Changes in Microbial Biomass, Activity, Functional Diversity, and Enzyme Activity in Tree Peony (Paeonia suffruticosa) Garden Soils

To understand the effects of tree peony (Paeonia suffruticosa) on soil microbiological and biochemical properties, soil samples were collected from tree peony growing sites with 3 growth years and four tree peony cultivars as well as from an adjacent wasteland in a tree peony garden at Luoyang, Henan Province of China. With the development of the tree peony garden ecosystem, soil microbial biomass carbon (Cmic), basal respiration (Rmic), Cmic as a percent of soil organic C (Cmic/Corg), and enzyme activities first increased and then decreased. For the tree peony cultivars Yao Huang and Dou Lu, Cmic, Rmic, Cmic/Corg, catalase, invertase, cellulose, proteinase, and phosphatase decreased after 5 years of growth, whereas urease decreased after 12 years. For the cultivars Er Qiao and Shou An Hong, catalase, proteinase, and phosphatase decreased after 5 years, whereas Cmic, Rmic, Cmic/Corg, invertase, cellulose, and urease decreased after 12 years. Biolog analysis indicated that the average well color development and microbial functional diversity were significantly greater at the 5-year sites than in the wasteland but decreased significantly as growth continued. The growth duration of tree peony had a greater effect on soil microbial communities than did tree peony cultivar. Tree peony (Paeonia suffruticosa), known as the ‘‘queen of flowers,’’ is one of the most famous horticultural plants and has been cultivated throughout the temperate regions of the world. China is the country of first domestication and the origin center for cultivated tree peonies, and the species continues to be planted throughout the country. To develop tourism and the commercialization of tree peony, planting areas of this species have increased year after year. However, because tree peony grows for multiple years, its growth potential becomes weak and it experiences serious diseases, which are inevitably connected with the soil environment. Plant cover type, planting years, and management are known to significantly impact soil ecological sustainability (Yao et al., 2000). Tree peony gardens have unique cultivation and management styles. To reduce diseases and insect pests and save nutrition for the growth of floral buds and flowers, weak and ill branches, deadwood, and fallen leaves are cut annually from tree peony plants and the litter layer is removed from the garden. Like with many perennial woody flowers, fertilization is a very important management measure for tree peonies. Tree peony garden soils usually receive three applications of fertilizer per year to ensure large and colorful flowers. These management measures inevitably affect soil properties and microbial ecology. Soil biological and biochemical properties have become increasingly recognized as important for assessing the sustainability of ecosystems. In soil ecosystems, soil microorganisms play a crucial role in nutrient cycling, energy flow, and organic matter decomposition (Unger et al., 2013; Yao et al., 2000). Consequently, these microorganisms have been widely recognized as integral components of soil quality. Microbial biomass C (Cmic), basal respiration (Rmic), and enzyme activities have been widely used in soil investigations as a result of their great sensitivity to anthropogenic disturbance and environmental changes (Franco-Otero et al., 2012; Reeve and Drost, 2012; Wells, 2011). Recently, the functional diversity of soil microbial communities has frequently been used to assess soil quality and ecosystem functions (McGuire and Treseder, 2010; Sherman and Steinberger, 2012). The Biolog system, which reflects the oxidative catabolism of substrates to generate patterns of potential sole carbon source use, has been proven more advantageous for assessing the functional diversity of microbial communities in various soil ecosystems than other methods such as phospholipid fatty acid analysis and denaturing gradient gel electrophoresis (Saul-Tcherkas and Steinberger, 2009). Most studies regarding tree peony have focused primarily on the plant itself, examining such issues as germplasm resources, cultivar classification, and tissue culture and micropropagation (Suo et al., 2005, 2008, 2012; Teixeira da Silva et al., 2012). Relatively few studies have focused on the soil environment under tree peony and the influence of tree peony growth on soil properties. Little is currently known about how soil microbiological and biochemical properties change with the growth of tree peony. The objective of our study was to evaluate the changes in soil microbiological and biochemical properties in response to growth duration and cultivars of tree peony. The results of the study will facilitate the assessment of ecological sustainability and definition of appropriate management strategies in tree peony garden ecosystems. Materials and Methods Site description. The study was conducted in central China at the National Peony Garden (lat. 34 42# N, long. 112 23# E) located in Luoyang, Henan Province, which is famous for its Luoyang peony. The area is characterized by a temperate monsoon climate with a mean annual temperature of 14.9 C and mean annual rainfall of 530 to 600 mm. The soils of this area are cinnamon soils derived from carbonatite. Twelve tree peony growing sites with 3 growth years and four cultivars were selected to assess the responses of soil chemical, microbiological, and biochemical properties to growth duration and cultivars of tree peony. The four tree peony cultivars were Yao Huang, Dou Lu, Er Qiao, and Shou An Hong, and they were planted on the wasteland in 1986, 1999, and 2006. Therefore, the plants had been growing for 25, 12, and 5 years, respectively, when the soil samples were taken. Each site consisted of several plots separated by a footpath. All sites received annual applications of nitrogen– phosphorus–potassium compound fertilizer (15N–6.6P–12.4K) three times per year, averaging 3000 kg·ha per year. A neighboring wasteland covered with sparse grasses was chosen as a control site. All of the sites had similar ecological conditions with the same soil type and topography. Received for publication 17 Mar. 2014. Accepted for publication 2 Aug. 2014. This work was supported by the National Natural Science Foundation of China (No. 41101222) and the Project of Science and Technology Department (No. 112300410139, 132102210225) and Education Department (No. 2011A210024, 13A610788) of Henan Province. The authors greatly appreciate the support provided by National Peony Garden of Luoyang city in China. To whom reprint requests should be addressed; e-mail [email protected]. 1408 HORTSCIENCE VOL. 49(11) NOVEMBER 2014 Sample collection and preparation. Soil samples were collected using a soil sampler from three sampling plots randomly chosen within each study site. Twenty cores (5 cm diameter · 20 cm length) were taken from each sampling plot and mixed. Field-moist soils were sieved through a 2-mm mesh to remove plant debris and soil fauna. One portion of soil was stored at 4 C for microbiological and biochemical analyses, and the other portion was air-dried for chemical analysis. Chemical analysis. Soil pH was measured using a combination glass electrode with the ratio of soil and distilled water at 1:2.5. Total nitrogen was determined using Kjeldahl digestion (Keeney and Nelson, 1982) and quantified using a continuous flow analyzer (Skalar, Delft, The Netherlands), and total organic C was determined by dichromate oxidation (Nelson and Sommers, 1982). Total P concentration was determined colorimetrically at 440 nm using the molybdate procedure (Murphy and Riley, 1962) (Table 1). Microbial biomass and basal respiration. Cmic was determined using the chloroform fumigation–extraction method (Vance et al., 1987). The K2SO4-extracted C of both fumigated and unfumigated samples was analyzed using a total organic C analyzer (Shimazu, TOC-500, Chiba, Japan), and a KEC value of 0.45 was used to convert the measured flush of C to Cmic (Yao et al., 2000). Rmic was determined by measuring CO2 evolution. A 20-g sample (oven-dry basis) of field-moist soil was incubated in an airtight 250-mL glass vessel at 25 C for 1 d. The CO2 produced from the soil was absorbed in NaOH and determined by titration with HCl. The metabolic quotient (qCO2) was defined as the ratio of Rmic to Cmic, i.e., the amount of CO2-C produced per unit of Cmic (Anderson and Domsch, 1986). Enzyme activities. Soil enzyme activities were determined using the methods described by Guan (1986). Catalase activity was determined by back-titrating residual H2O2 with a standard solution of 0.1 M KMnO4 in the presence of H2SO4. Invertase activity was determined with the 3,5-dinitrosalicylic acid colorimetry method using sucrose as the substrate, and the amount of glucose released was assayed colorimetrically at 508 nm. Cellulase activity was measured using carboxymethyl cellulose as the substrate, and the amount of glucose released was assayed with 3,5-dinitrosalicylic acid colorimetry at 540 nm. Urease activity was determined using urea as the substrate, and the released ammonium was assayed colorimetrically using the indophenol blue method at 578 nm. Proteinase activity was measured colorimetrically using casein as the substrate with the ninhydrin method at 500 nm. Phosphatase activity was analyzed using disodium phenyl phosphate as the substrate, and the amount of phenol released was assayed colorimetrically at 660 nm. Community-level substrate use analysis. Biolog EcoPlates (Biolog Inc., Hayward, CA) were used to study the substrate use patterns of the soil microbial communities, as described by Girvan et al. (2003). Briefly, 10 g of fresh soil was added to 100 mL of distilled water in a 250-mL flask and shaken at 200 rpm for 10 min to achieve a 10 dilution. Ten-fold serial dilutions were prepared, and the 10 dilution was used to inoculate the Biolog EcoPlates. The plates were incubated at 25 C for 6 d, and color development was read daily as absorbance at 590 nm with a Biolog microplate reader. Statistical analysis. All data were expressed as means and SDs and were compared statistically using Fisher’s least significant difference test at the 5% level with the software package SPSS 16.0 for Windows (SPSS Inc., Chicago, IL). The average well color development (AWCD) value of the Biolog data was calculated at each time point by dividing the sum of the optical density data by 31 (number of substrates). The functional diversity of the microbial community was assessed by the Shannon index (Zak et al., 1994) calculated using the following formula: