Microbial soil quality indicators under different crop rotations and tillage management 1 Indicadores microbianos da qualidade do solo sob diferentes rotações de culturas e manejo do solo

An experiment was carried out under field conditions to assess the effects of soil management (no-tillageNT and conventional tillageCT) and crop rotation systems on microbial biomass-C (C mic ), basal soil respiration (BSR), metabolic quotient (qCO 2 ), soil organic carbon content (C org ) and microbial carbon to organic carbon ratio (C mic /C org ). Soil samples were collected on an area cultivated with wheat as winter crop and soybean as summer crop, both in rotation with vetch, maize and oats. Samples were also collected in a secondary forest used as reference. Data of each management system (NT and CT) were compared to forest area by “t” test (p<0.05) and crop rotations were compared by Tukey test (p<0.05). All data were submitted to multivariate analysis (Principal Component Analysis PCA). There were observed significant differences (“t” test; p<0.05) for C mic , BSR, qCO 2 and C mic /C org between NT and CT, by which NT values resemble those for forest area. For crop rotations significant differences (Tukey test; p<0.05) were found only for BSR and qCO 2 . The sum of the two first principal components on the PCA explained about 75% of the data variation. PCA showed NT closest to forest area than CT, especially treatments with soybean and vetch as consecutive crops. The forest area-NT clustering was mostly due to C mic and C mic /C org relationship. Results indicate that the NT system is more sustainable than the CT system and can contribute for the accumulation a greater quantity of carbon in soil.


Introduction
The use of conventional system combined with excessive use of fertilizers and pesticides leads to loss in health and soil quality.Besides, depletion of soil organic carbon and associated nutrients in soil organic matter and intensive tillage causes severe negative impacts to the environment, by subjecting bare soil to erosive processes.Soil degradation and contamination of underground water, may also occur from residues of pesticides and fertilizers widely employed under this agricultural model.Oldeman (1994) has pointed out that this model of soil exploitation results in environmental degradation that goes far beyond its simple ecological aspects, which leads to irreversible environmental damage with destruction of fauna and flora and loss of agricultural soil (LEONARDOS et al., 2000), and greenhouse gases emission (ELDER;LAL, 2008).
According to Batjes (1998), the global average of C liberation to the atmosphere in the 1980s was 7.1 ± 1.1 Gt C year -1 , from which, 1.6 ± 1.0 Gt C year -1 was due to agriculture in tropical regions and 5.5 ± 0.5 Gt year -1 due to the burning of fossil fuels.Similar amounts were described for the 1990s where the total emission was estimated to be 7.0 Gt year -1 of C, from which, 5.5 Gt year -1 was due to fossil fuel burning and 1.2 Gt year -1 was due to agricultural soil use and deforestation (International..., 1996cited by RESCK, 2001).
Joint scientific efforts have been focused on the development of systems capable of maintaining high yields while promoting greater sustainability to the agroecosystem through the use of alternative soil management practices, improved varieties and crop rotations.However, irrespectively of the adopted system, soil management will always result in changes in some soil characteristics.These variations have an important effect on the soil microbial community (GUNDALE et al., 2005) which in turn affects agroecosystem function and consequently crop yield.
Besides the influence of soil management on total microbial biomass, it also affects differentially the biomass activity resulting in greater or smaller CO 2 efflux.Nowadays the trend is towards soil management practices capable of increasing the immobilization of atmospheric CO 2 .According to Hermle et al. (2008), it may be possible to manipulate paddy soil through conservational tillage and crop practices, and thereby maintain adequate soil organic matter concentrations, and mitigate soil organic carbon loss from soil to atmosphere, which increases C content and microbial activity (FRANCHINI et al., 2007;MOSCATELLI et al., 2005;ROLDÁN et al., 2005).
Soil microbial communities are influenced by many factors as soil management and cover crops (CARRERA et al., 2007); kind of fertilizer and its applying way (CARRERA et al., 2007); plant development stage and cultivars (FERREIRA et al., 2008) as well as pesticides (FERREIRA et al., 2009).Thus, microbial properties allied to the total organic C content can be used to evaluate the sustainability of agricultural production.These properties are described as biological indicators capable of detecting changes in soil quality and its biological properties (NOGUEIRA et al., 2006).High contents of organic C on the soil surface is an important factor contributing to the C mic content in soil (VARGAS; SCHOLLES, 2000) which has been reported as a key factor for microbial community growth (MOSCATELLI et al., 2005).
This work aimed to determine the effects of soil management and crop rotation systems on soil microbial biomass and organic carbon and to verify the use of these parameters as indicators of soil quality.

Material and methods
This study was carried out in a long-term experiment conducted since 1996 at the Experimental Station at the National Wheat Research Center (Embrapa) in Passo Fundo, Rio Grande do Sul State,Brazil (28º12'56" S and 52º23'43" W,altitude 684m).The soil at the site is an Haplic ferralsol (10 g kg -1 of soil of coarse sand; 230 g kg -1 of soil of fine sand; 130 g kg -1 of soil of silt; 630 g kg -1 of soil of clay).
Crop rotations were evaluated in 2 management systems: no-tillage (NT) and conventional tillage (CT) where a disc plough was used.Crop rotations consisted of wheat (Triticum aestivum L.) and soybean (Glycine max (L.) Merrill) as the main crops for winter and summer, respectively, and vetch (Vicia sativa L.), maize (Zea mays L.) and oats (Avena sativa L.) as secondary crops for the rotation system.Three different rotation were used: 1-wheat/soybean (W/S); 2-wheat/soybean/vetch/maize (W/S/V/M); 3-wheat/soybean/vetch/maize/oats/soybean (W/S/V/M/O/S), in a way that all possible combinations had been cultivated at the same cropping season, resulting in 6 subplots per management system for each replicate.The experiment was carried out in a split-plot randomized block design with 3 replicates.The management systems (NT and CT) were the main plots and the subplots were the crop rotation treatments.
Composite samples consisting of 6 sub-samples per plot were taken at 0-10 cm depth.Soil sampling was also carried out in a neighboring secondary forest area (Araucaria woodland).
Microbial biomass-C (C mic ) and basal soil respiration (BSR) were performed by the use of three 20 g Microbial soil quality indicators under different crop rotations and tillage management sub-samples derived from composite soil samples at 60% of field capacity.They were then homogenized and sieved through a 2 mm sieve.Microbial biomass-C (C mic ) evaluation was carried out by fumigation-extraction method (VANCE et al., 1987) using soil:extract solution ratio of 1:2.5 and a correction factor (kc) of 0.33 (TATE et al,. 1988).For the fumigation step 1 mL of ethanol-free chloroform was added to each sub-sample flask of 100 mL, kept tight closed, and after incubation of 24 hour the chloroform was allowed to evaporate in the fume hood for 60 min.After that, the extraction and titration were performed according to Vance et al. (1987).Basal soil respiration (BSR) was determined according to Jenkinson and Powlson (1976) where three 20 g sub-samples of soil were placed in a 3 L flask and incubated for 5 days at 28 o C. CO 2 was trapped in a 100 mL flask containing 10 mL of NaOH (1 M).After this period, 2 mL of BaCl 2 (10%) was used to precipitate the CO 2 and the excess NaOH was titrated using HCl (0.5 M).
The specific microbial respiration, or metabolic quotient (qCO 2 ), expresses the C-CO 2 e v olved per unit of microbial biomass and time.Metabolic quotient was calculated according to the procedures described by Anderson and Domsch (1990) and expressed as mg C g C mic -1 h -1 .
Organic carbon content (%C org ) was determined by potassium dichromate oxidation.The ratio of microbial biomass-C to organic carbon (C mic /C org ) was calculated according to Jenkinson and Ladd (1981).
Data were submitted to analysis of variance and significant differences were determined by Tukey's test at p<0.05.To compare data from NT, CT and the forest area, a 't' test was used at p<0.05 and values found for forest area were used as a reference.Previously to variance analysis, data were submitted to Lilliefors and CoChran-Bartlett tests to verify its normality and homogeneity of variances using the software SAEG v. 7 (EUCLYDES, 1982).Comparisons between forest area and both NT and CT treatments should be done with care as the forest area was not part of the experimental design.A principal component analysis (PCA) was performed using the software CANOCO v. 4.5 (ter BRAAK;SMILAUER, 2002), for data obtained for all the parameters studied involving a matrix of 13 lines by 5 columns, representing treatments and parameters, respectively.The determination of the number of principal components used for the treatments clustering was defined according to Jollife (2002), since the sum of the first 2 principal components (PC1 and PC2) was greater than 70%.1).Fialho et al. (2006) did not found significant difference for C mic between cultivated and forest area, however forest area showed a tendency to present greater values of C mic than cultivated area.

Results and discussion
C mic value for NT was greater than CT and closer to forest area, suggesting that this system could promote a greater sustainability level compared to CT. Furthermore NT increases the litter accumulation rate on the soil surface which improves both the C content and microbial activity (MOSCATELLI et al., 2005;ROLDÁN et al., 2005), indicating a close association between these results and the low impacts to the environment caused by the NT system (HERMLE et al., 2008).
BSR did not show significant difference among soil management systems (NT and CT) and forest area, however NT was statistically different of CT (Table 1).The greatest values of BSR were expected in the treatments with the greatest C mic values, such as NT and forest area, that compared to CT promoted greater CO 2 e f flux.According to Zornoza et al. (2007), BSR shows a close relation to abiotic soil conditions such as temperature and humidity.Besides, the greatest quantity of organic material in the forest area Same letters means no significant differences among treatments in the same column (Tukey's test p<0.05) and asterisk means significant difference between each treatment and forest area ("t" test p<0.05) and NT reflects upon the decomposing microbial activity and, consequently on the BSR growth, as stated by Vargas and Scholles (2000) that found a correlation between the soil warming and substrate availability with BSR.
Significant differences in qCO 2 values were observed to NT system when compared to CT.However, both treatments were not different to forest area (Table 1).Compared to CT, NT showed lowest qCO 2 v a lue and, consequently, greater sustainability because the soil microbial population under NT was less stressed, indicating the lowest relative loss of CO 2 , which, in the long-term can translate into a greater accumulation of C in the soil (FRANCHINI et al., 2007).Besides, a low qCO 2 indicates a high quality of the substrate used by microorganisms or a low microbial maintenance requirement (SARMIENTO; BOTTNER, 2002).In addition, even with the lowest content of C mic , CT can promote greater qCO 2 values due to differences in the substrate accessibility by the microorganisms, metabolic patterns changing or by the alteration of the soil microbial composition (ALVAREZ et al., 1995).According to Dilly et al. (2001), lowest values of qCO 2 are normally found under forest vegetation compared with cultivated areas because forest systems show greater stability and, therefore lower disturbance on the soil microbial community.
C org content under NT and CT showed a significant difference compared with the forest area, however, under the present study conditions it was not possible to verify significant difference in C org content between NT and CT management practices (Table 1).This result may be possible because C org assessment was only performed on the soil surface (0-10 cm depth), and differences on C org contents may occur on the whole soil profile, as stated by Sisti et al. (2004) that found significant differences for C stocks through the soil profile under crop rotations in this same experimental area.
Data for C mic /C org r a tio show that there were significant differences only for CT, when the tillage treatments were compared to the forest area.In addition, between management systems the lowest value was found under CT management, indicating that the quantity of C mic as a proportion of the total soil C org is greater under the forest area and NT than under CT.
As reported by Jenkinson and Ladd (1981), the C mic /C org ratio is related to the changes in the quantities of soil C. In both the forest area and NT the greatest values of C mic /C org ratio suggest that a greater quantity of biomass is supported per organic carbon unit derived from the inputs of C in the system. 1 indicate a more evident impact of the CT on the soil quality indicators, which may be resulted as a consequence of the degradation of the natural soil conditions (OLDEMAN, 1994) and/or by a strong impact on the fauna and flora and loss of agricultural soil (LEONARDOS et al., 2000).

Data on Table
Table 2 shows the effects of crop rotations within each management system.Significant differences were found only for BSR and qCO 2 within each crop rotation (Table 2).Under NT BSR was higher for crop rotation IIb, while the highest value for CT was observed in the crop rotation IIIc.In both rotations soybean was included as the summer crop but during the sampling period, vetch, a leguminous plant, was under cultivation.On the other hand, the lowest values of BSR were found in the crop rotations IIIb and I for NT and CT, respectively, when wheat was under cultivation.These data suggests that a succession of legume plants (soybean and vetch), as presented for NT and CT, in the crop rotations IIb and IIIc, respectively , may increase microbial activity represented by the CO 2 -C evolved.Some factors such as C/N ratio (RAUT et al., 2008) andN content (WANG et al., 2008) have a great influence on the decomposition and mineralization of the organic matter.Compared with wheat, the organic residues of vetch had a smaller C/N ratio and a greater N content, which contributed to the faster decomposition of its residues results in a greater CO 2 efflux rate.
The metabolic quotient (qCO 2 ) was lower for the crop rotation IIIb under NT, and for the crop rotation I under CT (Table 2).In both crop rotations soybean was followed by wheat, which possibly had been influenced the low values of BSR observed under t h ese crop rotations.Greater values of qCO 2 were found in the crop rotations IIb and IIIa under NT and IIIc under CT (Table 2).Except for crop rotation IIIa under NT, the other crop rotations had soybean/vetch in succession, and as stated above this situation may had influenced the high values of BSR under these treatments, resulting on high values of qCO 2 .
Compared to data displayed on tables of mean tests, principal component analysis (PCA), provides more details of the interactions between the studied variables once it mitigate the data variation into several components and allows a multidimensional view of the data, which complements the information of the mean tests, helping the interpretation of the data.The difference between the X and Y axes with our data was about 20%, indicating that X axis was the most important for data interpretation because it had been explained most of the data variation.
The PCA was performed with basis on the data of C mic , BSR, qCO 2 , C org and C mic /C org .The first three principal components discriminated 94.9% of all information.However, the sum of the first two principal components Microbial soil quality indicators under different crop rotations and tillage management * I-wheat/soybean; IIa-wheat/soybean/vetch/maize, with wheat as the winter cropping; IIb-wheat/soybean/vetch/maize, with vetch as the winter cropping; IIIa-wheat/soybean/vetch/maize/oats/soybean, with oats as the winter cropping; IIIb-wheat/soybean/vetch/maize/oats/soybean, with wheat as the winter cropping; IIIc-wheat/soybean/vetch/maize/oats/soybean, with vetch as the winter cropping.Same or none letters means no significant differences among treatments in the same column within management system (Tukey's p<0.05) explained 79.2% of the information, with a contribution of 43.1% on PC1 and 36.1% on PC2.The most important parameters for the distribution of the treatments along X axis (PC1) were C mic and the C mic /C org ratio and qCO 2 , while BSR had a greater influence for the distribution along the Y axis (PC2) (Figure 1), which are key characteristics of the multivariate approach of principal component analysis (PCA) on distinguishing areas as a function of the soil management (SENA et al., 2002).
Data from PCA showed a clear separation between CT and NT, in which NT treatments had clustering closer to the forest area than CT, except for NT-IIIa (Figure 1).PC1 shows that treatments under NT are closer to the forest area and they are more dispersed than CT treatments (Figure 1).NT is closer to the forest area because under this management system a high quantity of C mic and C mic / C org were accumulated than under CT (Table 1).Treatments NT-IIb and NT-IIIc both had consecutive crop rotations with the same legume plants in the last 2 cropping seasons (soybean/vetch -Table 2), and high values of C mic and C mic / C org .High concentrations of organic C in soil promote greater C mic a c cumulation (ALVAREZ et al., 1995;VARGAS;SCHOLLES, 2000) which can be used by soil microorganisms for their growth (MOSCATELLI et al., 2005), reflecting the greater concentration of C mic in the soil.This fact could explain the position of both treatments NT-IIb, NT-IIIb and NT-IIIc closest to the forest area (Figure 1).Treatments distributed along PC2, represented by the Y axis, were most influenced by BSRs.Along this axis, treatments SFA and NT-IIb had showed the greatest distance from the CT-I and CT-IIIb (Figure 1), in which the first two treatments showed high values of BRS, while low values were found for CT-I and CT-IIIb treatments.Therefore, crop rotation system could have stimulated microbial activity, resulting in greater BSR without an increase in the C mic content.
Within NT treatments, the rotation NT-IIIa was very distinct from the others (Figure 1).Among NT treatments, this rotation presumably shows the greatest C: N ratio because this system had consecutive crop rotations with cereals in the last 2 cropping seasons (maize/oats -Table 2).Possibly such conditions influenced soil organic matter decomposition, reflecting the greater rates of BSR during the studied period.
The ratio of microbial biomass-C to organic carbon (C mic /C org ), and the metabolic quotient (qCO 2 ) associated with soil microbial activity, have been used by many authors as indices to determine the sustainability of agricultural systems.According to Araújo et al. (2008), the ratio C mic /C org was significantly enhanced by organic management, improving soil microbial characteristics and slowly increasing soil organic C. Moscatelli et al. (2007) verified that soil management alters the values of qCO 2 .Franchini et al. (2007) reported that the decrease in qCO 2 under NT allowed enhancements in soil C stocks, such that in the 0-40 cm profile, a gain of 2500 kg of C ha -1 was observed in relation to CT, evidence of a smaller relative loss of CO 2 and, consequently a larger accumulation of C.

Conclusions
1. Microbial carbon (C mic ), basal soil respiration (BSR), metabolic quotient (qCO 2 ) and microbial carbon/ organic carbon ratio (C mic /C org ) are efficient to determine differences between NT and CT management but only BSR and qCO 2 a r e suitable to identify significant differences among crop rotations systems.
2. C mic , C org and C mic /C org are key indicators on determining the soil quality, which is clearly observed by the close clustering among crop rotations under NT and forest area as compared to CT.
3. The effects of soil management and crop rotations systems on soil quality indicators are only evident upon an integrated analysis of the data by using mean test and principal component analysis.

Figure 1 -
Figure 1 -PCA of the influence of each variety (C mic , BSR, qCO 2 , C org and C mic /C org ) on the distribution of soil management systems (NT: no-tillage; CT: conventional tillage) and crop rotations I, IIa, IIb, IIIa, IIIb and IIIc (Table2) Table 1 shows C mic , BSR, qCO 2 , C org and C mic /C org values obtained for NT, CT and forest area.Mean test between NT and CT was performed by Tukey´s test, and forest area was compared separately with each management systems (NT and CT) by "t" test.Significant differences between NT and CT were observed to C mic , BSR, qCO 2 and C mic /C org .Values of C mic and C org of forest area were greater than NT and CT, and the C mic /C org of forest area was greater than CT but not statistically different of NT (Table