen et al., 2005). Hence, the predictive power can possibly be increased if the prediction is based on the C/N ratio of the more easy decomposable plant constituents. From an associate project (Stenberg et al., 2004), estimates of contents and C/N ratios of WS, NDS, and ADS in the plant residues are available where the degradability largely is in the order WS > NDS > ADS (Swift et al., 1979). Figure 2 shows that prediction based on WS and NDS was slightly lower (R2 = 0.56 and 0.76) than prediction based on total plant litter, whereas prediction based on ADS was slightly improved (R2 = 0.89). For the later incubation periods, the regression coefficients were markedly lower (R2 varying between 0.1 and 0.4; regressions not shown).

From Eq. [8] it is calculated that the C use efficiency calculated from regression with ADS for incubation period Day 5 to 6 is –0.76. When calculated from regression based on total plant material, WS, or NDS, the C use efficiency also become negative. This is obviously not possible and must be an artifact. Saetre and Stark (2005) estimated C use efficiency in two soil systems to vary between 0.3 and 0.7, which is in the same magnitude as reported elsewhere (Murphy et al., 2003). Assuming an average use efficiency in our study of 0.5 would, according to Eq. [10], result in a ratio between gross N mineralization and gross N decomposition of 0.3, meaning that 70% of the decomposed litter N was directly assimilated by the soil microbes without prior release into to the NH4+ pool. Similar magnitude of immobilization of organic N has been reported by Saetre and Stark (2005).

Net N mineralization rates and predicted net mineralization rates for incubation period Day 5–6 were poorly related for total plant material (R2 = 0.38), WS (R2 = 0.14), and NDS (R2 = 0.30), whereas an R2 value of 0.64 was obtained for ADS (Fig. 3 ). Higher coefficients of determination (R2 values) would have been desirable. However, net N mineralization rates were derived from the difference between rates of gross N mineralization and immobilization, whereby the variability of the gross rates were transferred onto the net rate. Because the net rates were smaller then the corresponding gross rates, the relative variability of the net rate became markedly higher than the relative variability of the gross rates. Nevertheless, the correlation has markedly increased compared with the correlation between net N mineralization rates and respiration (Fig. 1c). Furthermore, variation among treatments in microbial C use efficiency (Müller et al., 2003; Saetre and Stark, 2005), microbial assimilation of organic N (Hadas et al., 1992; Barraclough, 1997; Saetre and Stark, 2005), and the interaction between residue and soil dynamics (Bingeman et al., 1953; Jenkinson et al., 1985) would also influence the relation between respiration and gross N mineralization. Likewise, the fact that we applied KNO3 solution to the soil with incorporation of high C/N ratio residues to avoid N limitation could influence variability between treatments (Fog, 1988; Schimel and Weintraub, 2003).


Figure 3
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  		Fig. 3. Determined net N mineralization rate (NMD) vs. predicted net N mineralization rate (NMP). Predicted net N mineralization was dependent on (a) C/N ratio of total plant litter, (b) C/N ratio of water solubles (WS), C/N ratio of neutral detergent solubles (NDS), or C/N ratio of acid detergent solubles (ADS). The regressions are based on data from the incubation period Day 5 to 6. Error bars show standard error (n = 3).

 
In conclusion, the study demonstrated that gross N immobilization rates were correlated with the corresponding respiration rates. In contrast, gross N mineralization rates were less well correlated to the corresponding respiration rates. The correlation was improved when data from the incubation period Day 5 to 6 and the C/N ratio of acid detergent solubles were taken into account. Under the assumption that the soil microorganisms have a C use efficiency of 0.5, the study showed that the gross N mineralization rate only made up 30% of the total litter N decomposition rate; the remaining 70% was directly assimilated by soil microorganisms in organic form. Predicted net N mineralization rates, derived from the difference between predicted rates of gross N mineralization and immobilization, could predict up to 64% of the variability in the measured net N mineralization. The results suggests that in the initial phase of decomposition, it is possible to derive gross N immobilization and gross N mineralization, and consequently also net N mineralization, from data on respiration and C/N ratio. However, at the later stage of decomposition, the relationship between gross N mineralization and respiration is not valid, probably because the dynamics of the soil microbial biomass is not taken into account. If the quantitative relationships found here (e.g., Fig. 1a and Fig. 2d) are relatively stable across soil types as suggested by Booth et al. (2005), these relationships can probably greatly simplify the process of calibrating and validating of subroutines describing these processes in mechanistic models.

Received for publication April 27, 2005.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 




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