1 Introduction

Conventional farming has played a significant role in improving food and fiber productivity to meet human consumption demands, but has become excessively dependent on high-yield varieties, intense cropping, increased irrigation, and pesticide and fertilizer use. This in turn places a strain on land resources. Desertification, salinization, soil nutrient depletion, excessive fertilizer use, and biocide residues in food have become prominent issues in China. The problems arising from conventional agricultural management have led to the development and promotion of organic farming practices that address environmental and public health concerns (Melero et al. 2006). Organic farming has been proposed as a valid alternative to conventional practices with regard to the improvement of soil fertility and quality (Stockdale et al. 2006).

Organic farming is gaining worldwide acceptance and has been expanding at an annual rate of 20% in the last decade, accounting for over 32.2 million hectares worldwide (Willer and Lukas 2009). In China, 300,000 ha are currently farmed organically or are in the process of conversion. Significantly, this figure is second in the world. In Eastern China (e.g., Shanghai), organic vegetable production has been intensified over the last few years to meet market demands. Organic vegetable production avoids the application of chemical fertilizers and pesticides, relies on organic input and recycling for nutrient supplies, and emphasizes cropping system designs and biological processes for pest management, as defined by world organic farming regulations. These methods may reduce certain negative effects attributed to conventional farming and provide potential benefits in enhancing soil fertility and quality (Araujo et al. 2008; Mäder et al. 2002).

An understanding of microbial processes is important in the management of farming systems, particularly those that rely on organic materials for nutrient input. Soil organic matter is transformed through the activity of soil microorganisms and enzymes (Melero et al. 2006). Microbial and enzymatic activities are influenced by various factors including physical and chemical conditions, primarily temperature and water content in the case of microorganisms (Griffiths et al. 2006).

Soil microorganisms play an essential role in the environment through their role in cycling mineral compounds and decomposing organic material. Microbial biomass, the living component of soil, functions as a transient nutrient sink responsible for releasing nutrients from organic matter for use by plants. It also acts as a small but labile reservoir of nutrients that contributes to maintaining long-term agricultural sustainability. Microbial biomass, rather than total organic C, has been suggested as a useful and more sensitive measure of change in organic matter status. Changes in microbial biomass C can provide an early indication of short-term trends in total organic C (Bergstrom et al. 1998). Soil respiration (calculated as carbon dioxide evolution or molecular oxygen consumption) is one of the most frequently used parameters for quantifying microbial activity in soil (Anderson and Domsch 1990). Soil enzyme production resulting from microbial metabolism is a sensitive indicator of microbial activity. Extracellular soil enzyme activity is indirectly regulated through increased production and secretion by microbes (Aon and Colaneri 2001) and directly by changes in physicochemical conditions (Sinsabaugh 1994). Long-term studies have demonstrated that soil enzyme activities enable the identification of various soil management practices, such as fertilization by means of animal manure or green manures/crop residues (Martens et al. 1992) and municipal refuse amendment (Perucci 1992), as well as tillage treatments (Gupta and Germida 1988). The response of soil enzyme activities to specific soil practices has been used to compare agricultural systems (combinations of soil practices) such as organic versus conventional farming (Melero et al. 2006). In the present study, we have focused on the activities of three enzymes critical to carbon (C), nitrogen (N), and phosphate (P) cycling in soils: sucrase, urease, and phosphatase. Sucrase is a common soil enzyme important in the degradation of sucrose and is involved in the direct metabolism of soil organic matter as well as playing an important role in the enhancement of soil soluble nutrients; urease is closely linked to N mineralization potential because it is required in converting urea into a form usable by plants. Soil phosphatase plays an important role in the P nutrition of plants by mediating the release of inorganic phosphorus from organically bound phosphorus (Guo et al. 2009).

Microbial activity can provide an indication of soil fertility and quality (Dumontet et al. 2001), highlighting the importance of soil management in enhancing agricultural use (Kushwaha et al. 2001). Radioisotopes, chemical measurements, and microelectrodes are frequently used to determine microbial activity. Information concerning changes in microbial biomass is valuable in studies of soil microbial activity because it not only provides a measure of slower, less easily detectable changes in soil organic matter but also provides a description of the important pool of labile plant nutrients (Zheng et al. 2007). Due to the limitations of traditional microbiological methods, the application of microcalorimetric techniques to measuring soil microbial activity has drawn increasing attention (Plante et al. 2009). Microcalorimetry is a highly sensitive method used to assess the overall biomass and activity of soil microorganisms. Power–time curves enable the study of biology at the molecular and cellular levels by providing substantial kinetic information such as microbial growth rate constant, heat evolution process, and heat yield of microbial growth (Zheng et al. 2007). Continuous recording of the signal permits repeated measurements of the same sample, a procedure not possible with other methods (Critter et al. 2004). Microcalorimetry enables the study of soil organism activity and behavior in a nonintrusive environment, providing the following characteristics: (1) It allows growth studies in heterogeneous media; (2) the method is performed under isothermal conditions; (3) the results are independent of the type of organism; (4) the method measures the thermal effect of the total activity in the soil; (5) it permits direct measurements without damaging the samples; and (6) enables a continuous record of the experiment (Wadsö 1999; Silvana et al. 2001).

Understanding microbial changes in organic and conventional farming systems may offer guidance in designing optimal farming system strategies, minimizing yield losses, and protecting the environment (Araujo et al. 2008). Knowledge of microbial processes in arable soils requires a substantial number of measurements under different conditions, a large number of samples, and accurate measurement methods (Turner et al. 2001). Considering the above aspects, the objectives of our work were to investigate the influence of organic horticultural production on soil microbial biomass, activity, and soil enzyme activities as compared to conventional production systems in Shanghai, Eastern China. We also determined various soil chemical and biochemical properties and established whether relationships exist between these parameters.

2 Materials and methods

2.1 Location and management

The study was conducted at two sites belonging to Beigang Horticultural Farms in Fengxian, Shanghai, Eastern China, during March 2009 (Fig. 1). The sites were located approximately 2.5 km from the Yangtze River and the East China Sea, and the elevation was 4 m above sea level. The climate is humid subtropical (Cfa according to the Köppen scheme), with 70% of the annual precipitation (1,255 mm) occurring between May and September. The mean annual air temperature is 17.5°C and the average total annual sunshine is 1,778 h. The soil was a fluvisol developing toward a cambisol (according to FAO 1998) containing approximately 9.3% sand, 70.7% silt, and 20% clay to a soil depth of 40 cm.

Fig. 1
figure 1

Location of sampling sites: QCO Qingqun Village (open field), QCP Qingqun Village (plastic greenhouse, TZO Tao-zhai Village (open field), TZP Tao-zhai Village (plastic greenhouse). Management systems evaluated were: 1 soil samples from QCO2, QCO1, and QCO4 under ORG-OP, 2 soil samples from TZO under CNV-OP, 3 soil samples from QCP1, QCP5, and QCP7 under ORG-GR, and 4 soil samples from TZP under CNV-GR

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The fields QCP1, QCP5, QCP7, QCO2, QCO1, and QCO4 located in Qingcun county (30°56′ N, 121°35′ E) were farmed using an organic approach. All of the fields were near to each other within a continuous area of approximately 4 ha. They had been conventionally cultivated for several years before being converted to organic farming in 2005. The organic horticultural methods used in this study were accredited by the Organic Farming Development Center of China and thus had used no chemical fertilizers, pesticides, or genetically modified organisms for at least 3 years. Reducing conditions were more pronounced in the organic portion of the trial as the irrigation technique of the farm resulted in permanent water table depths between 0.4 and 0.7 m below the soil surface. The fields TZP and TZO located in Taozhai county (30°57′ N, 121°33′ E) were cultivated using a conventional farming approach. These fields had been conventionally cultivated for more than 6 years. The conventional fields were only occasionally irrigated. Farmers completed a questionnaire concerning farm practices for the last 3 years such as (cover) crops, amount and type of animal and green manure and/or fertilizer used, pesticides, disinfectants, mechanical weeding, soil improvements, and plowing depth.

Greenhouse cultivation in plastic tunnels was conducted at QCP1, QCP5, QCP7 (organic management in plastic tunnel fields, ORG-GR), and TZP conventional management in greenhouse fields (CON-GR), and crops were cultivated in open fields at QCO2, QCO1, QCO4 (organic management in open fields, ORG-OP), and TZO conventional management in plastic tunnel fields (CON-OP).

Further details on the major crops grown and fertilizers used in the various fields are summarized in Table 1. The characteristics of the pig manure and organic fertilizer (ProtexPlus®, a commercial product) are described in Table 2.

Table 1 Characteristics of land management practices investigated in this study
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Table 2 Chemical characteristics of pig manure and organic fertilizer (ProtexPlus®) added to organically managed fields
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2.2 Sampling and soil analysis

The soils were sampled during final harvesting to avoid the effects of direct fertilization. Using a stainless steel corer (5.5-cm diameter), soil samples were collected from the surface horizon (0–10 cm) of each field at 10–15 randomly selected positions over an area of 300 m2 within the vegetable planting rows. The soils were mixed to obtain three composite samples per field. Soil samples were immediately transported in gas-permeable plastic bags placed in ice-filled containers. Upon arrival in the laboratory, each sample was thoroughly mixed and sieved through a 2-mm mesh to remove plant matter and earthworms. Half of the fresh samples were stored at 4°C until analysis; the remainder were dried in a thin layer for 2 days at 30°C and stored at room temperature in dark plastic bags.

Soil pH and electrical conductivity (EC) were determined in a 1:2.5 soil/water suspension. Total organic C (TOC) was determined using dichromate oxidation (Nelson and Sommers 1982). Total N was measured using dry combustion in a C/N (Vario MAX C/N, Germany) auto-analyzer. Olsen-P was extracted in a pH 8.5 solution of 0.5 mol dm−3 sodium bicarbonate (NaHCO3, Colwell 1963) and determined using ultraviolet spectrophotometry (UV-8500). The exchangeable cation (K+, Ca2+, Mg2+, and Na+) concentration and cation exchange capacity (CEC) were measured using titration (Rhoades 1982).

2.3 Laboratory sample preparation

The moisture content of the samples was determined by weighing to 0.1 or 1.0 g using a double-scale balance and heating to constant weight in a natural convection oven at 105°C. The moisture content was obtained from the weight loss of the sample after drying (Lamprecht 1999).

All samples were passed through a 2-mm sieve, thoroughly mixed, placed in hermetically closed polyethylene bags, and stored at 4°C for at least 1 month to ensure calorimetric measurement reproducibility (Liu Xiao-Mei et al. 2009).

2.4 Microcalorimetric measurements

Calorimetric experiments were performed using a thermal activity monitor air multichannel thermal activity microcalorimeter (Thermometric 3114/3236, Sweden; Wadso 2002). Measurements were carried out in 5-cm3 stainless steel ampoules that had been cleaned and sterilized in an oven at 120°C for 2 h before use. The soil samples were incubated at 28°C for 24 h prior to the experiments. A 1.0-g soil sample at water holding capacity was placed into the ampoule and mixed with 0.4 cm3 of sterilized solution containing 1.5 mg of glucose and 1.5 mg of ammonium sulfate. The ampoules were hermetically sealed using Teflon discs in order to control evaporation and energy loss. Glucose and ammonium sulfate were provided as C and N sources to stimulate soil microbial activity. The reference ampoule was filled with 1.0 cm3 of sterilized distilled water. The results obtained using water agreed reasonably well with those obtained using a reference soil (Lisardo et al. 2006).

All experiments were performed in triplicate. The calorimeter and isothermal box were maintained at 300.15 K. The power–time curve was continuously monitored and recorded using a computer.

2.5 Microbiological analysis

Microbial biomass C (C mic) and N (N mic) were measured using a fumigation and extraction technique (Brookes et al. 1985). Briefly, samples of moist soil (20 g oven-dried) were fumigated by exposure to alcohol-free CHCl3 vapor for 24 h in a vacuum desiccator. CHCl3 was removed under vacuum and the samples were extracted with 80 cm3 of 0.5 mol dm−3 K2SO4 by shaking for 30 min on a reciprocating shaker (Unimax, 2010; Heidolph Elekrto Gmbh, Kelheim, Germany) at a speed of 250 rpm. The suspension was filtered using Whatman no. 42 filter paper. Equivalent portions of non-fumigated soil were directly extracted. Organic C in the extract was analyzed using an automated TOC analyzer (Phoenix-8000). C mic was calculated by subtracting the amount of organic C extracted from the non-fumigated soil from that extracted from the fumigated soil using a conversion coefficient of 0.45 (Wu et al. 1990). Total N in the extracted sample was analyzed using a FIAStar5000 after the addition of 0.19 mol dm−3 CuSO4 and oil of vitriol. N mic was calculated by subtracting the total N extracted from the non-fumigated soil from that extracted from the fumigated soil using a conversion coefficient of 0.45 (Brookes et al. 1985; Jenkinson 1988).

Soil respiration was determined from CO2 evolution according to Alef (1995). Soil samples (50 g) were placed in 100-cm3 beakers which were in turn placed in 1-dm3 glass containers closed with rubber stoppers. The soil was moistened to 45% of the maximum water holding capacity and incubated for 7 days at 25°C. Simultaneously, glass vials holding 20 cm3 of 1 mol dm−3 NaOH to trap the evolved CO2 were placed in each glass container. On day 7 after incubation, the glass vials were removed and the CO2 trapped in the NaOH was immediately determined using an automated TOC analyzer (Phoenix-8000).

Enzyme activities were assessed using air-dried soil samples. Sucrase activity was measured using the Hoffmann–Seegerer method (Zhou 1988). In a 100-cm3 flask, 10-g soil was combined with 10 cm3 20% sucrose solution in pH 5.5 buffer and shaken vigorously at 37°C for 24 h. The sucrose content was measured using a starch indicator following the addition of 0.2 mol dm−3 Na2SO4. Reagent blanks were produced for each sample by adding toluene to identical samples prior to reagent addition to inhibit sucrase activity. The difference in sucrose content between the reagent blanks and the samples was used to calculate the sucrase activity [cm3 (10 g soil)−1 (24 h)−1]. Alkaline phosphatase activity was determined by measuring p-nitrophenol released when the soil was incubated with buffered (pH 7.0) sodium p-nitrophenyl phosphate solution and toluene at 37°C for 24 h. The procedure was derived from the method proposed by Tabatabai (1982). The urease activity in the soil was determined using a modification of the method proposed by Kandeler and Gerber (1988). In this procedure, 10 cm3 of urea solution (10%) and 20 cm3 of citrate buffer (pH 6.7) were added to 2 g of air-dried soil. The mixture was then incubated for 24 h at 37°C. The ammonium content was determined using a modified indophenol blue reaction. Controls were prepared without substrate to determine the ammonium ion production in the absence of added urea.

2.6 Statistical analyses

All measurements were made in triplicate and reported as mean values with standard error. Statistical analyses were carried out using SPSS 10.5 (SPSS Inc., Chicago, IL, USA). For multiple comparisons, it was assumed that the data were from two independent groups (organic and conventional). Normality within each group was tested using residuals (Anscombe and Tukey 1963) according to Shapiro and Wilk (1965). Homogeneity of variance was assessed using Levene’s test on squared residuals (Levene 1960). If one of the assumptions was violated, the data were first transformed to conform to the assumptions before multiple comparison tests were performed. Protected Duncan’s tests (Fisher 1935) were used for means comparison between any sampling dates (Einot and Gabriel 1975). For comparisons over time within one season, the MIXED procedure was used. According to Akaike’s information criterion, the spatial power correlation structure (which accounts for unequal spacing in time) most often provided the best fit to the data (Moser 2004). Paired t tests were employed as an exception on comparable treatments (plastic tunnel fields versus (GR) open fields (OP)) within one sampling date as a cautious approach to determining possible differences between systems. A MIXED procedure split-plot model (Steel and Torrie 1980) was used to evaluate comparable treatments between sites. Linear regression analysis was used to identify relationships between soil biological and microbial parameters.

3 Results

3.1 Soil chemical properties

Farm management practice significantly affected soil chemical properties (Tables 3 and 7). The highest pH (8.38) occurred in conventionally managed plots in open fields, followed by conventional management in plastic tunnel fields (CNV-GR, 8.15), organic management in open fields (8.03), and ORG-GR (7.49, see Table 3). The soil salinity in terms of EC and exchangeable K+ content was significantly influenced by management practice and generally followed the series ORG-GR>CNV-GR>ORG-OP>CNV-OP (p < 0.001, see Table 7). Similarly, the highest levels of soil exchangeable Ca (17.07 cmol kg−1), Mg (6.53 cmol kg−1), Na (2.09 cmol kg−1), Olsen-P (330.7 ± 6.53 mg kg−1), cation exchange capacity (20.36 cmol kg−1), total organic C (25.79 mg kg−1), and total N (3.30 mg kg−1) were observed in organically managed plots in plastic tunnel fields and generally followed the order ORG-GR> ORG-OP> CNV-GR>CNV-OP (see Tables 3 and 7).

Table 3 Soil chemical properties (0–10 cm) under different management systems
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3.2 Soil respiration and microbial biomass

The highest soil respiration and microbial biomass C (C min) and N (N mic) values occurred in organically managed plots in plastic tunnel fields. Significant differences (p < 0.001, see Table 7) between the organic and conventional management systems were observed, indicating higher soil microbial activity and greater supply of available C (Table 4). The soil respiration values in open fields were higher than in plastic tunnel fields under conventional management.

Table 4 Soil microbial biomass C (C mic), microbial biomass N (N mic), and soil respiration under different management systems at the 0- to 10-cm depth
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3.3 Soil enzyme activities

Sucrase activity was highest in CNV-GR soil and lowest in ORG-OP and conventional management in open fields (CNV-OP) soils. No significant difference was observed between ORG-OP and CNV-OP soils (Fig. 2). The alkaline phosphatase and urease activities were significantly different and followed the series ORG-GR>ORG-OP>CNV-GR>CNV-OP for alkaline phosphatase activity and ORG-GR>ORG-OP>CNV-OP>CNV-GR for urease activity. No significant differences were observed between CNV-GR and CNV-OP (see Fig. 2 and Table 7).

Fig. 2
figure 2

Soil sucrase, alkaline phosphatase, and urease activities of soils. Bars represent standard errors and letters indicate significant differences by Duncan’s multiple range test at p < 0.05

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3.4 Microbial activity measurements using microcalorimetry

The results obtained from calorimetric experiments on three soil samples are contained in Fig. 3 and Table 5. The power–time curves indicate that microbial activity evolved over time. The total heat (Q total, J g−1) and the microbial growth rate constant (k, min−1) were calculated from the curves. The correlation coefficients were all larger than 0.9936, indicating good reproducibility and correlation (see Table 5).

Fig. 3
figure 3

Power–time curves recorded microcalorimetrically from samples amended with glucose and ammonium sulfate. In these curves, thermal power (μw) is plotted against time (min). Integration of these curves provides values of the total heat evolved by the process. The evolution of peak height (P max) is the power at the maximum of the peak, and peak time (t max) is the time spent to reach the maximum of the peak

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Table 5 Microcalorimetric parameters from power–time curves
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Exponential cell growth occurred during the log phase of the curve. If the heat output power was P 1 at time 1 and P t at time t, then

$$ {P_t}{ } = { }{P_1}{ \exp }\left( {kt} \right){\hbox{ or ln }}{P_t} = { \ln }{P_1} + { }kt{.} $$

The recorded power–time curves described typical microbial metabolic activity. In ORG-GR and CON-GR, the heat flow increased exponentially after a lag phase, which was followed by stationary and decline phases. However, the thermal activity curves of ORG-OP and CNV-OP were markedly different from the other management systems, exhibiting complex curves with two peaks smaller than the single peak of the other soil samples. These samples also displayed faster initial growth (see Fig. 3).

There were no significant differences in peak heat evolution values P max (μw) between ORG-OP and CNV-GR samples, but differences were observed between ORG-GR and CNV-OP (see Table 5). The highest P max value occurred in ORG-GR samples, and the lowest was observed in CNV-GR and ORG-OP samples. The time t max required to reach the peak heat evolution exhibited the opposite trend and generally followed the series ORG-OP> CNV-GR>ORG-OP>ORG-GR (see Table 5 and Fig. 3).

The microbial growth rate constants (k) during the log or exponential phase of microbial activity, Q total, and the total heat released by microorganisms in the soil are presented in Table 5 and Fig. 3. ANOVA revealed that management practice had a significant impact (p < 0.05) on the values of k and Q total and generally followed the series ORG-GR>ORG-OP>CNV-GR>ORG-OP.

3.5 Correlations between microcalorimetric parameters and soil chemical and microbial properties

Correlation coefficients (with their level of significance) between microcalorimetric parameters and chemical and microbial properties are presented in Table 6 and Fig. 4. There was a strong positive correlation between P max, Q total, and k values (p < 0.01) and chemical and microbial properties (TOC, total N, alkaline phosphatase and urease activities, C mic, N mic, and soil respiration) other than sucrase activity. However, a significant (p < 0.05) negative correlation was observed between t max and the chemical and microbial properties (TOC, total N, alkaline phosphatase and urease activities, C mic, N mic, and soil respiration) other than sucrase activity. For all soils, there was a significant positive linear correlation between microbial biomass (C mic and N mic) and k value (see Fig. 4).

Table 6 Spearman rank correlation coefficients between microcalorimetric parameters (P max, t max, Q total, and k) and soil chemical and microbial properties (TOC, total N, enzyme activity, C mic, N mic, and soil respiration) in soils under different management systems
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Fig. 4
figure 4

Correlation between microbial biomass (C mic and N mic) and growth rate (k); in this graph, a higher correlation between k and microbial biomass (C mic and N mic) was observed for the soil samples under different management practices

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4 Discussion

The lower pH values observed in organically farmed soil samples (ORG-OP and ORG-GR) were similar to the findings of Melero et al. (2006). The decrease of soil pH in the organic management system may be due to higher soil microbial activity. The EC was higher when the soil was managed organically (Table 7).

Table 7 P values for three-way ANOVA of the effects of organic versus conventional management, plastic field versus open field, and site
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The differences in available P and Ca between the organic (ORG-GR and ORG-OP) and conventional management systems (CNV-GR and CNV-OP) suggest that the addition of organic matter to calcareous soils can increase available P and decrease sparingly soluble phosphates (Braschi et al. 2003). Furthermore, compost addition contributes to the nutrient content of soil (Ca2+, Mg2+), resulting in higher CEC in the organic systems and an increase in soil fertility. A higher TOC content was present in the organic farming samples. Organic management and the use of organic residues have been shown to maintain soil organic matter at higher levels compared with inorganic fertilization (Edmeades 2003). High organic C is important for sustainability because of the influence of organic matter on soil physical, chemical, and biological properties. Soil also benefits from C sequestration due to global environmental changes (Lal 2004).

Organically managed soils exhibited greater biological activity than conventionally managed soils. This agrees with the results of Mäder et al. (2002). The present investigation also provides support to the hypothesis that C min and N min are significantly and rapidly enhanced under organic systems due to their incorporation into organic amendments. C min in organic plots has been reported to be 45–64% higher than in conventional plots with manure amendments (Tu et al. 2006). Microbial biomass (C mic and N mic) is one of the most labile pools of organic matter and serves as an important reservoir of plant nutrients such as N and P (Marumoto et al. 1982). Changes in microbial biomass can therefore have important implications for nutrient bioavailability.

The metabolic efficiency of a microbial community is supposedly reflected in its specific respiration rate (Elsgaard et al. 2010). Higher soil respiration in the organic farming system indicates a higher soil microbial activity caused by permanent and continuous addition of an exogenous source of labile organic matter to the soil and the consequent stimulation of heterotrophic microorganisms (Moeskops et al. 2010). Because diversity of substrate use in organic soils has been found to be higher than in conventional soils (Fliessbach and Mäder 2000), the hypothesis that a more diverse community has higher metabolic efficiency is supported by our data.

A number of long-term studies have suggested that soil enzyme activities are capable of discriminating among soil management practices (García-Ruiz et al. 2009). Despite the relatively low variability of the soil enzyme activities in this investigation, differences between the organic and conventional farming techniques were significant. Urease and alkaline phosphatase activities were higher under organic management. However, this was not always true when paired values for sucrase activity in organic and conventional management were compared (Kremer and Li 2003). Both intracellular and extracellular forms of urease are present in soil. Extracellular urease activity has been associated with clay–organic matter complexes (Kandeler et al. 1999). In addition, because enzymatic action plays a role in nutrient cycling, the correlation between soil chemical properties and enzyme activities (see Table 6) could be the result of improved soil chemical and physical properties after treatment with organic amendments.

An important limitation of this study is that we obtained samples only at the end of the crop growing season and consequently could not identify possible seasonal trends (March 2009). Previous studies examining organic and conventional olive oil farming soils reported that soil enzymes, nematode population, and other physicochemical properties display distinct seasonal variations (Roberto et al. 2009).

Based on our results, microbial biomass, activity, and chemical parameters were affected by the choice of agricultural management system. Organic farming supports soil quality because (1) microorganisms use available resources more economically (for growth rather than maintenance) and (2) higher microbial biomass indicates better conditions within the soil organic matter, which may contribute to nutrient mineralization and temporary storage of potentially leachable elements (Fliessbach and Mäder 2000).

All metabolic processes occurring within living cells produce heat; thus, metabolic processes can be studied by monitoring changes in heat flux with a sufficiently sensitive microcalorimeter. Microcalorimetry can directly measure the biological activity of a living system and provides continuous information concerning heat production, thereby providing considerable qualitative and quantitative information (Critter et al. 2004; Yao et al. 2003). Power–time curves were recorded for samples representing different management practices in which great variability in microbial activity can be observed (see Fig. 3). The calorimetric information provides considerable assistance in understanding features related to the effects of management practices on soil. The calorimetric curves resulting from soil microbial activity can display a single large thermal effect, various minor initial thermal effects, or a rapid increase followed by a slow decrease. The area under the curve (Q total) describing the thermal power as a function of time (s) reveals the intensity and time necessary for the development of microbial activity within a specific soil system. As a result, the area under the heat flux peak is a good indicator of microbial activity.

In general, a positive correlation exists between TOC, microbial biomass and activity, and P max, Q total, and k values (see Table 6 and Fig. 4). Diversity of microorganisms is a key parameter of soil structure, fertility, and microbial metabolism. The metabolic activity of soil microorganisms depends on the quality and nature of the organic matter rather than the quantity (Diaz-Ravina and Carballas 1988). The diversity and structure of soil microbe populations was not investigated in our present study. However, in the future, the relationship between microcalorimetric parameters and diversity should be studied in soils farmed using organic and conventional management practices.

5 Conclusions

Very few studies have examined the impact of farming management on the microbial properties of tropical soils. The extreme differences between organic and conventional management practices were reflected in strong differences in microbial biomass and enzyme activities. Organic residues added to the soil (organic production systems) promoted increased microbial biomass and enzyme activities, which led to improved chemical properties, higher nutrient availability, and enhanced fertility. Further work is clearly required to characterize the significant long-term ecological effects (such as carbon sequestration, microbial structure and functional diversity, productivity, etc.) of organic agricultural systems.