Fertilization can accelerate the pace of soil microbial community response to rest-grazing duration in the Three-river Source Region of China
Data files
Nov 14, 2023 version files 25.43 KB
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1.Soil_original_datas_with_non_fertilization.csv
2.39 KB
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2.Soil_original_datas_with_fertilization.csv
2.44 KB
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3.PLFA_original_datas_with_non_fertilization.csv
1.84 KB
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4.PLFA_original_datas_with_fertilization.csv
1.85 KB
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5.Correlation_cofficient_datas_with_non_fertilization.csv
1.53 KB
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6.Correlation_cofficient_datas_with_fertilization.csv
1.53 KB
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7._P_value_with_non_fertilization.csv
1.51 KB
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8._P_value_with_fertilization.csv
1.46 KB
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README.md
10.87 KB
Abstract
Objectives
Overgrazing leads to grassland degradation and productivity decline. Rest-grazing during the regreen-up period can quickly restore grassland and fertilization is a common restoration measure. Meanwhile, soil microorganisms are more sensitive indicators. Therefore, the experiment of rest-grazing time and fertilization was carried out to explore the response of soil microorganisms to rest-grazing time and fertilization measures.
Methods
A field control experiment with rest-grazing time and fertilization as factors was conducted from the time when grass returned to green till the livestock moved to the summer pasture in Dawu Town of Maqin County. The primary treatment we established was the five rest-grazing times, including rest-grazing times of 20 days, 30 days, 40 days, 50 days, and traditional grazing was used as a check group. At the same time, the secondary treatment was nitrogen addition of 300 kg·hm-2 in each primary treatment.
Results
The results showed that: the total phospholipid fatty acid (total PLFA), actinomyces (Act) and arbuscular mycorrhizal fungi (AMF) showed an ever-increasing biomass with the increase of rest-grazing time and the highest was at 50 days of rest-grazing, and they were all significantly higher than CK. In addition, soil microbial biomass carbon-nitrogen ratio (MBC/MBN) had a great influence on the change of microbial community. Applying nitrogen fertilizer can increase the maximum value of biomass of all PLFA groups and the biomass of all PLFA groups changed in an “inverted V” shape with the increase of rest-grazing time. Besides, instead of MBC/MBN, NO3--N was positively affected by the biomass of all PLFA groups, which actively regulated the trend of microbial functions.
Conclusions
The longer rest-grazing time is more conducive to the biomass of all PLFA groups. However, applying nitrogen fertilizer could break this pattern, namely, the 30d rest-grazing would be beneficial to the biomass of all PLFA groups. These findings provide key information that rest-grazing during the regreen-up period is beneficial to all PLFA groups and fertilization could change the response of microorganisms to rest-grazing, which provides reference measures for the restoration of degraded alpine meadows.
README
This README file was generated on 2023-11-14 by Xuanbo Zhou.
GENERAL INFORMATION
- Title of Dataset: Fertilization can Accelerate the Pace of Soil Microbial Community Response to Rest-grazing Duration in the Three-river Source Region of China
- Author Information
A. Principal Investigator Contact Information
Name: Xuanbo Zhou
Institution: Qinghai University Address: Xining,Qinghai, OR China Email: 15500533873@163.com <br> B. Corresponding author Contact Information Name:Xiaoli Wang Institution: Qinghai University Address: Xining,Qinghai, OR China Email: wxl.yu@163.com - Date of data collection (single date, range, approximate date): July 2017 - August 2017
- Geographic location of data collection: Yongbao Animal Husbandry Committee, Dawu Town, Guoluo Tibetan Autonomous Prefecture
- Information about funding sources that supported the collection of the data: National Natural Science Foundation of China (32260327 ,32230068 & U21A20183) and the “Western Light” Young Scholars Program of the Chinese Academy of Sciences.
SHARING/ACCESS INFORMATION
- Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain
- Links to publications that cite or use the data: Xuanbo Zhou, Xiaoli Wang, Yushou ma...& Lele Xie.(2023). Fertilization can Accelerate the Pace of Soil Microbial Community Response to Rest-grazing Duration in the Three-river Source Region of China
- Links to other publicly accessible locations of the data: None
- Links/relationships to ancillary data sets: None
- Was data derived from another source? No A. If yes, list source(s): NA
- Recommended citation for this dataset: Xuanbo Zhou, Xiaoli Wang, Yushou ma...& Lele Xie.(2023). . Data from: Fertilization can Accelerate the Pace of Soil Microbial Community Response to Rest-grazing Duration in the Three-river Source Region of China. Dryad Digital Repository.https://doi.org/10.5061/dryad.280gb5mv6
DATA & FILE OVERVIEW
- File List:
A) Soil original datas with non fertilization.csv
B) Soil original datas with fertilization.csv
C) PLFA original datas with non fertilization.csv
D) PLFA original datas with fertilization.csv
E) Correlation cofficient datas with non fertilization.csv
F) Correlation cofficient datas with fertilization.csv
G) P value with non fertilization.csv
H) P value with fertilization.csv
- Relationship between files, if important: None
- Additional related data collected that was not included in the current data package: None
- Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA
#########################################################################
DATA-SPECIFIC INFORMATION FOR:
Soil original datas with non fertilization.csv
- Number of variables: 14
- Number of cases/rows: 16
- Variable List:
- Name: A1,A2orA3(i.e., three independent replicate blocks); all treatments CK, 20d, 30d, 40or 50d ( i.e., times of rest-grazing).
- SM(%): soil moisture content
- pH: soil potential of hydrogen
- SOC(g/kg): soil organic carbon
- TN(g/kg): total nitrogen
- soil C:N:soil carbon-nitrogen ratio
- NH4+(mg/g) and NO3-(mg/g): inorganic N
- TP(g/kg): total phosphorus
- AP(mg/g):vailable phosphorus
- TK(g/kg):total potassium
- MBC(mg/g):soil microbial biomass carbon
- MBN(mg/g):soil microbial biomass nitrogen
- MBC:MBN:soil microbial biomass carbon-nitrogen ratio
- Missing data codes: None
- Specialized formats or other abbreviations used: None
#########################################################################
DATA-SPECIFIC INFORMATION FOR:
Soil original datas with fertilization.csv
- Number of variables: 14
- Number of cases/rows: 16
- Variable List:
- Name: A1,A2orA3(i.e., three independent replicate blocks); all treatments CK, 20d, 30d, 40or 50d ( i.e., times of rest-grazing); N：representing this group is fertilization treatment.
- SM(%): soil moisture content
- pH: soil potential of hydrogen
- SOC(g/kg): soil organic carbon
- TN(g/kg): total nitrogen
- soil C:N:soil carbon-nitrogen ratio
- NH4+(mg/g) and NO3-(mg/g): inorganic N
- TP(g/kg): total phosphorus
- AP(mg/g):vailable phosphorus
- TK(g/kg):total potassium
- MBC(mg/g):soil microbial biomass carbon
- MBN(mg/g):soil microbial biomass nitrogen
- MBC:MBN:soil microbial biomass carbon-nitrogen ratio
- Missing data codes: NA (data not available)
- Specialized formats or other abbreviations used: None
#########################################################################
DATA-SPECIFIC INFORMATION FOR:
PLFA original datas with non fertilization.csv
- Number of variables: 10
- Number of cases/rows: 16
- Variable List:
- Name: A1,A2orA3(i.e., three independent replicate blocks); all treatments CK, 20d, 30d, 40or 50d ( i.e., times of rest-grazing).
- AMF(nmol/g): arbuscular mycorrhizal fungi
- Act(nmol/g): actinomyces
- B(nmol/g): bacterial biomarkers
- G-(nmol/g): gram-negative bacterial PLFA biomarkers
- G+:(nmol/g)gram-positive bacterial PLFA biomarkers
- F(nmol/g): fungal PLFA biomarkers
- F:B: bacterial PLFA biomarker biomass ratios
- G+:G-: the ratio of gram-positive bacterial PLFA biomass to gram-negative bacterial PLFA biomass
- total PLFA(nmol/g): the total phospholipid fatty acid
- Missing data codes: NA (data not applicable)
- Specialized formats or other abbreviations used: None
#########################################################################
DATA-SPECIFIC INFORMATION FOR:
PLFA original datas with fertilization.csv
- Number of variables: 10
- Number of cases/rows: 16
- Variable List:
- Name: A1,A2orA3(i.e., three independent replicate blocks); all treatments CK, 20d, 30d, 40or 50d ( i.e., times of rest-grazing); N：representing this group is fertilization treatment.
- AMF(nmol/g): arbuscular mycorrhizal fungi
- Act(nmol/g): actinomyces
- B(nmol/g): bacterial biomarkers
- G-(nmol/g): gram-negative bacterial PLFA biomarkers
- G+(nmol/g):gram-positive bacterial PLFA biomarkers
- F(nmol/g): fungal PLFA biomarkers
- F:B: bacterial PLFA biomarker biomass ratios
- G+:G-: the ratio of gram-positive bacterial PLFA biomass to gram-negative bacterial PLFA biomass
- total PLFA(nmol/g): the total phospholipid fatty acid
- Missing data codes: None
- Specialized formats or other abbreviations used: None
#########################################################################
DATA-SPECIFIC INFORMATION FOR:
Correlation cofficient datas with non fertilization.csv
These are the data used to indicate the correlation absolute value between soil physicchemical properties and soil microbial community.
- Number of variables: 10
- Number of cases/rows: 14
- Variable List:
- r value: soil physicchemical properties( Abbreviations are shown in “Soil original datas with non fertilization.csv”)
- AMF(nmol/g): arbuscular mycorrhizal fungi
- Act(nmol/g): actinomyces
- B(nmol/g): bacterial biomarkers
- G-(nmol/g): gram-negative bacterial PLFA biomarkers
- G+(nmol/g):gram-positive bacterial PLFA biomarkers
- F(nmol/g): fungal PLFA biomarkers
- F:B: bacterial PLFA biomarker biomass ratios
- G+:G-: the ratio of gram-positive bacterial PLFA biomass to gram-negative bacterial PLFA biomass
- total PLFA(nmol/g): the total phospholipid fatty acid
- Missing data codes: NA (data not available)
- Specialized formats or other abbreviations used: None
#########################################################################
DATA-SPECIFIC INFORMATION FOR:
Correlation cofficient datas with fertilization.csv
These are the data used to indicate the correlation absolute value between soil physicchemical properties and soil microbial community of fertilization treatment.
- Number of variables: 10
- Number of cases/rows: 14
- Variable List:
- r value: soil physicchemical properties( Abbreviations are shown in “Soil original datas with non fertilization.csv”)
- AMF(nmol/g): arbuscular mycorrhizal fungi
- Act(nmol/g): actinomyces
- B(nmol/g): bacterial biomarkers
- G-(nmol/g): gram-negative bacterial PLFA biomarkers
- G+(nmol/g):gram-positive bacterial PLFA biomarkers
- F(nmol/g): fungal PLFA biomarkers
- F:B: bacterial PLFA biomarker biomass ratios
- G+:G-: the ratio of gram-positive bacterial PLFA biomass to gram-negative bacterial PLFA biomass
- total PLFA(nmol/g): the total phospholipid fatty acid
- Missing data codes: None
- Specialized formats or other abbreviations used: None
#########################################################################
DATA-SPECIFIC INFORMATION FOR:
P value with non fertilization.csv
These are the data used to indicate the P value between soil physicchemical properties and soil microbial community.
- Number of variables: 10
- Number of cases/rows: 14
- Variable List:
- P-value: soil physicchemical properties( Abbreviations are shown in “Soil original datas with non fertilization.csv”)
- AMF(nmol/g): arbuscular mycorrhizal fungi
- Act(nmol/g): actinomyces
- B(nmol/g): bacterial biomarkers
- G-(nmol/g): gram-negative bacterial PLFA biomarkers
- G+(nmol/g):gram-positive bacterial PLFA biomarkers
- F(nmol/g): fungal PLFA biomarkers
- F:B: bacterial PLFA biomarker biomass ratios
- G+:G-: the ratio of gram-positive bacterial PLFA biomass to gram-negative bacterial PLFA biomass
- total PLFA(nmol/g): the total phospholipid fatty acid
- Missing data codes: None
- Specialized formats or other abbreviations used: None #########################################################################
DATA-SPECIFIC INFORMATION FOR:
P value with fertilization.csv
These are the data used to indicate the P value between soil physicchemical properties and soil microbial community of fertilization treatment..
- Number of variables: 10
- Number of cases/rows: 14
- Variable List:
- P-value: soil physicchemical properties( Abbreviations are shown in “Soil original datas with non fertilization.csv”)
- AMF(nmol/g): arbuscular mycorrhizal fungi
- Act(nmol/g): actinomyces
- B(nmol/g): bacterial biomarkers
- G-(nmol/g): gram-negative bacterial PLFA biomarkers
- G+(nmol/g):gram-positive bacterial PLFA biomarkers
- F(nmol/g): fungal PLFA biomarkers
- F:B: bacterial PLFA biomarker biomass ratios
- G+:G-: the ratio of gram-positive bacterial PLFA biomass to gram-negative bacterial PLFA biomass
- total PLFA(nmol/g): the total phospholipid fatty acid
- Missing data codes: None
- Specialized formats or other abbreviations used: None
Methods
Soil samples were obtained in August 2017, when the grass was growing vigorously. In each subplot, three soil samples were collected to reduce spatial heterogeneity, and five soil cores (3.5cm diameter) were taken at a depth of 0 to 15 cm regarding each one as one soil sample. After the soil samples were measured, the data of three soil samples from each subplot were averaged to obtain one value. Therefore, only one value was obtained for each subplot. In total, three independent samples were obtained from each treatment of three replicate blocks (A, B and C). In order to avoid the influence of livestock waste on soil characteristics, we absolutely avoid livestock waste and the traces of livestock waste when sampling. A total of 90 soil samples (three soil samples ×ten subplots × three replicate blocks) were collected in this study. Then, three cutting ring soil samples were collected from 0–15 cm depths in each plot to determine the soil moisture content (SM). In addition, when collecting soil samples, we remove debris such as plant litter, stone particles and livestock excreta. Each soil sample was sieved through a 2 mm mesh and then divided into two parts when the soil samples were taken back to the laboratory (Joshi et al., 2023). One part of the fresh soil from the field was stored at 4°C for microbial biomass (carbon and nitrogen) for inorganic N (NH4+−N and NO3－−N ) and available phosphorus (AP) analyses, and the other part was air-dried indoors and used to determine indicators such as soil physicochemical properties (Zhao et al., 2018).In order to reveal the effects of rest-grazing and fertilization during the regreen-up period on the structure of soil microbial community. Soil microbial PLFA was determined based on the method of Bossio and Scow (1998) (Bossi et al., 1998).
The data for each subplot was expressed as the mean of three soil samples, so data analysis was carried out on the data duplicate value (n=3), and the data analysis was performed according to the following steps. Firstly, one-way analysis of variance (ANOVA) and least significant difference (LSD) tests were used to examine the differences of soil physicochemical properties and microbial community structure among the different rest-grazing treatments. Then the independent sample T-tests were used to examine the response of soil physicochemical properties and microbial community structure to fertilization treatment. Secondly, the correlation analysis was used to test the relationship between soil physicochemical properties and microbial community structure. Finally, the redundancy analysis method (RDA) was used to evaluate the associations between soil physicochemical properties and community microorganisms. The above analysis used Microsoft Excel 2013 to organize and summarize the original data, and used SPSS25.0 statistical software to conduct statistical analysis on the data. Histogram of microbial community compositions and percentage increase of fertilization to the maximum value of all PLFA groups were drawn with Origin 2021. The graphs of correlation analysis were performed using the “ggplot2” and “ggthemes” packages in R 4.2.2 and RDA employed CANOCO 5 to complete (Lu et al., 2023). P＜0.05 was considered statistically significant.
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