Soil ionomic and enzymatic responses and correlations to fertilizations amended with and without organic fertilizer in long-term experiments

Feng, Xumeng; Ling, Ning; Chen, Huan; Zhu, Chen; Duan, Yinghua; Peng, Chang; Yu, Guanghui; Ran, Wei; Shen, Qirong; Guo, Shiwei

doi:10.1038/srep24559

Soil ionomic and enzymatic responses and correlations to fertilizations amended with and without organic fertilizer in long-term experiments

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Published: 15 April 2016

Volume 6, article number 24559, (2016)
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Soil ionomic and enzymatic responses and correlations to fertilizations amended with and without organic fertilizer in long-term experiments

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Xumeng Feng¹^na1,
Ning Ling¹^na1,
Huan Chen²^na1,
Chen Zhu¹^na1,
Yinghua Duan³^na1,
Chang Peng⁴^na1,
Guanghui Yu¹^na1,
Wei Ran¹^na1,
Qirong Shen¹^na1 &
…
Shiwei Guo¹^na1

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Abstract

To investigate potential interactions between the soil ionome and enzyme activities affected by fertilization with or without organic fertilizer, soil samples were collected from four long-term experiments over China. Irrespective of variable interactions, fertilization type was the major factor impacting soil ionomic behavior and accounted for 15.14% of the overall impact. Sampling site was the major factor affecting soil enzymatic profile and accounted for 34.25% of the overall impact. The availabilities of Pb, La, Ni, Co, Fe and Al were significantly higher in soil with only chemical fertilizer than the soil with organic amendment. Most of the soil enzyme activities, including α-glucosidase activity, were significantly activated by organic amendment. Network analysis between the soil ionome and the soil enzyme activities was more complex in the organic-amended soils than in the chemical fertilized soils, whereas the network analysis among the soil ions was less complex with organic amendment. Moreover, α-glucosidase was revealed to generally harbor more corrections with the soil ionic availabilities in network. We concluded that some of the soil enzymes activated by organic input can make the soil more vigorous and stable and that the α-glucosidase revealed by this analysis might help stabilize the soil ion availability.

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Introduction

In the last 50 years, China have remarkable growth in agricultural production. This has created the so called “Miracle in China” with 7% of the world’s arable land feeding 22% of the world’s population. The intensification of crop production over the last 50 years has been achieved through the use of modern high-yielding varieties and major benefits have been realized by using chemical fertilizers¹. China is currently the world’s largest consumer of mineral fertilizer, the consumption of which has increased almost linearly because farmers prefer to use greater amounts of chemical fertilizers to attain higher yields. Unfortunately, this high increment of mineral fertilizer consumption coexists with extremely low fertilizer nutrient use efficiency, which is attributed to widespread environmental damages^2,3. Therefore, government is planning to reduce the consumption of chemical fertilizers and pesticides in future years while maintaining high crop production. The partial replacement of chemical fertilizers with organic fertilizers is an alternative way to achieve this goal.

Since China has large livestock and poultry breeding industries⁴, the use of the organic wastes can reduce chemical fertilizer input easily and thus maintain high crop production to meet the food requirement of the large population and improve the soil quality^{Full size image}

Network analysis between soil enzyme profile and soil ionomic profile

Network inference was employed to explore co-occurrence patterns between the soil enzyme activity profile and the soil ionomic profile (Fig. 4, Table S1). In general, there were fewer correlations in the CF than in the COF soils. In the CF soils, the network had 25 nodes and 46 edges (26 positive correlations), the modularity was 0.344 with 2 communities and the α- and β-glucosidase activities were the most active factors. For the COF soils, the network had 26 nodes and 53 edges (27 positive correlations), the modularity was 0.295 with 3 communities and the α-glucosidase, acid phosphomonoesterase and β-D-xylosidase activates were the most active factors. In the networks, we selected α-glucosidase activity as the generalist because it had the most connections in both CF and COF. Most of the connections (72.7% in both CF and COF) of α-glucosidase were negative, which indicated that it may provide a certain contribution on stabilizing the ion availability.

Network analysis within the soil ionome

We further explored co-occurrence patterns within the soil ionome by using network inference based on Pearson’s significant correlations (Fig. 5, Table S1). The network analysis was markedly different for the CF and COF samples and the connections in the CF samples were much more complicated than were those in the COF samples. In the CF, the network had 19 nodes and 88 edges (53 positive correlations), with 4.632 average degrees (connections). For the COF, the network had 19 nodes and 64 edges (43 positive correlations), with 3.368 average degrees (connections). More active ions (with high degrees), such as Pb, Co, Al, Na, Ca, As, La, B, Cu, K, P and Zn, were found in CF, the degrees of which were greater than 10. In the COF, only Mn, Ni and Zn were found to have the degree greater than 10. In this network, Zn was active in both the CF and COF samples. Most of the positive connections of Zn, which were 80% and 90% in the CF and COF samples, respectively, were found, which indicated that Zn could play a certain role in inter-activating ion availability.

Discussion

Unraveling the relative importance of fertilization patterns versus soil types in the full-scale evaluation of soil fertility pose significant challenges. Data currently available are mostly based on the simple comparison of soil properties in long-term fertilizations^13,14. Moreover, organic waste is also recognized to be a significant source of contaminants in depositions in soils¹⁵. While these previous works have provided great insights into the effects of fertilizations on individual soil factors, the classic statistical way used in these works have a limited ability to differentiate the combined effects of soil properties that occur in agricultural ecosystems. Thus, in our study, the samples from long-term experiments (Table 1) were analyzed in depth and a series of advanced statistical analyses were performed. The ordination of the soil ionomic and enzymatic profiles were generated and revealed a clear separation between the CF and COF samples (Figs 1 and 2), which indicates a selective change in the ion availability and enzyme activities under the organic amendment compared with long-term chemical fertilizer-treated soils, as noted in other studies²⁵. At the same time, the organic input offers an organic substrate for soil microbes and activates their functions¹⁷. Compared with the samples that had no organic input, the P availability showed the largest increase among the tested ions following treatment with organic fertilizer (Fig. 3). Further, the available Zn, Cu, Mo and Cd were significantly increased by the long-term application of organic fertilizer.

Most organic fertilizer in China originates from farmyard manure. Due to the use of feed additives, livestock and poultry manures from factory farms contain high heavy metal concentrations, particularly the heavy metals Cu and Zn²⁶. Additionally, manure is rich in Cd, which might accumulate in the soil^{Fig. 4). In our results, the α-glucosidase activity was activated (Fig. 3) and the network of the soil ionome became more stabilized by organic amendment (Fig. 5). Therefore, it is possible to deduce that organic amendment can stabilize soil ion interaction and improve the soil buffering capacity, which might partially be attributed to the activated α-glucosidase activity. To the best of our knowledge, this is the first study to utilize the novel approach of soil ionomics with mutual information to simultaneously investigate the single ion and multiple ions that were represented by ion networks and the associations with soil enzymes. The next logical step is to go beyond merely describing the patterns revealed by the network analysis and design more focused experiments to understand the mechanisms producing patterns of α-glucosidase activity and soil stability.}

In summary, our results suggest that organic amendment can regulate soil ionome and soil enzyme activities, which result in benefits to the soil nutrient supply state and plant heavy metal content. The novel analysis approach also deciphered soil vitality as an aspect of the network between the soil ionome and the soil enzymes and clarified the manipulation power of organic fertilizer and other environmental variables in sha** soil functions. The α-glucosidase was exposed by the analysis and might provide a certain contribution to stabilizing soil ion availability. Further analysis is needed to better understand the mechanisms by which α-glucosidase mediates soil ion availability through activation by organic input, whereby the study of the role of organic fertilizer in sha** soil functions is of prime importance.

Methods

Field sites and sample collection

Four long-term fixed fertilization experiments were selected throughout the major grain producing areas of China. As shown in Table 1, the first long-term experiment was located in Jilin Province (JL) and was initiated in 1990; the climatic conditions, crop** system and physical and chemical characteristics of the initial field soil were all introduced in our previous paper¹⁷. The second long-term experiment was located in Shandong Province (SD) and has been performed since 1978; detailed information about the climate, crop** system and physical and chemical characteristics of the initial field soil in this experiment was described by Song et al.³¹. The third long-term experiment was set in Anhui Province (AH), which is located in the Yangliu experimental base of Anhui agricultural academy and was conducted starting in 1981 with wheat-maize rotation. The primary soil chemical and physical properties of the AH long-term experiment were pH (H₂O) of 7.6, 10.22 g · kg⁻¹ organic matter, 0.78 g · kg⁻¹ total N and 2.5 g · kg⁻¹ of available P. The fourth long-term experiment was carried out starting in 1990 at Hunan Province (HN), the basic status of which was published previously^32,33. According to the China Soil Classification System³⁴, the soil great groups of each experimental site are listed in Table 1.

In every long-term experiment, two treated soils were collected: the soil applied only with chemical N, P and K fertilizers (CF) and the soil applied with chemical fertilizers plus organic fertilizer (COF). The chemical properties of sampled soil and the fertilizer application rate of the treatments in each long-term experiment are listed in Table S2. Three replicates, which were collected from three separated plots, were sampled in each treatment at each experimental site. Each sample was composited with 10 soil cores (5 cm diameter) from each plot at a depth of 0–20 cm. All soil samples were collected from the four long-term experiments (2 treatments *3 replicates *4 experiments = 24 samples in total) at the fallow stage, post-harvest but before the next season’s crop planting in 2013–2014. In order to maximally evaluate the long-term effect of the fertilization and avoid the nonuniformity of moisture between samples from different sites, all the soil samples were air dried for about 7 days to maintain ~15% soil water content and then sieved through a 1.0 mm sieve in preparation for the analyses of exchangeable ions and soil enzyme activities.

Soil ionomic analyses

The exchangeable ions, B, Na, Al, P, K, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Mo, Cd, La, Pb and Ca, were extracted from each sample. The available P was extracted with sodium bicarbonate and then determined by the molybdenum-blue method⁶. The other ions were extracted by 1 M Mg(NO₃)₂ (pH7, Soil: Mg(NO₃)₂ = 2.5:10) after shaking for 2 h at 25 °C. The soil extracts were filtered and the ionomic concentrations of soil extracts were determined by inductively coupled plasma mass spectrometry (ICP-MS, Perkin Elmer Nexion 300×) in the He gas collision mode to minimize polyatomic interferences.

Enzyme activities analysis

The potential activities of 7 enzymes involved in C, N, P and S cycling, i.e., acid phosphomonoesterase, sulfatase, β-glucosidase, β-cellobiosidase, N-acetyl-glucosaminidase, β-xylosidase and α-glucosidase, of soil samples were measured using 4-methylumbelliferyl-esters as substrates, producing fluorescent 4-methylumbeliferone (MUF) after hydrolysis³⁵. A 96-well microplate was used for these analysis as described by Deng et al.³⁶. The fluorescence intensity was determined by a microplate fluorometer (Scientific Fluoroskan Ascent FL, Thermo) with 365 nm excitation and 450 nm emission filters. The enzyme activities were expressed as nanomoles per hour per gram of soil (Table S3).

Data analysis

Nonmetric multidimensional scaling (NMDS) plots were used to visualize the profile variations among samples, using the ionomic and enzymatic concentration matrix. The plots were generated from Bray–Curtis dissimilarity index matrices of the 24 samples. Variance partitioning analysis (VPA) was also used to determine the contributions of pH, locations and fertilization and the interactions among them on the variation in ionome and enzyme activities with Hellinger-transformed data. NMDS and VPA were performed by R (version 2.15.0) with the vegan packages³⁷.

The effects of organic amendment on the ionome and enzyme activities across the experiments were analyzed using the response ratio (RR) as a common effect size metric³⁸. To quantify simple two-group experimental designs, the calculation of RR is straightforward:

Here, R is the natural-log proportional change in the means (X) of the target variable of CF (X_CF) and OF (X_COF), respectively. The analysis was performed on a spreadsheet using a random effect model according to Geisseler and Scow²⁰.

The analysis, when pooling RR from multiple studies, also assigns a weight to each R that is inversely proportional to its sampling variance:

where SD and n are the standard deviation and sample size (n = 12 in this study) of X_COF and X_CF, respectively³⁹. The 95% confidence interval (CI) for the response ratio is:

λ = 1.96 for 95% CI. The difference is significant only if 95% of a response variable does not overlap with zero⁴⁰. A positive response ratio indicates an increase in soil exchangeable ion concentrations and soil enzyme activities under long-term organic fertilizer amendment relative to the set of soil ions and enzyme activities of only chemical fertilization, whereas a negative response ratio indicates the opposite.

Network analyses were performed to better understand the relationships between enzymes and ions and between ions within the long-term experiments. To analyze the networks, we calculated all possible Pearson’s correlation coefficients. To filter the data for reduced network complexity, we considered high correlations with a cutoff at r > 0.58 and a statistically significant P-value < 0.05, accounting for all replicates. The nodes in the reconstructed network represent ionomic and enzymatic groups and the edges represent high and significant correlations between nodes. The network graphs were made based on a set of measures, as average node connectivity, average path length, diameter and cumulative degree distribution⁴¹. Statistical analyses were carried out in the R software package (http://www.r-project.org/) and network visualization with the interactive platform Gephi⁴².

Additional Information

How to cite this article: Feng, X. et al. Soil ionomic and enzymatic responses and correlations to fertilizations amended with and without organic fertilizer in long-term experiments. Sci. Rep. 6, 24559; doi: 10.1038/srep24559 (2016).

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Acknowledgements

Financial supports from the National Basic Research Program of China (2013CB127403, 2015CB150500) are acknowledged. Many graduate students and staffs involved in maintaining the field plots and collecting soil samples but not listed as coauthors are grateful.

Author information

Feng Xumeng and Ling Ning contributed equally to this work.

Authors and Affiliations

Jiangsu Key Laboratory for Solid Organic Waste Utilization, Nan**g Agricultural University, Nan**g, 210095, China
Xumeng Feng, Ning Ling, Chen Zhu, Guanghui Yu, Wei Ran, Qirong Shen & Shiwei Guo
Crop Research Institute, Anhui Academy of Agricultural Science, Hefei, 230031, China
Huan Chen
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Bei**g, 100081, China
Yinghua Duan
Agriculture Environment and Resources Center, Jilin Academy of Agricultural Sciences, Changchun, 130033, China
Chang Peng

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Xumeng Feng
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Ning Ling
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Guanghui Yu
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Contributions

L.N. and G.S. proposed and organized the overall project. F.X. and L.N. performed the majority of the experiments. C.H., Z.C., D.Y., P.C., Y.G. and R.W. gave assistance in sample preparation and long-term experiment. L.N. and S.Q. wrote the main manuscript text. All authors reviewed the manuscript.

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Feng, X., Ling, N., Chen, H. et al. Soil ionomic and enzymatic responses and correlations to fertilizations amended with and without organic fertilizer in long-term experiments. Sci Rep 6, 24559 (2016). https://doi.org/10.1038/srep24559

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Received: 19 June 2015
Accepted: 31 March 2016
Published: 15 April 2016
DOI: https://doi.org/10.1038/srep24559
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Soil ionomic and enzymatic responses and correlations to fertilizations amended with and without organic fertilizer in long-term experiments

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Introduction