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Evaluating soil microbial biomass carbon as an indicator of long-term environmental change
Soil Biology & Biochemistry 35 (2003) 401–407 www.elsevier.com/locate/soilbio
Evaluating soil microbial biomass carbon as an indicator of long-term environmental change P.R. Hargreaves*, P.C. Brookes, G.J.S. Ross, P.R. Poulton Agriculture and the Environment Division, Rothamsted Research Ltd, Harpenden, Herts AL5 2JQ, UK Received 20 February 2002; received in revised form 16 October 2002; accepted 8 November 2002
Abstract The aim is to assess whether soil microbial biomass carbon (biomass C) could be used as an indicator of environmental change in natural and semi-natural ecosystems. Biomass C was measured by fumigation– extraction in soils from two sites at Rothamsted. One was a plot from the Broadbalk Wheat Experiment, given inorganic fertiliser and chalk, which has been in continuous cultivation for more than 150 yr. The other was a similar sized area, from Geescroft Wilderness, which has been left to revert to woodland since 1885, after being an arable field. Other soil properties (pH, soil organic C and exchangeable cations) were also measured to compare with biomass C. The coefficients of variation (cvs) of the properties measured were calculated for comparison, little difference was found between the cvs for biomass C from each site: cv ¼ 26% for Broadbalk and 23% for Geescroft. The cvs for the other, chemical properties, were mostly ,10% for Broadbalk and generally . 25% for Geescroft, as expected, given the different cultivation histories. Statistical analysis of the variation in biomass C concentration revealed that such measurements would not be valid indicators of environmental change, without processing impossibly large numbers of samples. To decrease the least significant percentage change to less than 5% after three samplings, 320 samples would have to be taken each time. This would be also be true of the other chemical properties in Geescroft Wilderness, where the measured background variation would mask any subtle environmental change. This indicates that, for some properties at least, statistically significant changes will only be detected in the longer term with regular sampling, e.g. 30 – 40 yr. q 2003 Elsevier Science Ltd. All rights reserved. Keywords: Environmental change; Soil microbial biomass; Carbon; Soil chemical properties
1. Introduction The Environmental Change Network (ECN) was launched in January 1992 as a network of sites throughout the UK to monitor environmental change, specifically brought about by climate or atmospheric pollutants (Tinker, 1993; Burt, 1994). Methods have been established to monitor the many potential inputs and effects of environmental change (Sykes and Lane, 1996). These methods aim to follow a holistic approach with the integrated monitoring of insects, animals, vegetation and chemical properties. Changes in the chemistry of the soils at the terrestrial sites are currently assessed by soil sampling every 5 yr. The measurement of soil microbial biomass C was one method of providing a more sensitive indicator of change than soil chemistry alone * Corresponding author. Tel.: þ 44-1582-763133; fax: þ 44-1582760981. E-mail address: [email protected] (P.R. Hargreaves).
and addressing the gap in monitoring, between the soil chemistry and the vegetation cover. An initial comparison of one of the more commonly used microbial biomass methods (fumigation – extraction) was done at Rothamsted Research, one of the ECN terrestrial sites. Our aim is to assess whether microbial biomass C could possibly serve as an ‘environmental indicator’ or whether inherent variation, found in a regular soil sampling method, would mask any effects of environmental change. Biomass C was considered as a possible measure in this context because it is well established as an early indicator of gross changes in C input caused by pollution (Brookes and McGrath, 1984) or crop residue incorporation (e.g. Jenkinson and Powlson, 1976; Powlson et al., 1987). As we expected the variability of soil microbial biomass to be large in a natural or semi-natural ecosystem, a deliberate comparison was made with a homogenous, well mixed, fertilised arable soil that was presumed to be less variable for the measured soil properties.
0038-0717/03/$ - see front matter q 2003 Elsevier Science Ltd. All rights reserved. doi:10.1010/S0038-0717(02)00291-2
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The main objective is to assess the natural variation in soil biomass C concentration and compare this with the variability of other soil properties in the two soils. The use of statistical predictions, based on the variation of the soil properties, could then determine the number of samples required to detect statistically significant change, due to effects of environmental change, on the soil microbial biomass C concentration in the two soils.
2. Materials and methods 2.1. Sites and soil sampling The first site was a homogenous soil taken from an arable plot, given inorganic fertiliser (N at 96 kg ha21, P at 35 kg ha21, K at 90 kg ha21 and Mg at 30 kg ha21 annually for more than 150 yr) and chalk, on the Broadbalk Wheat Experiment. This is the longest continuously running agronomic experiment in the world, started in 1843 (Bawden, 1969; Johnston, 1993). The second site was Geescroft Wilderness, a site of natural regeneration with a highly acidic soil (pH 4.2). This was initially part of an arable field fenced off in 1885 and allowed to regenerate to mixed deciduous woodland. No chalk had been added to the site since it was fenced off (Johnston et al., 1986; Goulding and Johnston, 1989). The soil on both sites is a flinty silty clay loam (24% clay) of the Batcombe series (Chromic Luvisol (FAO); Avery and Catt, 1995). A selected area (28 £ 6 m2) within Broadbalk plot No. 141, pH 7.2, was sampled (Johnston, 1969). To avoid any edge effect, a 1.5 m strip down each side and a 1 m strip at either end of the plot were avoided. Eighteen sampling points were used at regular intervals along four transects running the length of the plot, these transects were 1 m apart. Transects 1 and 3 had five sampling points and transects 2 and 4 had four sampling points. An area of the same size was marked out in the Geescroft Wilderness and 18 samples taken in the same way (Fig. 1). At each of the 18 sampling points, four cores were taken with a 2 cm dia auger; the four cores were then bulked to form 18 samples. The cores were taken from the 0 –23 cm depth on Broadbalk (plough depth) and the 0 –12.5 cm depth on Geescroft. These depths were assumed to provide the most representative soil depths to quantify the soil microbial biomass.
Fig. 1. The pattern of sampling used for Broadbalk and Geescroff.
2.2. Soil analysis Half of each sample was air-dried, sieved , 2 mm and analysed for pH (1:2 soil-to-H2O ratio), cations exchangeable to 0.5 M ammonium acetate (Metson, 1956) and organic C using an automated combustion method after an acid treatment to eliminate calcium carbonate (Allison, 1965). The other half of each sample was carefully dried to 40% of water holding capacity, ensuring that no part of the sample became air-dry, then sieved , 2 mm. Biomass C was then measured by fumigation –extraction. Briefly, the moist sub-samples (45 g dry weight) were then fumigated for 24 h with alcohol-free CHCl3 and extracted with 0.5 M K2SO4. A second non-fumigated set of sub-samples were extracted during the 24 h fumigation (Vance et al., 1987; Wu et al., 1990). The filtered soil extracts (Whatman No. 45) were frozen at 2 15 8C prior to analysis for organic C by automated UVpersulphate analysis (Wu et al., 1990). Biomass C (Bc) was calculated from: Bc ¼ 2.22 Ec, where Ec ¼ [(C extracted from fumigated soil minus C extracted from non-fumigated soil)]. All results are expressed on an oven-dry soil basis (105 8C, 24 h) and are the mean of three replicate analyses. 2.3. Statistical analysis Variogram analysis was used to determine whether relationships existed between samples taken close together and those samples taken at a distance from each other on each of the plots. The coefficients of variation (cvs) for the various properties measured, to assess any statistical relationships, were also calculated. It was assumed that if samples are taken at different locations within a site, two sources of variability are involved: field (or site) variation when the samples are taken and analytical error. In the simplest of models these deviations are considered independent and normally distributed about a mean value (unless the distribution is clearly skewed with a few very large values and many smaller values). The observed value of the jth laboratory determination of the ith sample is given by the formula: yij ¼ m þ di þ 1ij where m is the mean value, di is the location deviation with cv cL expressed as a percentage, and therefore standard deviation mcL/100 and 1ij is the laboratory deviation with a standard deviation micR/100. If the coefficient of variation between locations is cL and the coefficient of variation between replicate laboratory determinations is cR then the formula for the coefficient of variation of the mean of n samples, each replicated r times, is: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi cm ¼ ðc2L =n þ c2R =rnÞ
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The significance of a percentage difference D between two successive mean determinations may be tested by comparing the ratio: pffiffi D=cm 2 with critical values of the t-distribution (with degrees of freedom depending on the number of samples used to determine cL). With little loss of accuracy, the normal distribution may be used instead. At the 5% level of significance the value of t may be taken as 1.96, so that the least significant difference (LSD) in percentage terms is computed as: pffiffi %LSD ¼ 1:96cm 2
3. Results 3.1. Variability of biomass C As expected, the biomass C concentrations and other measured properties in the Broadbalk and Geescroft soils varied considerably, both within and between soils (Tables 1 and 2). The Broadbalk site was presumed to be a much more homogenous soil than Geescroft because of many years of annual ploughing and fertiliser inputs. This was evident from the much smaller (cv) for exchangeable K and soil organic C (, 10% for the Broadbalk soil, while those of Geescroft were . 25%). The higher cv of biomass C compared to other soil properties from Broadbalk was expected as this is a biological variable, which is affected,
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for example, by the distribution of organic material such as root and stubble within the soil both laterally and with depth. The mean biomass C concentration in Broadbalk of 263 mg C g21 soil was very similar to previous measurements (Patra et al., 1990). However, this mean figure did encompass considerable variation, ranging from 134 to 350 mg C g21 soil. The distribution of biomass C within the Broadbalk plot was uniform with few outlying points; this was also generally true in the Geescroft plot. Here, although the measured amounts of biomass C were twice as large as Broadbalk, the samples were also evenly distributed except for fewer samples at lower biomass C concentrations (less than 400 mg C g21 soil). The results of variogram analysis revealed no relationship between the distance between samples taken and the analysis of these samples. The lack of a relationship indicated that the spatial variability was random. Even if a close correlation between neighbouring observations was found in a given year, this is not relevant to the problem of detecting significant change over time, since different sample locations will be necessary on later occasions, to minimise effects of soil disturbance. 3.2. Variability of soil properties There were few significant correlations between soil properties, including relationships between biomass C and soil organic C, for either Broadbalk or Geescroft (data not shown). Of the few that occurred, the most important were the positive correlations between pH and K, Ca and Mg, and
Table 1 Analyses of Broadbalk soil (Plot 141, NPK) Sample no.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Mean CV %
Organic C (%)
1.08 1.08 1.05 1.12 1.05 1.11 1.10 1.12 1.17 1.21 1.23 1.15 1.15 1.15 1.19 1.04 1.08 1.08 1.1 5.0
n.a., not applicable.
pH (in H20)
7.91 7.88 7.90 7.94 7.90 7.91 7.97 7.91 7.83 7.94 7.93 7.97 7.94 7.83 7.94 7.89 8.04 7.98 7.90 0.6
Biomass
Exchangeable cations (mg g21 soil)
C
K
Ca
Mg
Al
Mn
315 279 317 171 303 180 246 350 321 205 341 337 228 221 294 134 186 300 263 25.6
195 190 180 227 231 193 188 196 166 200 172 203 204 223 187 180 182 182 194 9.3
4360 4270 4140 4290 4410 4640 4470 4350 4280 4580 4600 4560 4550 4430 4650 4510 4410 4750 4457 3.6
145 148 146 158 162 140 138 138 150 136 125 133 142 149 137 128 138 140 142 6.6
,2.26 ,2.26 4.13 ,2.26 ,2.26 ,2.26 ,2.26 ,2.26 ,2.26 ,2.26 65.71 ,2.26 ,2.26 ,2.26 ,2.26 ,2.26 ,2.26 ,2.26 n.a. n.a.
,0.05 ,0.05 2.28 ,0.05 ,0.05 ,0.05 0.21 ,0.05 ,0.05 ,0.05 0.74 ,0.05 ,0.05 ,0.05 ,0.05 ,0.05 ,0.05 ,0.05 n.a. n.a.
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Table 2 Analyses of Geescroft Wilderness soil Sample no.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Mean CV %
Organic C (%)
3.76 5.64 4.21 3.35 3.90 5.08 3.45 3.19 4.45 4.74 3.10 3.81 3.25 6.56 8.14 3.60 3.89 3.35 4.30 31.0
pH (in H2O)
3.88 4.18 4.36 3.97 4.36 3.96 4.42 4.28 4.25 4.01 4.13 4.35 4.50 3.73 3.70 4.40 4.27 4.50 4.18 6.0
Biomass C
592 711 740 468 727 616 656 551 645 483 320 622 506 724 451 519 315 530 565 22.8
Exchangeable cations (mg g21 soil) K
Ca
Mg
Al
Mn
250 200 132 182 124 190 116 141 159 243 179 130 120 208 188 103 161 91 162 28.5
500 950 1010 530 930 510 1240 700 790 570 430 870 930 530 730 840 620 1120 767 30.7
76 88 114 71 112 68 126 86 91 77 63 90 107 74 74 89 83 121 89 21.3
437 326 264 438 394 392 369 448 410 468 365 360 377 444 377 352 396 371 388 12.7
90 97 86 84 81 81 105 99 90 79 139 100 80 51 51 107 55 92 87 24.5
Depth of organic matter (cm)
Depth of organic mattera (%)
5.5 3.0 5.5 2.5 6.0 1.5 1.0 2.5 4.5 2.5 1.5 1.5 5.8 2.5 10.0 4.0 3.5 3.0 3.7 n.a.
44 24 44 20 48 12 8 20 36 20 12 12 47 20 80 32 28 24 30 60.8
n.a., not applicable. a % of soil (0–12.5 cm).
a negative correlation between Ca and Al in Geescroft. Blake et al. (1999) have discussed these relationships in detail. The mean biomass C concentration of the woodland Geescroft soil was more than twice that of the Broadbalk arable soil, 565 mg g21 compared to 263 mg g21, respectively. Unexpectedly, however, the cvs of biomass C for the woodland and arable soils were very similar (22.8 and 25.6%, respectively). The estimates of the cvs are computed directly from the data given in Tables 1 and 2, treating the samples as if they were randomly distributed rather than in a systematic layout. All the measured properties, except biomass C, were more variable in Geescroft than Broadbalk (Tables 1 and 2). The Geescroft soil is very acid (c.a. pH 3.9 –4.7 in water) as the area has not been limed for at least 120 yr. The buffering capacity in this soil is now governed by free aluminum rather than calcium carbonate (Sverdrup et al., 1995). This is illustrated by the high concentrations of exchangeable Al and Mn compared to Broadbalk and the strong correlation between Al and pH (Blake, PhD thesis, University of Reading, 1994). 3.3. Effect of organic matter The depth of the organic matter was measured in the Geescroft soils. The acidic nature of the soil means that decomposition of organic material is very slow (Jenkinson, 1971), more so than other less acidic woodland soils at
Rothamsted, and varying amounts of litter has built up on the surface. When the identifiable leaf litter was scraped away, the top layer of the soil contained a large proportion of decomposing organic material, this varied in depth from 1 to 10 cm. These pockets of organic material will have an influence on both biomass C and total soil organic C and could explain some of the variation in the organic C results. The distribution of biomass C within the marked plot showed a more even distribution pattern, with more samples with a higher concentration of biomass C, than in the Broadbalk plot. Significant correlations were found between certain of the chemical components of the soil, such as Ca and Mg, Al and pH and K and Mg.
4. Discussion 4.1. Mineral concentrations in the soil from the two sites The main difference between the two soils was the much greater variability of soil organic C and exchangeable cations within Geescroft Wilderness than Broadbalk (Tables 1 and 2). This was expected as the Broadbalk arable soil receives annual fertiliser applications and is well mixed by ploughing every year. In contrast the Geescroft soil has had less mixing and has not been mixed at all, even by worms, in the last few decades as the pH became too acidic. Since 1885 the site has gradually reverted to deciduous woodland.
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The significant relationship between exchangeable Ca and Mg in the Geescroft soil could possibly be related to inputs of both to the soil from the leaves of the woodland trees that ultimately supply the ground leaf litter. Duvigneaud et al. (1969) calculated total inputs in leaf litter of 91 kg ha21 Ca and 8 kg ha21 Mg (Ca-to-Mg ratio of 11.3:1). The litter of Geescroft Wilderness had a comparable Ca-to-Mg ratio of 12.5:1. This was higher than the ratio Ca-to-Mg of 8.4:1 in the soil of Geescroft and was probably due to the acidic nature of the soil decreasing the rate of breakdown of the leaf litter, slowing the release of exchangeable cations (Williams and Gray, 1974). There was a significant positive correlation between exchangeable Ca and pH across the site. Again, this was due to the overall acidity of the site and the lack of mixing of the soil. This has produced pockets of soil with higher Ca concentrations and correspondingly higher pH.
detect a statistically significant change in biomass C in response to subtle environmental change? It would seem best to sample at the same time each year to minimise effects of seasonal changes in soil biomass content. However, there is evidence that the biomass does not undergo large fluctuations in community size under UK arable or grassland soils (e.g. Patra et al., 1990). Exceptions include soils where large amounts of crop residues, such as cereal straw, have recently been incorporated (e.g. Ocio et al., 1991). Under these conditions microbial dynamics are very rapid, which would then lead to difficulties in comparing data. As all the ECN terrestrial sites are based on areas that do not involve major changes in land use, this should not be a problem. Similarly, the sampling of soils which are partially, or completely air-dry should be avoided as biomass dynamics are similarly difficult to evaluate under such conditions (e.g. Shen et al., 1987).
4.2. Variation in the biomass C in the soils from both sites
4.4. Prediction of sampling numbers to detect environmental change
It is interesting that there was no significant correlation between biomass C and soil organic C at either site. In view of the many reports of significant positive correlation between these two properties, it might be expected that this would occur (Jenkinson and Powlson, 1976; Powlson et al., 1987). That it did not suggests that these linked parameters should not be considered as sensitive indicators of environmental change. Rather, they appear to respond only to major changes such as land use, e.g. from grassland to arable (Jenkinson and Powlson, 1976), straw incorporation from burning (Powlson et al., 1987) or increases in soil heavy metal concentration (Brookes and McGrath, 1984). 4.3. The possible use of biomass as an indicator of environment change In a long-term study, the area of interest would be resampled on a regular basis although it is impossible to resample in exactly the same locations as these would have been disturbed by the previous sampling or cultivation. As expected from previous work, the fumigation – extraction method provided an apparently satisfactory measurement of the biomass in these two differently managed soils. However, this alone is not enough to recommend the method for use as an indicator of environmental change. The monitoring of more subtle environmental effects needs a method that will detect changes in the short term in soils with much inherent variability. This is different to the well-established use of biomass measurements to detect the effects of major changes in microbial biomass in well-managed soils (Jenkinson and Powlson, 1976; Powlson et al., 1987). The main question is: given the relatively large inherent variability in biomass C measurements in both sites, how many samples would have to be taken on each occasion to
Table 3 values were calculated using the two component (cvs) estimated from the results for Broadbalk (cL ¼ 25.6% and cR ¼ 7.9%) and Geescroft Wilderness (cL ¼ 22.8% and c R ¼ 9.0%) and compromise values (c L ¼ 25% and cR ¼ 8%). This shows that the statistical probability of detecting a significant change between samplings based on increased laboratory analysis is very low and the number of samples taken in the field would have to be increased. If a significant trend over time using regularly spaced time intervals (say of 10 yr) was to be detected, we would require the regression equation giving the significance of the linear regression of the measurement against time (the mean percentage change per unit interval). For a straight line fitted to I samples representing (I-1) successive unit intervals, the coefficient of variation of the slope of the line was: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi cm = ðnððI 3 2 IÞ=12ÞÞ: Calculated from this equation Table 4 gives the predicted number of samples that would need to be taken for a number
Table 3 Theoretical least significant percentage differences for a range of numbers of samples and analytical replicates No. of samples taken
10 20 40 80 160 320
Number of analytical replicates per sample One
Three
Five
23.48 16.60 11.74 8.30 5.87 4.15
22.74 16.08 11.37 8.04 5.69 4.02
22.59 15.97 11.29 7.98 5.65 3.99
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of sampling periods for the probability of change to be significant. The analytical variation was ignored as it was so small compared with the site variation (a sample coefficient of variation of 25% was used). This showed that if 160 samples were taken at each sampling then after three samplings (two intervals) the least significant percentage change was 5.59%, i.e. still above 5%. The statistical analysis showed that the percent (LSD’s) of the soils could be decreased either by taking more samples in the field or, to a much lesser extent, by carrying out more replicate analyses of each sample. However, sample numbers cannot be increased indefinitely as there are obviously practical restrictions upon the number that can be handled at one time. Also, large numbers of samples taken at one time could cause damage to the site and either prevent other measurements or interfere with the next sampling. A possible compromise would be to take 20 samples, which would each be analysed in triplicate. However, even if 20 samples were taken, and the sampling done at regular intervals, the error would still only decline to about 16% after three sampling intervals, or four sampling events (Table 4). Thus, even after this time, the different forms of inherent variation that combine to give the overall variability in the soil measurement is still likely to mask any differences resulting from environmental change. Biomass C has been shown to be a useful early indicator of changes in soil as it responds much more rapidly to changes due, for example, to soil management than does total soil organic matter (e.g. Powlson et al., 1987; Saffigna et al., 1989). However, in both instances these experiments were done on managed field plots and even then the measurements of Powlson et al. (1987) were made 18 yr after the first treatments and those by Saffigna et al. (1989) were 6 yr after the experiment started. These can only be considered as gross changes when compared to differences attributable to environmental change. Where much less marked changes are expected the statistical predictions show that, on Geescroft, even with regular measurements every 3 yr over a 15 yr period any significant changes would
Table 4 Detecting a trend using up to five regularly spaced sampling dates No. of samples takena
10 20 40 80 160 320
Number of sampling intervals Two
Three
Four
Five
22.36 15.81 11.81 7.91 5.59 3.96
22.36 15.81 11.18 7.91 5.59 3.96
21.21 15.00 10.62 7.50 5.31 3.75
20.00 14.16 10.00 7.08 5.00 3.54
The results are in units of least significant percentage change. a Analysed in triplicate.
be masked by background variation in biomass C concentrations. 4.5. Biomass C in relation to other soil properties The chemical components of the Geescroft soil also had cvs similar to the biomass C due to the heterogeneous nature of the soil. Again the inherent sample variation, when measuring these properties every 3 yr over a 15 yr period, would mask any subtle changes. Past data from the Geescroft Wilderness show that the soil has changed considerably in the long term (Blake et al., 1994, 1999). For example, since 1883 exchangeable Ca has decreased from c.a. 2800 to 490 mg kg21. If it is assumed that the variability in those measurements was similar to those found in our work then we would only detect a statistically significant change in exchangeable Ca about 35 – 40 yr after the first measurement. In comparison, the chemical properties of the Broadbalk soil were much less variable because of the regular fertiliser additions and mixing. Exchangeable Ca had a cv of only 3.6%, so significant change in this property would be detected sooner. 4.6. Conclusions The spatial variation in microbial biomass C concentrations in the managed arable soil of Broadbalk was as large as in the unmanaged woodland Geescroft soil. Other chemical determinants, e.g. pH and exchangeable cations, were much less variable. Analytical and sampling errors would have to be kept to a minimum to prevent these errors masking detection of genuine changes. Some sampling errors can be minimised by using a simple sampling procedure at each site and by sampling at the same time each year (Kaiser et al., 1994). In the two soils studied here, sampling errors have been shown statistically to decrease when more samples were taken. However, the number of samples which would be required, to detect a significant change during a reasonable time would be impractical and so only trends rather than statistically significant differences would be detectable in the shorter term. The more samples taken at regular intervals, the more certain it becomes that any changes in microbial biomass concentrations would be detected, but an excessive number of samples would have to be taken to detect that statistically significant change. Soil microbial biomass C would only show trends in the long term and may only be as sensitive, as an indicator of environmental change, as other soil chemical properties in a semi-natural or natural ecosystem in the short term. However, as it changes faster than, for example, soil organic C, it would show the direction of such change more quickly. Such change may take too long for biomass measurements to be a useful indicator of environmental
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change, despite its proven usefulness as an indicator of changes of soil management.
Acknowledgements Rothamsted Research Ltd receives grant-aided support from the Biotechnology and Biological Sciences Research Council of the UK.
References Allison, L.E., 1965. Organic carbon. In: Black, C.A., (Ed.), Methods of Soil Analysis, vol. 2. American Society of Agronomy, Madison, pp. 1149–1178. Avery, B.W., Catt, J.A., 1995. The Soil at Rothamsted, Lawes Agricultural Trust, Harpenden, UK. Bawden, F.C., 1969. The Broadbalk Wheat Experiment, Rothamsted Experimental Station Report for 1968, Part 2, p. 215. Blake, L., Johnston, A.E., Goulding, K.W.T., 1994. Mobilization of aluminum in soil by acid deposition and its uptake by grass cut for hay—a chemical time bomb. Soil Use and Management 10 (2), 51–55. Blake, L., Goulding, K.W.T., Mott, C.J.B., Johnston, A.E., 1999. Changes in soil chemistry accompanying acidification over more than 100 years under woodland and grass at Rothamsted Experimental Station, UK. European Journal of Soil Science 50, 401–412. Brookes, P.C., McGrath, S.P., 1984. Effects of metal toxicity on the size of the soil microbial biomass. Journal of Soil Science 35, 341–346. Burt, T.P., 1994. Long-term study of the natural environment—perceptive science or mindless monitoring? Progress in Physical Geography 18 (4), 475–496. Duvigneaud, P., Denayer-De Smets, S., Marbaises, J.-L., 1969. Litie`re totale annuelle et restitution au sol des polye´le´ments bioge`nes. Bulletin de la Socie´te´ Royale de Botanique de Belgique 102, 339–354. Goulding, K.W.T., Johnston, A.E., 1989. Long-term atmospheric deposition and soil acidification at Rothamsted Experimental Station. In: Longhurst, J.W.S., (Ed.), Acid Deposition; Sources, Effects and Controls, British Library, Technical Manuscripts, London, pp. 213 –218. Jenkinson, D.S., 1971. The accumulation of organic matter in soil left uncultivated. Rothamstead Experimental Station Report for 1970, Part 2, Lawes Agricultural Trust, Harpenden, UK. Jenkinson, D.S., Powlson, D., 1976. The effects of biocidal treatments on metabolism in soil V. A method for measuring soil biomass. Soil Biology & Biochemistry 8, 209–213. Johnston, A.E. 1969. Plant nutrients in Broadbalk soils, Rothamsted Experimental Station Report for 1968 (Part 2), p. 93–115.
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Johnston, A.E., 1993. The Rothamsted classical experiments. In: Leigh, R.A., Johnston, A.E. (Eds.), Long-term Experiments in Agricultural and Ecological Sciences, CAB International, Wallingford, pp. 9–38. Johnston, A.E., Goulding, K.W.T., Poulton, P.R., 1986. Soil acidification during more than 100 years under permanent grassland and woodland at Rothamsted. Soil Use and Management 2, 3 –10. Kaiser, E., Martens, R., Heinemeyer, O., 1994. Temporal changes in soil microbial biomass carbon in an arable soil. Consequences for soil sampling. Plant and Soil 170, 287 –295. Metson, A.J., 1956. Methods of chemical analysis for soil survey samples. New Zealand Soil Bureau Bulletin 12. Ocio, J.A., Brookes, P.C., Jenkinson, D.S., 1991. Field incorporation of straw and its effects on soil microbial biomass and soil inorganic N. Soil Biology & Biochemistry 23, 171– 176. Patra, D.D., Brookes, P.C., Coleman, K., Jenkinson, D.S., 1990. Seasonal changes of soil microbial biomass in an arable and a grassland soil which have been under uniform management for many years. Soil Biology & Biochemistry 22 (6), 739–742. Powlson, D.S., Brookes, P.C., Christensen, B.T., 1987. Measurements of soil microbial biomass provide an early indication of changes in total soil organic matter due to straw incorporation. Soil Biology & Biochemistry 19, 159–164. Saffigna, P.C., Powlson, D.S., Brookes, P.C., Thomas, G.A., 1989. Influence of sorghum residues and tillage on soil organic matter and soil microbial biomass in an Australian vertisol. Soil Biology & Biochemistry 21, 759–765. Shen, S.M., Brookes, P.C., Jenkinson, D.S., 1987. Soil respiration and the measurement of microbial biomass C by the fumigation technique in fresh and in air-dried soil. Soil Biology & Biochemistry 19, 153–158. Sykes, J.M., Lane, A.M., 1996. The United Kingdom Environmental Change Network: Protocols for Standard Measurements at Terrestrial sites, Centre for Ecology and Hydrology, NERC, ITE Merlewood, Cumbria, UK. Sverdrup, H., Warfvinge, P., Blake, L., Goulding, K.W.T., 1995. Modelling recent and historic soil data from the Rothamsted Experimental Station, UK using SAFE. Agriculture, Ecosystems and Environment 53, 161– 177. Tinker, P.B., 1993. Monitoring environmental change through networks. In: Leigh, R.A., Johnston, A.E. (Eds.), Long-term Experiments in Agricultural and Ecological Sciences, CAB International, Wallingford, pp. 407–421. Vance, E.D., Brookes, P.C., Jenkinson, D.S., 1987. An extraction method for measuring soil microbial C. Soil Biology & Biochemistry 19 (6), 703– 707. Williams, S.T., Gray, T.R.G., 1974. Decomposition of litter on the soil surface. In: Dickinson, C.H., Pugh, G.J.F. (Eds.), Biology of Plant Litter Decomposition, vol. 2. Academic Press, London, pp. 611–632. Wu, J., Joergensen, R.G., Pommerening, B., Chaussod, R., Brookes, P.C., 1990. Measurement of soil microbial biomass C by fumigationextraction—an automated procedure. Soil Biology & Biochemistry 22, 1167–1169.
Evaluating soil microbial biomass carbon as an indicator of long-term environmental change P.R. Hargreaves*, P.C. Brookes, G.J.S. Ross, P.R. Poulton Agriculture and the Environment Division, Rothamsted Research Ltd, Harpenden, Herts AL5 2JQ, UK Received 20 February 2002; received in revised form 16 October 2002; accepted 8 November 2002
Abstract The aim is to assess whether soil microbial biomass carbon (biomass C) could be used as an indicator of environmental change in natural and semi-natural ecosystems. Biomass C was measured by fumigation– extraction in soils from two sites at Rothamsted. One was a plot from the Broadbalk Wheat Experiment, given inorganic fertiliser and chalk, which has been in continuous cultivation for more than 150 yr. The other was a similar sized area, from Geescroft Wilderness, which has been left to revert to woodland since 1885, after being an arable field. Other soil properties (pH, soil organic C and exchangeable cations) were also measured to compare with biomass C. The coefficients of variation (cvs) of the properties measured were calculated for comparison, little difference was found between the cvs for biomass C from each site: cv ¼ 26% for Broadbalk and 23% for Geescroft. The cvs for the other, chemical properties, were mostly ,10% for Broadbalk and generally . 25% for Geescroft, as expected, given the different cultivation histories. Statistical analysis of the variation in biomass C concentration revealed that such measurements would not be valid indicators of environmental change, without processing impossibly large numbers of samples. To decrease the least significant percentage change to less than 5% after three samplings, 320 samples would have to be taken each time. This would be also be true of the other chemical properties in Geescroft Wilderness, where the measured background variation would mask any subtle environmental change. This indicates that, for some properties at least, statistically significant changes will only be detected in the longer term with regular sampling, e.g. 30 – 40 yr. q 2003 Elsevier Science Ltd. All rights reserved. Keywords: Environmental change; Soil microbial biomass; Carbon; Soil chemical properties
1. Introduction The Environmental Change Network (ECN) was launched in January 1992 as a network of sites throughout the UK to monitor environmental change, specifically brought about by climate or atmospheric pollutants (Tinker, 1993; Burt, 1994). Methods have been established to monitor the many potential inputs and effects of environmental change (Sykes and Lane, 1996). These methods aim to follow a holistic approach with the integrated monitoring of insects, animals, vegetation and chemical properties. Changes in the chemistry of the soils at the terrestrial sites are currently assessed by soil sampling every 5 yr. The measurement of soil microbial biomass C was one method of providing a more sensitive indicator of change than soil chemistry alone * Corresponding author. Tel.: þ 44-1582-763133; fax: þ 44-1582760981. E-mail address: [email protected] (P.R. Hargreaves).
and addressing the gap in monitoring, between the soil chemistry and the vegetation cover. An initial comparison of one of the more commonly used microbial biomass methods (fumigation – extraction) was done at Rothamsted Research, one of the ECN terrestrial sites. Our aim is to assess whether microbial biomass C could possibly serve as an ‘environmental indicator’ or whether inherent variation, found in a regular soil sampling method, would mask any effects of environmental change. Biomass C was considered as a possible measure in this context because it is well established as an early indicator of gross changes in C input caused by pollution (Brookes and McGrath, 1984) or crop residue incorporation (e.g. Jenkinson and Powlson, 1976; Powlson et al., 1987). As we expected the variability of soil microbial biomass to be large in a natural or semi-natural ecosystem, a deliberate comparison was made with a homogenous, well mixed, fertilised arable soil that was presumed to be less variable for the measured soil properties.
0038-0717/03/$ - see front matter q 2003 Elsevier Science Ltd. All rights reserved. doi:10.1010/S0038-0717(02)00291-2
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The main objective is to assess the natural variation in soil biomass C concentration and compare this with the variability of other soil properties in the two soils. The use of statistical predictions, based on the variation of the soil properties, could then determine the number of samples required to detect statistically significant change, due to effects of environmental change, on the soil microbial biomass C concentration in the two soils.
2. Materials and methods 2.1. Sites and soil sampling The first site was a homogenous soil taken from an arable plot, given inorganic fertiliser (N at 96 kg ha21, P at 35 kg ha21, K at 90 kg ha21 and Mg at 30 kg ha21 annually for more than 150 yr) and chalk, on the Broadbalk Wheat Experiment. This is the longest continuously running agronomic experiment in the world, started in 1843 (Bawden, 1969; Johnston, 1993). The second site was Geescroft Wilderness, a site of natural regeneration with a highly acidic soil (pH 4.2). This was initially part of an arable field fenced off in 1885 and allowed to regenerate to mixed deciduous woodland. No chalk had been added to the site since it was fenced off (Johnston et al., 1986; Goulding and Johnston, 1989). The soil on both sites is a flinty silty clay loam (24% clay) of the Batcombe series (Chromic Luvisol (FAO); Avery and Catt, 1995). A selected area (28 £ 6 m2) within Broadbalk plot No. 141, pH 7.2, was sampled (Johnston, 1969). To avoid any edge effect, a 1.5 m strip down each side and a 1 m strip at either end of the plot were avoided. Eighteen sampling points were used at regular intervals along four transects running the length of the plot, these transects were 1 m apart. Transects 1 and 3 had five sampling points and transects 2 and 4 had four sampling points. An area of the same size was marked out in the Geescroft Wilderness and 18 samples taken in the same way (Fig. 1). At each of the 18 sampling points, four cores were taken with a 2 cm dia auger; the four cores were then bulked to form 18 samples. The cores were taken from the 0 –23 cm depth on Broadbalk (plough depth) and the 0 –12.5 cm depth on Geescroft. These depths were assumed to provide the most representative soil depths to quantify the soil microbial biomass.
Fig. 1. The pattern of sampling used for Broadbalk and Geescroff.
2.2. Soil analysis Half of each sample was air-dried, sieved , 2 mm and analysed for pH (1:2 soil-to-H2O ratio), cations exchangeable to 0.5 M ammonium acetate (Metson, 1956) and organic C using an automated combustion method after an acid treatment to eliminate calcium carbonate (Allison, 1965). The other half of each sample was carefully dried to 40% of water holding capacity, ensuring that no part of the sample became air-dry, then sieved , 2 mm. Biomass C was then measured by fumigation –extraction. Briefly, the moist sub-samples (45 g dry weight) were then fumigated for 24 h with alcohol-free CHCl3 and extracted with 0.5 M K2SO4. A second non-fumigated set of sub-samples were extracted during the 24 h fumigation (Vance et al., 1987; Wu et al., 1990). The filtered soil extracts (Whatman No. 45) were frozen at 2 15 8C prior to analysis for organic C by automated UVpersulphate analysis (Wu et al., 1990). Biomass C (Bc) was calculated from: Bc ¼ 2.22 Ec, where Ec ¼ [(C extracted from fumigated soil minus C extracted from non-fumigated soil)]. All results are expressed on an oven-dry soil basis (105 8C, 24 h) and are the mean of three replicate analyses. 2.3. Statistical analysis Variogram analysis was used to determine whether relationships existed between samples taken close together and those samples taken at a distance from each other on each of the plots. The coefficients of variation (cvs) for the various properties measured, to assess any statistical relationships, were also calculated. It was assumed that if samples are taken at different locations within a site, two sources of variability are involved: field (or site) variation when the samples are taken and analytical error. In the simplest of models these deviations are considered independent and normally distributed about a mean value (unless the distribution is clearly skewed with a few very large values and many smaller values). The observed value of the jth laboratory determination of the ith sample is given by the formula: yij ¼ m þ di þ 1ij where m is the mean value, di is the location deviation with cv cL expressed as a percentage, and therefore standard deviation mcL/100 and 1ij is the laboratory deviation with a standard deviation micR/100. If the coefficient of variation between locations is cL and the coefficient of variation between replicate laboratory determinations is cR then the formula for the coefficient of variation of the mean of n samples, each replicated r times, is: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi cm ¼ ðc2L =n þ c2R =rnÞ
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The significance of a percentage difference D between two successive mean determinations may be tested by comparing the ratio: pffiffi D=cm 2 with critical values of the t-distribution (with degrees of freedom depending on the number of samples used to determine cL). With little loss of accuracy, the normal distribution may be used instead. At the 5% level of significance the value of t may be taken as 1.96, so that the least significant difference (LSD) in percentage terms is computed as: pffiffi %LSD ¼ 1:96cm 2
3. Results 3.1. Variability of biomass C As expected, the biomass C concentrations and other measured properties in the Broadbalk and Geescroft soils varied considerably, both within and between soils (Tables 1 and 2). The Broadbalk site was presumed to be a much more homogenous soil than Geescroft because of many years of annual ploughing and fertiliser inputs. This was evident from the much smaller (cv) for exchangeable K and soil organic C (, 10% for the Broadbalk soil, while those of Geescroft were . 25%). The higher cv of biomass C compared to other soil properties from Broadbalk was expected as this is a biological variable, which is affected,
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for example, by the distribution of organic material such as root and stubble within the soil both laterally and with depth. The mean biomass C concentration in Broadbalk of 263 mg C g21 soil was very similar to previous measurements (Patra et al., 1990). However, this mean figure did encompass considerable variation, ranging from 134 to 350 mg C g21 soil. The distribution of biomass C within the Broadbalk plot was uniform with few outlying points; this was also generally true in the Geescroft plot. Here, although the measured amounts of biomass C were twice as large as Broadbalk, the samples were also evenly distributed except for fewer samples at lower biomass C concentrations (less than 400 mg C g21 soil). The results of variogram analysis revealed no relationship between the distance between samples taken and the analysis of these samples. The lack of a relationship indicated that the spatial variability was random. Even if a close correlation between neighbouring observations was found in a given year, this is not relevant to the problem of detecting significant change over time, since different sample locations will be necessary on later occasions, to minimise effects of soil disturbance. 3.2. Variability of soil properties There were few significant correlations between soil properties, including relationships between biomass C and soil organic C, for either Broadbalk or Geescroft (data not shown). Of the few that occurred, the most important were the positive correlations between pH and K, Ca and Mg, and
Table 1 Analyses of Broadbalk soil (Plot 141, NPK) Sample no.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Mean CV %
Organic C (%)
1.08 1.08 1.05 1.12 1.05 1.11 1.10 1.12 1.17 1.21 1.23 1.15 1.15 1.15 1.19 1.04 1.08 1.08 1.1 5.0
n.a., not applicable.
pH (in H20)
7.91 7.88 7.90 7.94 7.90 7.91 7.97 7.91 7.83 7.94 7.93 7.97 7.94 7.83 7.94 7.89 8.04 7.98 7.90 0.6
Biomass
Exchangeable cations (mg g21 soil)
C
K
Ca
Mg
Al
Mn
315 279 317 171 303 180 246 350 321 205 341 337 228 221 294 134 186 300 263 25.6
195 190 180 227 231 193 188 196 166 200 172 203 204 223 187 180 182 182 194 9.3
4360 4270 4140 4290 4410 4640 4470 4350 4280 4580 4600 4560 4550 4430 4650 4510 4410 4750 4457 3.6
145 148 146 158 162 140 138 138 150 136 125 133 142 149 137 128 138 140 142 6.6
,2.26 ,2.26 4.13 ,2.26 ,2.26 ,2.26 ,2.26 ,2.26 ,2.26 ,2.26 65.71 ,2.26 ,2.26 ,2.26 ,2.26 ,2.26 ,2.26 ,2.26 n.a. n.a.
,0.05 ,0.05 2.28 ,0.05 ,0.05 ,0.05 0.21 ,0.05 ,0.05 ,0.05 0.74 ,0.05 ,0.05 ,0.05 ,0.05 ,0.05 ,0.05 ,0.05 n.a. n.a.
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Table 2 Analyses of Geescroft Wilderness soil Sample no.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Mean CV %
Organic C (%)
3.76 5.64 4.21 3.35 3.90 5.08 3.45 3.19 4.45 4.74 3.10 3.81 3.25 6.56 8.14 3.60 3.89 3.35 4.30 31.0
pH (in H2O)
3.88 4.18 4.36 3.97 4.36 3.96 4.42 4.28 4.25 4.01 4.13 4.35 4.50 3.73 3.70 4.40 4.27 4.50 4.18 6.0
Biomass C
592 711 740 468 727 616 656 551 645 483 320 622 506 724 451 519 315 530 565 22.8
Exchangeable cations (mg g21 soil) K
Ca
Mg
Al
Mn
250 200 132 182 124 190 116 141 159 243 179 130 120 208 188 103 161 91 162 28.5
500 950 1010 530 930 510 1240 700 790 570 430 870 930 530 730 840 620 1120 767 30.7
76 88 114 71 112 68 126 86 91 77 63 90 107 74 74 89 83 121 89 21.3
437 326 264 438 394 392 369 448 410 468 365 360 377 444 377 352 396 371 388 12.7
90 97 86 84 81 81 105 99 90 79 139 100 80 51 51 107 55 92 87 24.5
Depth of organic matter (cm)
Depth of organic mattera (%)
5.5 3.0 5.5 2.5 6.0 1.5 1.0 2.5 4.5 2.5 1.5 1.5 5.8 2.5 10.0 4.0 3.5 3.0 3.7 n.a.
44 24 44 20 48 12 8 20 36 20 12 12 47 20 80 32 28 24 30 60.8
n.a., not applicable. a % of soil (0–12.5 cm).
a negative correlation between Ca and Al in Geescroft. Blake et al. (1999) have discussed these relationships in detail. The mean biomass C concentration of the woodland Geescroft soil was more than twice that of the Broadbalk arable soil, 565 mg g21 compared to 263 mg g21, respectively. Unexpectedly, however, the cvs of biomass C for the woodland and arable soils were very similar (22.8 and 25.6%, respectively). The estimates of the cvs are computed directly from the data given in Tables 1 and 2, treating the samples as if they were randomly distributed rather than in a systematic layout. All the measured properties, except biomass C, were more variable in Geescroft than Broadbalk (Tables 1 and 2). The Geescroft soil is very acid (c.a. pH 3.9 –4.7 in water) as the area has not been limed for at least 120 yr. The buffering capacity in this soil is now governed by free aluminum rather than calcium carbonate (Sverdrup et al., 1995). This is illustrated by the high concentrations of exchangeable Al and Mn compared to Broadbalk and the strong correlation between Al and pH (Blake, PhD thesis, University of Reading, 1994). 3.3. Effect of organic matter The depth of the organic matter was measured in the Geescroft soils. The acidic nature of the soil means that decomposition of organic material is very slow (Jenkinson, 1971), more so than other less acidic woodland soils at
Rothamsted, and varying amounts of litter has built up on the surface. When the identifiable leaf litter was scraped away, the top layer of the soil contained a large proportion of decomposing organic material, this varied in depth from 1 to 10 cm. These pockets of organic material will have an influence on both biomass C and total soil organic C and could explain some of the variation in the organic C results. The distribution of biomass C within the marked plot showed a more even distribution pattern, with more samples with a higher concentration of biomass C, than in the Broadbalk plot. Significant correlations were found between certain of the chemical components of the soil, such as Ca and Mg, Al and pH and K and Mg.
4. Discussion 4.1. Mineral concentrations in the soil from the two sites The main difference between the two soils was the much greater variability of soil organic C and exchangeable cations within Geescroft Wilderness than Broadbalk (Tables 1 and 2). This was expected as the Broadbalk arable soil receives annual fertiliser applications and is well mixed by ploughing every year. In contrast the Geescroft soil has had less mixing and has not been mixed at all, even by worms, in the last few decades as the pH became too acidic. Since 1885 the site has gradually reverted to deciduous woodland.
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The significant relationship between exchangeable Ca and Mg in the Geescroft soil could possibly be related to inputs of both to the soil from the leaves of the woodland trees that ultimately supply the ground leaf litter. Duvigneaud et al. (1969) calculated total inputs in leaf litter of 91 kg ha21 Ca and 8 kg ha21 Mg (Ca-to-Mg ratio of 11.3:1). The litter of Geescroft Wilderness had a comparable Ca-to-Mg ratio of 12.5:1. This was higher than the ratio Ca-to-Mg of 8.4:1 in the soil of Geescroft and was probably due to the acidic nature of the soil decreasing the rate of breakdown of the leaf litter, slowing the release of exchangeable cations (Williams and Gray, 1974). There was a significant positive correlation between exchangeable Ca and pH across the site. Again, this was due to the overall acidity of the site and the lack of mixing of the soil. This has produced pockets of soil with higher Ca concentrations and correspondingly higher pH.
detect a statistically significant change in biomass C in response to subtle environmental change? It would seem best to sample at the same time each year to minimise effects of seasonal changes in soil biomass content. However, there is evidence that the biomass does not undergo large fluctuations in community size under UK arable or grassland soils (e.g. Patra et al., 1990). Exceptions include soils where large amounts of crop residues, such as cereal straw, have recently been incorporated (e.g. Ocio et al., 1991). Under these conditions microbial dynamics are very rapid, which would then lead to difficulties in comparing data. As all the ECN terrestrial sites are based on areas that do not involve major changes in land use, this should not be a problem. Similarly, the sampling of soils which are partially, or completely air-dry should be avoided as biomass dynamics are similarly difficult to evaluate under such conditions (e.g. Shen et al., 1987).
4.2. Variation in the biomass C in the soils from both sites
4.4. Prediction of sampling numbers to detect environmental change
It is interesting that there was no significant correlation between biomass C and soil organic C at either site. In view of the many reports of significant positive correlation between these two properties, it might be expected that this would occur (Jenkinson and Powlson, 1976; Powlson et al., 1987). That it did not suggests that these linked parameters should not be considered as sensitive indicators of environmental change. Rather, they appear to respond only to major changes such as land use, e.g. from grassland to arable (Jenkinson and Powlson, 1976), straw incorporation from burning (Powlson et al., 1987) or increases in soil heavy metal concentration (Brookes and McGrath, 1984). 4.3. The possible use of biomass as an indicator of environment change In a long-term study, the area of interest would be resampled on a regular basis although it is impossible to resample in exactly the same locations as these would have been disturbed by the previous sampling or cultivation. As expected from previous work, the fumigation – extraction method provided an apparently satisfactory measurement of the biomass in these two differently managed soils. However, this alone is not enough to recommend the method for use as an indicator of environmental change. The monitoring of more subtle environmental effects needs a method that will detect changes in the short term in soils with much inherent variability. This is different to the well-established use of biomass measurements to detect the effects of major changes in microbial biomass in well-managed soils (Jenkinson and Powlson, 1976; Powlson et al., 1987). The main question is: given the relatively large inherent variability in biomass C measurements in both sites, how many samples would have to be taken on each occasion to
Table 3 values were calculated using the two component (cvs) estimated from the results for Broadbalk (cL ¼ 25.6% and cR ¼ 7.9%) and Geescroft Wilderness (cL ¼ 22.8% and c R ¼ 9.0%) and compromise values (c L ¼ 25% and cR ¼ 8%). This shows that the statistical probability of detecting a significant change between samplings based on increased laboratory analysis is very low and the number of samples taken in the field would have to be increased. If a significant trend over time using regularly spaced time intervals (say of 10 yr) was to be detected, we would require the regression equation giving the significance of the linear regression of the measurement against time (the mean percentage change per unit interval). For a straight line fitted to I samples representing (I-1) successive unit intervals, the coefficient of variation of the slope of the line was: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi cm = ðnððI 3 2 IÞ=12ÞÞ: Calculated from this equation Table 4 gives the predicted number of samples that would need to be taken for a number
Table 3 Theoretical least significant percentage differences for a range of numbers of samples and analytical replicates No. of samples taken
10 20 40 80 160 320
Number of analytical replicates per sample One
Three
Five
23.48 16.60 11.74 8.30 5.87 4.15
22.74 16.08 11.37 8.04 5.69 4.02
22.59 15.97 11.29 7.98 5.65 3.99
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of sampling periods for the probability of change to be significant. The analytical variation was ignored as it was so small compared with the site variation (a sample coefficient of variation of 25% was used). This showed that if 160 samples were taken at each sampling then after three samplings (two intervals) the least significant percentage change was 5.59%, i.e. still above 5%. The statistical analysis showed that the percent (LSD’s) of the soils could be decreased either by taking more samples in the field or, to a much lesser extent, by carrying out more replicate analyses of each sample. However, sample numbers cannot be increased indefinitely as there are obviously practical restrictions upon the number that can be handled at one time. Also, large numbers of samples taken at one time could cause damage to the site and either prevent other measurements or interfere with the next sampling. A possible compromise would be to take 20 samples, which would each be analysed in triplicate. However, even if 20 samples were taken, and the sampling done at regular intervals, the error would still only decline to about 16% after three sampling intervals, or four sampling events (Table 4). Thus, even after this time, the different forms of inherent variation that combine to give the overall variability in the soil measurement is still likely to mask any differences resulting from environmental change. Biomass C has been shown to be a useful early indicator of changes in soil as it responds much more rapidly to changes due, for example, to soil management than does total soil organic matter (e.g. Powlson et al., 1987; Saffigna et al., 1989). However, in both instances these experiments were done on managed field plots and even then the measurements of Powlson et al. (1987) were made 18 yr after the first treatments and those by Saffigna et al. (1989) were 6 yr after the experiment started. These can only be considered as gross changes when compared to differences attributable to environmental change. Where much less marked changes are expected the statistical predictions show that, on Geescroft, even with regular measurements every 3 yr over a 15 yr period any significant changes would
Table 4 Detecting a trend using up to five regularly spaced sampling dates No. of samples takena
10 20 40 80 160 320
Number of sampling intervals Two
Three
Four
Five
22.36 15.81 11.81 7.91 5.59 3.96
22.36 15.81 11.18 7.91 5.59 3.96
21.21 15.00 10.62 7.50 5.31 3.75
20.00 14.16 10.00 7.08 5.00 3.54
The results are in units of least significant percentage change. a Analysed in triplicate.
be masked by background variation in biomass C concentrations. 4.5. Biomass C in relation to other soil properties The chemical components of the Geescroft soil also had cvs similar to the biomass C due to the heterogeneous nature of the soil. Again the inherent sample variation, when measuring these properties every 3 yr over a 15 yr period, would mask any subtle changes. Past data from the Geescroft Wilderness show that the soil has changed considerably in the long term (Blake et al., 1994, 1999). For example, since 1883 exchangeable Ca has decreased from c.a. 2800 to 490 mg kg21. If it is assumed that the variability in those measurements was similar to those found in our work then we would only detect a statistically significant change in exchangeable Ca about 35 – 40 yr after the first measurement. In comparison, the chemical properties of the Broadbalk soil were much less variable because of the regular fertiliser additions and mixing. Exchangeable Ca had a cv of only 3.6%, so significant change in this property would be detected sooner. 4.6. Conclusions The spatial variation in microbial biomass C concentrations in the managed arable soil of Broadbalk was as large as in the unmanaged woodland Geescroft soil. Other chemical determinants, e.g. pH and exchangeable cations, were much less variable. Analytical and sampling errors would have to be kept to a minimum to prevent these errors masking detection of genuine changes. Some sampling errors can be minimised by using a simple sampling procedure at each site and by sampling at the same time each year (Kaiser et al., 1994). In the two soils studied here, sampling errors have been shown statistically to decrease when more samples were taken. However, the number of samples which would be required, to detect a significant change during a reasonable time would be impractical and so only trends rather than statistically significant differences would be detectable in the shorter term. The more samples taken at regular intervals, the more certain it becomes that any changes in microbial biomass concentrations would be detected, but an excessive number of samples would have to be taken to detect that statistically significant change. Soil microbial biomass C would only show trends in the long term and may only be as sensitive, as an indicator of environmental change, as other soil chemical properties in a semi-natural or natural ecosystem in the short term. However, as it changes faster than, for example, soil organic C, it would show the direction of such change more quickly. Such change may take too long for biomass measurements to be a useful indicator of environmental
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change, despite its proven usefulness as an indicator of changes of soil management.
Acknowledgements Rothamsted Research Ltd receives grant-aided support from the Biotechnology and Biological Sciences Research Council of the UK.
References Allison, L.E., 1965. Organic carbon. In: Black, C.A., (Ed.), Methods of Soil Analysis, vol. 2. American Society of Agronomy, Madison, pp. 1149–1178. Avery, B.W., Catt, J.A., 1995. The Soil at Rothamsted, Lawes Agricultural Trust, Harpenden, UK. Bawden, F.C., 1969. The Broadbalk Wheat Experiment, Rothamsted Experimental Station Report for 1968, Part 2, p. 215. Blake, L., Johnston, A.E., Goulding, K.W.T., 1994. Mobilization of aluminum in soil by acid deposition and its uptake by grass cut for hay—a chemical time bomb. Soil Use and Management 10 (2), 51–55. Blake, L., Goulding, K.W.T., Mott, C.J.B., Johnston, A.E., 1999. Changes in soil chemistry accompanying acidification over more than 100 years under woodland and grass at Rothamsted Experimental Station, UK. European Journal of Soil Science 50, 401–412. Brookes, P.C., McGrath, S.P., 1984. Effects of metal toxicity on the size of the soil microbial biomass. Journal of Soil Science 35, 341–346. Burt, T.P., 1994. Long-term study of the natural environment—perceptive science or mindless monitoring? Progress in Physical Geography 18 (4), 475–496. Duvigneaud, P., Denayer-De Smets, S., Marbaises, J.-L., 1969. Litie`re totale annuelle et restitution au sol des polye´le´ments bioge`nes. Bulletin de la Socie´te´ Royale de Botanique de Belgique 102, 339–354. Goulding, K.W.T., Johnston, A.E., 1989. Long-term atmospheric deposition and soil acidification at Rothamsted Experimental Station. In: Longhurst, J.W.S., (Ed.), Acid Deposition; Sources, Effects and Controls, British Library, Technical Manuscripts, London, pp. 213 –218. Jenkinson, D.S., 1971. The accumulation of organic matter in soil left uncultivated. Rothamstead Experimental Station Report for 1970, Part 2, Lawes Agricultural Trust, Harpenden, UK. Jenkinson, D.S., Powlson, D., 1976. The effects of biocidal treatments on metabolism in soil V. A method for measuring soil biomass. Soil Biology & Biochemistry 8, 209–213. Johnston, A.E. 1969. Plant nutrients in Broadbalk soils, Rothamsted Experimental Station Report for 1968 (Part 2), p. 93–115.
407
Johnston, A.E., 1993. The Rothamsted classical experiments. In: Leigh, R.A., Johnston, A.E. (Eds.), Long-term Experiments in Agricultural and Ecological Sciences, CAB International, Wallingford, pp. 9–38. Johnston, A.E., Goulding, K.W.T., Poulton, P.R., 1986. Soil acidification during more than 100 years under permanent grassland and woodland at Rothamsted. Soil Use and Management 2, 3 –10. Kaiser, E., Martens, R., Heinemeyer, O., 1994. Temporal changes in soil microbial biomass carbon in an arable soil. Consequences for soil sampling. Plant and Soil 170, 287 –295. Metson, A.J., 1956. Methods of chemical analysis for soil survey samples. New Zealand Soil Bureau Bulletin 12. Ocio, J.A., Brookes, P.C., Jenkinson, D.S., 1991. Field incorporation of straw and its effects on soil microbial biomass and soil inorganic N. Soil Biology & Biochemistry 23, 171– 176. Patra, D.D., Brookes, P.C., Coleman, K., Jenkinson, D.S., 1990. Seasonal changes of soil microbial biomass in an arable and a grassland soil which have been under uniform management for many years. Soil Biology & Biochemistry 22 (6), 739–742. Powlson, D.S., Brookes, P.C., Christensen, B.T., 1987. Measurements of soil microbial biomass provide an early indication of changes in total soil organic matter due to straw incorporation. Soil Biology & Biochemistry 19, 159–164. Saffigna, P.C., Powlson, D.S., Brookes, P.C., Thomas, G.A., 1989. Influence of sorghum residues and tillage on soil organic matter and soil microbial biomass in an Australian vertisol. Soil Biology & Biochemistry 21, 759–765. Shen, S.M., Brookes, P.C., Jenkinson, D.S., 1987. Soil respiration and the measurement of microbial biomass C by the fumigation technique in fresh and in air-dried soil. Soil Biology & Biochemistry 19, 153–158. Sykes, J.M., Lane, A.M., 1996. The United Kingdom Environmental Change Network: Protocols for Standard Measurements at Terrestrial sites, Centre for Ecology and Hydrology, NERC, ITE Merlewood, Cumbria, UK. Sverdrup, H., Warfvinge, P., Blake, L., Goulding, K.W.T., 1995. Modelling recent and historic soil data from the Rothamsted Experimental Station, UK using SAFE. Agriculture, Ecosystems and Environment 53, 161– 177. Tinker, P.B., 1993. Monitoring environmental change through networks. In: Leigh, R.A., Johnston, A.E. (Eds.), Long-term Experiments in Agricultural and Ecological Sciences, CAB International, Wallingford, pp. 407–421. Vance, E.D., Brookes, P.C., Jenkinson, D.S., 1987. An extraction method for measuring soil microbial C. Soil Biology & Biochemistry 19 (6), 703– 707. Williams, S.T., Gray, T.R.G., 1974. Decomposition of litter on the soil surface. In: Dickinson, C.H., Pugh, G.J.F. (Eds.), Biology of Plant Litter Decomposition, vol. 2. Academic Press, London, pp. 611–632. Wu, J., Joergensen, R.G., Pommerening, B., Chaussod, R., Brookes, P.C., 1990. Measurement of soil microbial biomass C by fumigationextraction—an automated procedure. Soil Biology & Biochemistry 22, 1167–1169.