Soil respiration and response of carbon source changes to vegetation restoration in the Loess Plateau, China

Journal Pre-proof Soil respiration and response of carbon source changes to vegetation restoration in the Loess Plateau, China

Peng Shi, Yanli Qin, Qi Liu, Tiantian Zhu, Zhanbin Li, Peng Li, Zongping Ren, Ying Liu, Feichao Wang PII:

S0048-9697(19)35501-9

DOI:

https://doi.org/10.1016/j.scitotenv.2019.135507

Reference:

STOTEN 135507

To appear in:

Science of the Total Environment

Received date:

4 September 2019

Revised date:

12 November 2019

Accepted date:

12 November 2019

Please cite this article as: P. Shi, Y. Qin, Q. Liu, et al., Soil respiration and response of carbon source changes to vegetation restoration in the Loess Plateau, China, Science of the Total Environment (2019), https://doi.org/10.1016/j.scitotenv.2019.135507

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© 2019 Published by Elsevier.

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Soil respiration and response of carbon source changes to vegetation restoration in the Loess Plateau, China Peng Shia,b, Yanli Qina, Qi Liuc, Tiantian Zhud, Zhanbin Lia,b, Peng Lia,b,*, Zongping Rena,b, Ying Liua,*, Feichao Wanga a

State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi’an University of

Technology, Xi’an, 710048, China b

Key Laboratory of National Forestry Administration on Ecological Hydrology and Disaster

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Prevention in Arid Regions, Xi’an, 710048, China Tianshui Soil and Water Conservation Experimental Station, Tianshui, 741000, China

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College of Architecture , Xi’an University of Architecture and Technology, Xi’an, 710043, China

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c

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⁎ Corresponding author.

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E-mail address: [email protected] (P. Li), [email protected](Y. Liu)

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Abstract Soil respiration is a large carbon flux from terrestrial ecosystems to the atmosphere, and small variations in soil respiration can prominently influence the global carbon (C) cycle. The vegetation changes could directly affect soil respiration. The large-scale

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“Grain for Green” project carried out on the Loess Plateau, China has importantly affected the contribution of soil respiration to atmospheric carbon dioxide (CO2).

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Therefore, it is important to study the effects of vegetation restoration on soil

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respiration. We selected four land-use types: crop, forest, shrub, and grassland in the

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Zhifanggou watershed to analyze variation in soil respiration during dry and rainy

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seasons. Furthermore, the source of CO2 emissions from soil respiration was

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identified using isotopes. The results showed that soil respiration in the rainy season was significantly higher than that in the dry season (P < 0.05). Soil respiration in the

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dry season was as follows: shrubland (1.04 µmol m−2 s−1) > cropland (0.72 µmol m−2 s−1) > forestland (0.44 µmol m−2 s−1) > grassland (0.33 µmol m−2 s−1). However, grass and forestland had significantly higher soil respiration than shrub and cropland in the rainy season (P < 0.05). Roots were the main source of soil respiration in cropland, which contributed >70% of CO2 emissions. Following revegetation, litter contributed more to soil respiration than roots or soil microorganisms at >68% of soil respiration. Our results provide a theoretical basis for assessing C balance in terrestrial ecosystems. Keywords:

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Carbon source; Grain for Green; Seasonal variation; Soil respiration rate

1. Introduction Soil, as an important carbon (C) reservoir in terrestrial ecosystems, stores about 2,000 Pg C, which is three times as much as is stored by vegetation (Bond-Lamberty

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et al., 2018). The global C flux from soil to atmosphere is 68–98 Pg C y−1, and soil respiration is an important C source (Jian et al., 2018). Soil respiration releases 10

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times more carbon dioxide (CO2) than human activities (e.g., fuel consumption) and is

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an important connection between soil C pools and atmospheric CO2 (Joo et al., 2012;

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Raich and Tufekcioglu, 2000). Soil respiration has a great influence on atmospheric

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CO2 concentration, and even small changes in soil respiration can greatly change

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global atmospheric CO2 concentrations (Adachi et al., 2017). Previous studies have shown that a number of factors affect soil respiration and soil

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C concentration, such as land-use change (Shi et al., 2019a; Yao et al., 2019), soil erosion (Yu et al., 2019a), climate change (Berryman et al., 2018), and agricultural management practices (Chen et al., 2018a). Among these, land-use change is one of the most important factors affecting soil respiration (Shi et al., 2019b). Land-use conversion affects the input and degradation rates of organic matter and physical and biological conditions of the soil, consequently affecting microbial activity (Assefa et al., 2017; Villarino et al., 2017). Unsustainable land-use management will decrease soil C sequestration. Llorente et al. (2010) reported that deforestation can cause a >60% decrease in soil C. Conversion of natural woodland or grassland to cultivated

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land reduces soil C reservoirs. Conversely, sustainable land-use structures can increase soil C storage (Machmuller et al., 2015). According to a meta-analysis carried out by Guo and Gifford (2002), the conversion of cropland to woodland or grassland increased the soil organic C pool by 19% and 53%. The Loess Plateau is the largest and deepest loess sedimentary region in the world,

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covering an area of 6.4 × 104 km2, most of which is covered by mountainous and hilly land (Fu et al., 2017). For a long time, the Loess Plateau has been severely disturbed

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by human activities, such as deforestation, farming, and grazing, leading to it

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becoming the most severely eroded region on Earth (Shi et al., 2019c; Wang et al.,

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2019; Yu et al., 2015). The Loess Plateau is also the largest source of sediment for the

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Yellow River, representing ~90% of the sediment transported into the system (Wang

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et al., 2016). To effectively control soil erosion and restore ecosystems, the Chinese government has implemented a policy since 1999 under which farmland is converted

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in forests on the Loess Plateau. At present, >30,000 km2 of sloping farmland has been converted into forest, shrub, and grassland (Xu et al., 2018). As human activities intensified over the past 1000 years, the natural vegetation was cleared (Fu et al., 2017). After this restoration project, the vegetation type changed, and the common species are Robinia pseudoacacia, Platycladus orientalis, Pines tabulaeformis, Melilotus suaveolens Ledeb, Caragana korshinskii, Hippophae rhamnoides, Astragalus adsurgens, and Medicago sativa. Large-scale vegetation restoration in the Loess Plateau has significantly improved vegetation coverage. Studies have shown that NDVI values in most regions have

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significantly increased over the past 10 years (Cao et al., 2018). Vegetation restoration also has a profound effect on the soil–atmosphere C cycle. After vegetation restoration, soil respiration increased mainly because, (1) root biomass increased, (2) soil structure improved, and microbial biomass and activity increased, (3) root secretion and litter increased (Barba et al., 2018; Xiao et al., 2019;

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Yu et al., 2019b). Less is known about the effects of vegetation restoration on the soil respiration rate and C source in eroded areas of the Loess Plateau, China. In the

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present study, soil respiration was analyzed for four different land-use types (crop,

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forest, shrub, and grassland). We hypothesized that, (1) soil respiration would change

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after vegetation restoration, (2) C source of soil respiration would be different among

2.1 The study area

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2. Materials and methods

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the different land-use types.

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The Zhifanggou watershed (36°51′30″ N, 109°19′30″ E) is in Anshai County, Shaanxi Province (Fig. 1). It is a second-level tributary of the Yanhe River and a typical hilly and gully loess region with a watershed area of 8.27 km2. The annual mean precipitation is 505.3 mm (Zhao et al., 2016), which mainly falls in June and July (76.6 mm and 175.4 mm in 2016, respectively, as shown in Fig. 2). This area has a semi-arid climate with annual sunshine hours of 2415 h and annual radiation of 493 J cm−2. The average annual temperature is 8.8 ℃ (with the highest temperature of 22.7 ℃ in August and the lowest temperature of −6.2 ℃ in January in 2016, as shown in Fig. 2). The dominant soil type in the study area is loessial soil (Calcaric Regosols

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in WRB soil classification) with a fine silt texture. The loessial soil is weakly cohesive and slope runoff during the rainy season can result in serious soil loss through water erosion. Insert Figure 1 here Insert Figure 2 here Owing to long-term deforestation and agricultural activity, there is much soil

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erosion in this region with an annual erosion rate of 8,000–12,000 t km−2 y−1, leading

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to the fragmentation of landform, vertical and horizontal gullies, and degradation of

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ecosystems. The vegetation in the area is dominated by Robinia pseudoacacia

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(Leguminosae), Caragana korshinskii (Leguminosae), Hippophae rhamnoides

2.2 Soil sampling

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(Leguminosae).

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(Elaeagnaceae), Astragalus adsurgens (Leguminosae), and Medicago sativa

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In August 2016, a land-use survey was conducted in the Zhifanggou watershed. Land-use history, vegetation coverage, and topography were surveyed. Four land-use types, namely crop, forest, shrub, and grassland were selected for soil sampling. Four replicate plots were set up for each land-use type resulting in a total of 16 sampling plots. Each plot was 1 m × 1 m, and the distance between two plots was 20 m. More detailed information of the plots is shown in Table 1. A soil drill was used for soil sampling (depth: 0–20 cm) at each plot. All soil samples were transported to the laboratory, air dried at 25 ℃ for 2 weeks, and sieved through a 0.15-mm soil sieve. Soil organic C, inorganic C, total nitrogen, total phosphorus and bulk density were

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measured. Soil organic C was analyzed using a colorimetric determination method (Sims and Haby, 1971). Inorganic C was measured using the pressure calcimeter method (Sherrod et al., 2002). Total nitrogen was quantified using an elemental analyzer (Vario EL cube, ThermoFisher, Karlsruhe, Germany). Total phosphorus was extracted by microwave digestion and measured using inductively coupled plasma optical emission spectrometry. Bulk density was determined by taking undisturbed

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soil cores using a stainless-steel cutter (200 cm3 inner volume). The samples were

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Insert Table 1 here

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reweighed after cooling at 25 ℃.

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weighed before drying in an oven at 105 ℃ for 16 h. Then the samples were

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2.3 Soil respiration analysis

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On August 20–27, 2016 (rainy season) and April 22–29, 2017 (dry season), an Ultra-portable Gas Analyzer (Green Gas Analyzer, LICA United Technology Ltd,

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Beijing, China) and Soil Gas Flux System (SF3000, LICA United Technology Ltd, Beijing, China) were used for soil respiration monitoring every 3 h at each sample site for 48 h. To avoid the effects of weather conditions, the monitoring work was all carried out on sunny days. 2.4 Source analysis of soil respiration Soil respiration can be partitioned according to the differences in isotopic signatures among litter, roots, and soil microorganisms (Song et al., 2016). 2.4.1 CO2 and C source sampling At each site, PVC cylinders were inserted into the soil, sealed for 24 h, and gas

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samples were extracted with syringes and transferred to sampling bags. The sampling interval was 3 h, and the samples were collected with six replicates each time. The root and litter samples were collected at each site. Soil from a depth of 0–10 cm was collected with a soil drill (10 cm diameter) and stored in an ice box at 4 ℃. Plant roots, litter, and soil were transported to the laboratory for analysis of δ13C sources.

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2.4.2 Analysis of δ13C A Carbon Isotope Analysis (CCIA-36d-EP, Los Gatos Research, USA) was used to

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determine δ13C in the gas samples. Plant roots and litter were dried in an oven at 60 ℃

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for 24 h, ground in a mortar, and then sifted through a 0.18-mm sieve. The plant

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samples were burned in a Soil Total Carbon Analyzer (MultiN/C3100, Analytikjena,

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Germany). The burning gas was collected by air pocket to determine the δ13C values.

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The microbial C was extracted by soil microbial chloroform-fumigation extraction technology, and the value of soil microbial δ13C was measured by the abovementioned

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method for plant samples.

2.5 Analysis of C source of soil respiration A Bayesian stable isotope model was used to estimate the proportion of C sources of soil respiration (Kwon et al., 2019). The Stable Isotope Analysis package in R (SIAR) was used to incorporate sources of isotope fraction and multiple C sources (Zhang et al., 2018). This model estimated the relative proportions of each source in isotopic mixtures (Zhang et al., 2019). The Bayesian model was applied to determine the potential soil respiration contribution from three different sources: roots, litter, and soil microorganisms. The model can be expressed as follows (Shi et al., 2019d; Zhang

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et al., 2020): K

X ij   Pk (S jk  c jk )   ij

(1)

S j k ~ N (

(2)

k 1

j k,

c j k ~ N (

2

j k,

j )k

2

(3)

j )k

(4)

 i j ~ N ( 0,2 j )

Where, Xij is the isotope value j of the mixture i; Sjk represents the source value k of

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isotope j and is normally distributed with mean mjk and standard deviation jk; Pk is

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the proportion of source k; cjk is the fractionation factor for isotope j of source k and is

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normally distributed with mean jk and standard deviation tjk; and εij is the residual

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error representing the additional unquantified variation between individual mixtures

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and is normally distributed with mean zero and standard deviation sj. In this study,

2.6 Land-use data

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one isotopes (j = 1) (δ13C) was used.

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Land-use data were obtained from Landsat TM images in 2000 and 2015. The TM images have a spatial resolution of 30 m. The supervised classification method was used to map the land-use types. Five land-use types were identified, including cropland, forestland, shrubland, orchard and grassland. A field check conducted in August 2016 showed that the accuracy of land-use classification approached 96%. The area and percentage of each land-use type were counted using ArcGIS software. 2.7 Statistical analysis The Shapiro-Wilk method was used to test the normality, and all data were normally distributed. The effects of land-use type and seasonal changes on soil respiration and

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soil properties were analyzed by two way-ANOVA in SPSS16.0 software. The Tukey test was used to indicate whether parameters were different or similar (P < 0.05). 3. Results 3.1 Land-use change in the study area Before 2000, the main land-use type in the Zhifanggou watershed was cropland,

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accounting for 53.72% of the total area, which was evenly distributed throughout the watershed (Fig. 2). The areas of grass, shrub, and forestland were 2.84, 0.89, and 0.04

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km2, accounting for 33.97, 10.62, and 2.84% of the watershed area, respectively

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(Table 2). After 15 years of revegetation, grassland was the main land-use type in the

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area, accounting for 46.63% of the watershed area (Fig. 3). Shrubland increased by

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2.38 km2, accounting for 39.07% and was mainly distributed in the middle reaches of

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the basin. The forestland was 0.42 km2, accounting for 5.02% of the watershed area. The cropland decreased to 0.50 km2, accounting for 5.97% of the area.

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Insert Table 2 here

Insert Figure 3 here

3.2 Soil respiration changes in different land-use types during dry and rainy seasons Forest, shrub and grassland had higher soil organic C, inorganic C, total nitrogen and total phosphorus than cropland (Table 1). Soil organic C, inorganic C, total nitrogen were significantly higher in forestland than in crop, shrub and grassland (P < 0.05). Cropland had a significantly higher bulk density than forest, shrub or grassland (P < 0.05). The daily variation in the soil respiration rate during the dry and rainy seasons

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showed a single peak trend. From 8:00, the soil respiration rate increased, peaking from 11:00 to 14:00, while the low peak appeared from 2:00 to 5:00 (Fig. 4). The two way-ANOVA showed that seasonal change and land-use type significantly affected soil respiration (P < 0.05). Soil respiration in the rainy season was significantly higher than that in the dry season (P < 0.05). In the dry season, soil respiration was in the

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following order: shrub > crop > forest > grassland. Shrubland had a significantly higher (P < 0.05) soil respiration rate than the other land-use types in this season

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(Table 3). In the dry season, the maximum value of the soil respiration rate was

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observed in the shrubland, reaching 1.33 µmol m−2 s−1, and the minimum value was

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observed in the grassland, at only 0.29 µmol m−2 s−1. In the rainy season, performance

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was as follows: grass > forest > shrub > cropland. The soil respiration rate in grass

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and forestland was significantly higher than that in crop and shrubland in this season. The soil respiration rate in grassland during the rainy season was significantly higher

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than that in the other land-use types, with a mean value of 5.12 µmol m−2 s−1, and the maximum and minimum values were 6.51 and 4.01 µmol m−2 s−1, respectively. The average soil respiration of cropland during the rainy season was 1.56 µmol m−2 s−1, which was lower than that in other land-use types. Insert Table 3 here Insert Figure 4 here 3.3 Analysis of C sources from soil respiration The average δ13C value of CO2 released in the dry season was as follows: forest > shrub > grass > cropland (Fig. 5). The average value of δ13C in forestland reached

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average value of δ13C in grassland and forestland was −25.04 and −25.17‰, respectively, in this season.

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Insert Table 4 here

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Insert Figure 5 here

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In the dry season, δ13C from root respiration of cropland plants was −27.02‰, and

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the δ13C from soil microorganisms was −25.61‰ (the difference was significant at

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0.05 level, Table 5). The values of δ13C from litter in grass, shrub, and forestland were smaller than those of the root system and soil microorganisms (−27.95, −28.73, and

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−27.23‰, respectively). The highest δ13C was in forestland soil, reaching −22.63‰, while that in the root system was −22.70‰. In shrubland, δ13C from soil microorganisms (−23.08‰) was slightly higher than that from roots (−23.09‰). In grassland, root and soil microorganisms had δ13C values of −23.64 and −23.99‰, respectively. Insert Table 5 here In the rainy season, the δ13C values of the root system, litter, and soil microorganisms were lower than those in the dry season. In this season, the δ13C values of litter in forest, grass, and shrubland were lower than those of the roots and

Journal Pre-proof soil microorganisms. Shrub δ13C in root (−23.61‰) > soil microorganisms (−24.47‰) > litter (−29.21‰). The results of δ13C value in grassland were consistent with those in the shrubland: the root system had the highest δ13C, reaching −24.34‰, which was higher than that of soil microorganisms (−25.44‰) and litter (−29.22‰). In forestland, soil microorganisms had higher δ13C values than those of roots and

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litter. The source of CO2 from soil respiration was calculated using the Bayesian model.

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The results showed that C sources of soil respiration changed in different land-use

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types. Soil respiration in cropland mainly came from root respiration, which

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contributed 93.76 and 73.79% in the dry and rainy seasons, respectively (Table 6). In

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forest, shrub, and grassland, litter contributed more to soil respiration than roots and

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soil microorganisms. The maximum source of litter to soil respiration was found in forestland (68.02% in the dry season and 68.14% in the rainy season). The

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contribution of roots and soil microorganisms to soil respiration in grass and shrubland increased in the rainy season compared with that in the dry season, whereas the contribution of litter decreased. Roots contributed more to soil respiration than soil microorganisms in grass, shrub, and forestland in both dry and rainy seasons. Roots and soil microorganisms contributed differently to soil respiration in the land-uses types in terms of vegetation restoration. In both seasons, the contribution of roots to soil respiration in different land-use types was as follows: shrub > grass > forestland. The contribution of soil microorganisms to respiration in shrubland was higher than that in grass and forestland in both dry and rainy seasons. Litter

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contribution was in the following order: forest > grass > shrubland in both dry and rainy seasons. Insert Table 6 here 4. Discussion 4.1 Changes in soil respiration in different seasons and land-use types

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Forestland and grassland had significantly higher CO2 emission rates than cropland during the growing season, indicating that vegetation restoration increased soil

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respiration. Similar results were found by other studies. Shi et al. (2014) observed that

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CO2 emissions from forest and shrubland soils were higher than those from cropland

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soils. In the present study, the soil respiration rates of grass and forestland in the rainy

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season were higher than those of cropland. After vegetation restoration, soil organic C

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and nitrogen pools increased, and root system and microbial activity also improved, which contributed to soil respiration (Qin et al., 2019). In the growing season, grass

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and forestland had abundant root exudates and litters, and well-developed root systems increased soil respiration (Sheng et al., 2010). Raich and Tufekciogul (2000) compared the differences in soil respiration rates among different vegetation types and found that grassland had the largest soil respiration rate followed by forests and then farmland. In the dry season, the rate of soil respiration in shrubland was higher than that in the other land-use types, which was related to the presence of Caragana, which is planted in shrubland areas. Caragana begins to germinate in mid-April and was the earliest germinating plant in the study area. Caragana roots grew rapidly and respired

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strongly, increasing CO2 release from the soil during the dry season. The soil respiration rate of cropland in the dry season was higher than that in forest and grassland owing to the soil tillage in this period improving soil permeability, and the decomposition of dead roots contributed greatly to soil respiration. The grassland soil respiration rate was the lowest in the dry season, which may be due to the poor soil

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moisture conditions, and the soil CO2 release rate was limited by soil water characteristics. Moinet et al. (2016) found a strong positive correlation between soil

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water content and soil respiration. Soil respiration will be limited owing to

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physiological adjustments and microbial death when soil water content is too low

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(Moyano et al., 2013).

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Soil respiration rate was higher in the rainy season than in the dry season. Studies

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showed that the release rate of soil CO2 in winter in the Loess Plateau is only 11−25% of that in the growing season (Shi et al., 2014). Soil respiration is correlated with

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temperature and soil moisture (Köster et al., 2016; Parro et al., 2019). Temperature variation can explain most of the daily and seasonal variation in soil respiration. The soil temperature coefficient (Q10) is generally used to represent the sensitivity of soil respiration to temperature changes, i.e., the multiple of soil respiration increases for every 10 ℃ rise in temperature, and the Q10 value of terrestrial ecosystem is generally from 1.3–5.6 (Ding et al., 2016). The influence of soil moisture on soil respiration is also important, especially in arid and semi-arid Loess Plateau areas where soil moisture may become the limiting factor of plant growth and microbial activity, and thus affecting soil respiration. Davidson et al. (1998) used the linear equation model

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to simulate the relationship between soil moisture and soil respiration and found that soil respiration increased with increasing soil moisture (where soil moisture was < 0.12 cm3 cm-3, soil respiration = -128 + 285 × soil moisture, R2 = 0.48), but there was a threshold (where soil moisture was > 0.12 cm3 cm-3, soil respiration = 201 - 198 × soil moisture, R2 = 0.22). Similarly, Liu et al. (2002) found that soil respiration

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increased with soil moisture within a certain range. 4.2 C source of soil respiration changed with vegetation restoration

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Litter, plant roots, and soil microorganisms are the main sources of soil respiration

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(Bréchet et al., 2018; Epron et al., 1999). Litter, as the most active layer in soil

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systems, has important effects on soil and vegetation. Plants and microorganisms

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absorb nutrient elements released from organic matter decomposition. Therefore, most

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of the C in soil respiration initially comes directly from litter. It was previously reported that soil respiration significantly decreases after removing the litter layer

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(Buchmann, 2000). In our study, litter was the main source of soil respiration in forest, grass, and shrubland, contributing >40% to soil respiration over the whole year. The contribution rate of litter to soil respiration in different land-use types was as follows: forest > grass > shrubland. The amount of litter and cellulose content of different vegetation types are different, which affects on the decomposition of dead plant material. Moreover, the local environmental conditions (such as temperature, soil moisture, and soil physical and chemical structure) will also affect litter decomposition (Djukic et al., 2018). All these environmental factors will affect the decomposition of litter and consequently soil respiration (Chen et al., 2018b).

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However, owing to crop harvesting, the aboveground biomass of the cropland is rarely returned to the soil, leading to the root systems and soil microorganisms being the main sources of soil respiration (Zhang et al., 2015). Roots account for a large proportion of soil respiration (Li et al., 2018). The photosynthetic products of plants are transported to the root system and enter the soil

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through root metabolism and root exudates. As the “C sink” of photosynthetic products from aboveground parts, the root system is one of the important C sources of

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soil respiration (Hasibeder et al., 2015). The decomposition of root exudates and dead

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roots directly affects the content of soil organic matter and contributes to the source of

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soil respiration (Hanson et al., 2000). In the present study, the contribution of shrub

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root respiration to soil respiration was higher than that of grass- and forestlands. Root

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density and biomass were positively correlated with soil respiration. The root density of shrubland was higher than that for grassland or forestland in the surface soil. In

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addition, root exudates also have an “excitation effect” on the decomposition of the soil C pool, which can promote the decomposition of soil organic C (Kuzyakov and Chen, 2004).

Soil microorganisms are important sources of soil respiration. Soil microbial respiration is aerobic and is closely related to the CO2 flux during soil respiration. Bond-Lamberty et al. (2004) proposed the relationship model between soil respiration (Rs) and soil microbial respiration (Rh): ln(Rh) = 1.22 + 0.73 ln(Rs). Soil microbial biomass C accounts only for 3% of soil organic matter C. However, the reported proportion of soil microbial respiration in soil respiration ranges from 10–90%, and

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this proportion has significantly increased globally in the past 20 years (Bond-Lamberty et al., 2018). Soil microbial respiration is affected by environmental factors such as temperature and soil moisture, organic matter, and texture (Dacal et al., 2019; Qin et al., 2019). In the dry season, owing to the low air temperature and soil moisture, soil microorganism activity is low and respiration intensity is weak. In the

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rainy season, soil temperature and humidity are suitable for the growth of soil microorganisms, and soil microbial respiration was greatly improved. Active C pools

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in soil provide decomposition substrates for microorganisms and directly affect soil

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respiration. Soil texture is related to air diffusion in the soil, and not only affects O2

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acquisition in the process of root and microbial respiration but also affects the release

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rate of CO2 generated by biological respiration (Józefowska et al., 2017).

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4.3 Suggestions for land use management to increase soil C sink C cycle studies have found that the known C sink and C source cannot reach

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equilibrium, and there is a large missing C sink (Owens et al., 2018). The atmosphere and marine and terrestrial ecosystems are the three possible pools of CO2. The terrestrial ecosystem is the most complex and uncertain owing to the large amounts of C stored in vegetation and soil pools (Schimel et al., 2015). The mechanisms that affect the formation of terrestrial C sinks can be divided into two broad categories. The first is the physiological metabolic mechanism that affects plant photosynthesis and respiration, and microbial decomposition, including increased atmospheric CO2 concentrations, which increases nutrient availability (Ding et al., 2016). Changes in temperature and rainfall, and factors influencing plant growth and microbial activity,

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would affect terrestrial C pools (Qin et al., 2019). The second is the interference and recovery mechanism, which includes the direct effect of natural disturbance, land-use changes, and management strategies (Shi et al., 2014; Zhang et al., 2015). Land-use change is one of the main factors affecting the changes in terrestrial C sources and sinks (Barba et al., 2018).

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Many recent studies have reported that forest- and grassland management can affect the sources and sinks of CO2, methane, and nitrous oxide (e.g., Berryman et al., 2018;

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Raich and Tufekciogul, 2000). Ecosystem restoration and protection can store,

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maintain, and increase soil C pools. Much of the increase in terrestrial C absorption in

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North America can be attributed to the abandonment of farmland and revegetation

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following deforestation (Fan et al., 1998). In the present study, soil organic C

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significantly increased after the cropland was restored to vegetation (Table 1). Statistical data show that, from 1980 to the 2000s, soil organic C in China′s forest

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ecosystems exhibited a significant increasing trend with an annual growth rate of 20.0 g C m−2 yr−1 (Yang et al., 2014).

With the implementation of the “Grain for Green” project, vegetation is gradually being restored, and soil C sinks are increasing. By the end of 2020, the Chinese government aims to further expand the “Grain for Green” project on the Loess Plateau. Large-scale vegetation restoration will increase soil C sinks. In the process of vegetation restoration, suitable vegetation types should be selected (Yu et al., 2017). Since soil moisture on the Loess Plateau is the limiting factor for vegetation growth, and there is a trade-off between soil moisture and soil C sequestration (Feng et al.,

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2017). Therefore, it is necessary to coordinate ecosystem service trade-offs and achieve win-win outcomes in the implementation of ecological restoration (Zheng et al., 2019). Vegetation restoration projects should select native species because exotic species tend to have robust root systems that consume higher amounts of soil moisture.

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5. Conclusion Soil respiration showed seasonal and daily dynamic changes: soil respiration rate

ro

was higher in the rainy season than in the dry season, and higher during the day than

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at night. After the conversion of cropland to forest, shrub, and grassland, soil

re

respiration rate improved during the rainy season, and the sequence was as follows:

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grass > forest > shrub > cropland. Land-use change affected the sources of soil

na

respiration. C sources of soil respiration in cropland mainly came from roots, which contributed from 73.79–93.76%. The soil respiration of forest, shrub, and grassland

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mainly came from litter, which contributed from 47.77–68.14%. The contribution of soil microorganisms to soil respiration ranged from 6.24–26.21%. The results of this study could provide a theoretical basis for greenhouse gas emission reduction, soil C sink improvement, and land use optimization in the Loess hilly region. In future research, continuous soil respiration should be measured to quantitatively evaluate CO2 flux changes with vegetation restoration.

Acknowledgements This work was supported by the National Key Research and Development

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Programof China (Grant 2017YFC0504704); National Natural Science Foundation of China

(Grant

41601092);

National

Geological

Prospecting special

(Grant

DD20190504); Shaanxi Provincial Technology Innovation Guidance Project (Grant 2017CGZH-HJ-06) and the Shaanxi Province Innovation Talent Promotion Project

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Technology Innovation Team (Grant 2018TD-037).

References

ro

Adachi, M., Ito, A., Yonemura, S., Takeuchi, W. 2017. Estimation of global soil

-p

respiration by accounting for land-use changes derived from remote sensing data.

re

J. Environ. Manage. 200, 97–104. http://doi.org/10.1016/j.jenvman.2017.05.076.

lP

Assefa, D., Rewald, B., Sandén, H., Rosinger, C., Abiyu,A., Yitaferu, B., Godbold,

nitrogen

stock

na

D.L. 2017. Deforestation and land use strongly effect soil organic carbon and in

Northwest

Ethiopia.

Catena

153,

89–99.

Jo ur

https://doi.org/10.1016/j.catena.2017.02.003. Barba, J., Lloret, F., Poyatos, R., Molowny-Horas, R., Yuste, J. C. 2018. Multi-temporal influence of vegetation on soil respiration in a drought-affected forest. iForest. 11, 189. https://doi.org/10.3832/ifor2448-011. Berryman, E. M., Vanderhoof, M. K., Bradford, J. B., Hawbaker, T. J., Henne, P. D., Burns, S. P., Frank, J. M., Birdsey, R. A., Ryan, M. G. 2018. Estimating soil respiration in a subalpine landscape using point, terrain, climate, and greenness data. J. Geophys. Res. 123, 3231–3249. https://doi.org/10.1029/2018JG004613. Bréchet, L.M., Lopez-Sangil, L., George, C., Birkett, A.J., Baxendale, C., Trujillo,

Journal Pre-proof

B.C., Sayer, E.J. 2018 Distinct responses of soil respiration to experimental litter manipulation in temperate woodland and tropical forest. Ecol. Evol. 8, 3787–3796. https://doi.org/10.1002/ece3.3945. Bond-Lamberty, B., Wang, C., Gower, S. T. 2004. A global relationship between the heterotrophic and autotrophic components of soil respiration? Glob. Change Biol.

of

10, 1756–1766. https://doi.org/10.1111/j.1365-2486.2004.00816.x. Bond-Lamberty, B., Bailey, V. L., Chen, M., Gough, C. M., Vargas, R. 2018. Globally

ro

rising soil heterotrophic respiration over recent decades. Nature 560, 80.

-p

https://doi.org/10.1038/s41586-018-0358-x.

abies

stands.

Soil

Biol.

Biochem.

32,

1625–1635.

lP

Picea

re

Buchmann, N. 2000. Biotic and abiotic factors controlling soil respiration rates in

na

https://doi.org/10.1016/S0038-0717(00)00077-8. Cao, Z., Li, Y., Liu, Y., Chen, Y., Wang, Y. 2018. When and where did the Loess

Jo ur

Plateau turn “green”? Analysis of the tendency and breakpoints of the normalized difference vegetation index. Land Degrad. Dev. 29, 162–175. https://doi.org/10.1002/ldr.2852. Chen, Z., Xu, Y., He, Y., Zhou, X., Fan, J., Yu, H., Ding, W. 2018a. Nitrogen fertilization stimulated soil heterotrophic but not autotrophic respiration in cropland soils: A greater role of organic over inorganic fertilizer. Soil Biol. Biochem. 116, 253–264. https://doi.org/10.1016/j.soilbio.2017.10.029. Chen, X., Han, Y., Chen, H. 2018b. Global effects of plant litter alterations on soil CO2

to

the

atmosphere.

Glob.

Change

Biol.

24,

3462–3471.

Journal Pre-proof

https://doi.org/10.1111/gcb.14147. Dacal, M., Bradford, M. A., Plaza, C., Maestre, F. T., García-Palacios. P. 2019. Soil microbial respiration adapts to ambient temperature in global drylands. Nat. Ecol. Evol. 3, 232–238. https://doi.org/10.1038/s41559-018-0770-5. Davidson, E. A., Belk, E., Boone, R. D. 1998. Soil water content and temperature as

mixed

hardwood

forest.

of

independent or confounded factors controlling soil respiration in a temperate Glob.

Change

Biol.

4,

217–227.

ro

https://doi.org/10.1046/j.1365-2486.1998.00128.x.

-p

Ding, J., Chen, L., Zhang, B., Liu, L., Yang, G., Fang, K., Chen, Y., Li, F., Kou, D., Ji,

re

C., Luo, Y., Yang, Y. 2016. Linking temperature sensitivity of soil CO2 release to

lP

substrate, environmental, and microbial properties across alpine ecosystems. Biogeochem.

Cy.

30,

1310–1323.

na

Global

https://doi.org/10.1002/2015GB005333.

Jo ur

Djukic, I., Kepfer-Rojas, S., Schmidt, I. K., Larsen, K. S., Beier, C., Berg, B., Verheyen, K. 2018. Early stage litter decomposition across biomes. Sci. Total Environ. 628–629, 1369–1394. https://doi.org/10.1016/j.scitotenv.2018.01.012. Epron, D., Farque, L., Lucot, E., Badot, P.M. 1999. Soil CO2 efflux in a beech forest: the contribution of root respiration. Ann. Forest Sci. 56, 289–295. https://doi.org/10.1051/forest:19990403. Fan, S., Gloor, M., Mahlman, J., Pacala, S., Sarmiento, J., Takahashi, T., Tans, P. 1998. A large terrestrial carbon sink in north America implied by atmospheric and oceanic

carbon

dioxide

data

and

models.

Science

282,

442–446.

Journal Pre-proof

https://doi.org/10.1126/science.282.5388.442. Feng, Q., Zhao, W., Fu, B., Ding, J., Wang, S. 2017. Ecosystem service trade-offs and their influencing factors: A case study in the Loess Plateau of China. Sci. Total Environ. 607-608, 1250–1263. https://doi.org/10.1016/j.scitotenv.2017.07.079. Fu, B., Wang, S., Liu, Y., Liu, J., Liang, W., Miao, C. 2017. Hydrogeomorphic

of

China.

Annu.

Rev.

of

ecosystem responses to natural and anthropogenic changes in the Loess Plateau Earth

Planet.

Sci.

45,

223–243.

ro

https://doi.org/10.1146/annurev-earth-063016-020552.

Glob.

Change

re

analysis.

-p

Guo, L. B., Gifford, R. M. 2002. Soil carbon stocks and land use change: a meta Biol.

8,

345–360.

lP

https://doi.org/10.1046/j.1354-1013.2002.00486.x.

na

Hanson, P. J., Edwards, N. T., Garten, C. T., Andrews, J. A. 2000. Separating root and soil microbial contributions to soil respiration: A review of methods and Biogeochemistry

Jo ur

observations.

48,

115–146.

https://xs.scihub.ltd/https://doi.org/10.1023/A:1006244819642. Hasibeder, R., Fuchslueger, L., Richter, A., Bahn, M. 2015. Summer drought alters carbon allocation to roots and root respiration in mountain grassland. New Phytol. 205, 1117–1127. https://doi.org/10.1111/nph.13146. Llorente, M., Glaser, B., Turrión, M.B., 2010. Storage of organic carbon and black carbon in density fractions of calcareous soils under different land uses. Geoderma 159, 31–38. https://doi.org/10.1016/j.geoderma.2010.06.011. Li, X., Guo, D., Zhang, C., Niu, D., Fu, H., Wan, C. 2018. Contribution of root

Journal Pre-proof

respiration to total soil respiration in a semi-arid grassland on the Loess Plateau, China.

Sci.

Total

Environ.

627,

1209–1217.

https://doi.org/10.1016/j.scitotenv.2018.01.313. Liu, X., Wan, S., Su, B., Hui, D., Luo, Y. 2002. Response of soil CO2 efflux to water manipulation in a tallgrass prairie ecosystem. Plant Soil 240, 213–223.

of

https://doi.org/10.1023/A:1015744126533. Jian, J., Steele, M. K., Thomas, R. Q., Day, S. D., Hodges, S. C. 2018. Constraining

ro

estimates of global soil respiration by quantifying sources of variability. Glob.

-p

Change Biol. 24, 4143–4159. https://doi.org/10.1111/gcb.14301.

re

Joo, S., Park, S .U., Park, M. S., Lee, C. S. 2012. Estimation of soil respiration using

lP

automated chamber systems in an oak (Quercus mongolica) forest at the Sci. Total Environ. 416, 400–409.

na

Nam-San site in Seoul, Korea.

https://doi.org/10.1016/j.scitotenv.2011.11.025.

Jo ur

Józefowska, A., Pietrzykowski, M., Woś, B., Cajthaml, T., Frouz, J. 2017. Relationships between respiration, chemical and microbial properties of afforested mine soils with different soil texture and tree species: Does the time of incubation

matter.

Eur.

J.

Soil

Biol.

80,

102–109.

https://doi.org/10.1016/j.ejsobi.2017.05.004 Köster, K., Köster, E., Orumaa, A., Parro, K., Jõgiste, K., Berninger, F., Pumpanen, J., Metslaid, M. 2016. How time since forest fire affects stand structure, soil physical-chemical properties and soil CO2 efflux in hemiboreal Scots pine forest fire chronosequence? Forests 7, 201. https://doi.org/10.3390/f7090201.

Journal Pre-proof

Kwon, M. J., Natali, S. M, Hicks, Pries. C. E, Schuur, E.A., Steinhof, A., Crummer, K. G., Zimov, N., Zimov, S.A., Heimann, M., Kolle, O., Göckede, M. 2019. Drainage enhances modern soil carbon contribution but reduces old soil carbon contribution to ecosystem respiration in tundra ecosystems. Global Change Biol. 25,1315–1325. https://doi.org/10.1111/gcb.14578.

rhizosphere.

Plant

Soil

of

Kuzyakov, Y., Cheng, W. X. 2004. Photosynthesis controls of CO2 efflux from maize 263,

85–99.

ro

https://doi.org/10.1023/B:PLSO.0000047728.61591.fd.

-p

Machmuller, M.B., Kramer, M.G., Cyle, T.K., Hill, N., Hancock, D., Thompson, A.,

re

2015. Emerging land use practices rapidly increase soil organic matter. Nat.

lP

Commun. 6, 6995. https://doi.org/6, 6995. 10.1038/ncomms7995.

na

Moinet, G.Y.K., Cieraad, E., Hunt, J.E., Fraser, A., Turnbull, M.H., Whitehead, D. 2016. Soil heterotrophic respiration is insensitive to changes in soil water content

Jo ur

but related to microbial access to organic matter. Geoderma 274, 68–78. https://doi.org/10.1016/j.geoderma.2016.03.027. Moyano, F.E., Manzoni, S., Chenu, C. 2013. Responses of soil heterotrophic respiration to moisture availability: An exploration of processes and models. Soil Biol. Biochem. 59, 72–85. https://doi.org/10.1016/j.soilbio.2013.01.002. Owens, J. D., Lyons, T. W., Lowery, C. M. 2018. Quantifying the missing sink for global organic carbon burial during a Cretaceous oceanic anoxic event. Earth Planet. Sc. Lett. 499, 83–94. https://doi.org/10.1016/j.epsl.2018.07.021. Parro, K., Köster, K., Jõgiste, K., Seglinš, K., Sims, A., Stanturf, J. A., Metslaid, M.

Journal Pre-proof

2019. Impact of post-fire management on soil respiration, carbon and nitrogen content in a managed hemiboreal forest. J. Environ. Manage. 233, 371–377. https://doi.org/10.1016/j.jenvman.2018.12.050. Qin, S., Chen, L., Fang, K., Zhang, Q., Wang, J., Liu, F., Yu, J., Yang, Y. 2019. Temperature sensitivity of SOM decomposition governed by aggregate and

microbial

communities.

Sci.

Adv.

5,

1218.

of

protection

https://doi.org/10.1126/sciadv.aau1218.

ro

Raich, J. W., Tufekciogul, A. 2000. Vegetation and soil respiration: Correlations and

-p

controls. Biogeochemistry 48, 71–90. https://doi.org/10.1023/A:1006112000616.

carbon

cycle.

P.

Natl.

Acad.

Sci.

USA

112,

436–441.

lP

terrestrial

re

Schimel, D., Stephens, B.B., Fisher, J.B. 2015. Effect of increasing CO2 on the

na

https://doi.org/10.1073/pnas.1407302112. Sheng, H., Yang, Y., Yang, Z., Chen, G., Xie, J., Guo, J., Zou, S. 2010. The dynamic

Jo ur

response of soil respiration to land-use changes in subtropical China. Global Change Biol. 16, 1107–1121. https://doi.org/10.1111/j.1365-2486.2009.01988.x. Sherrod, L. A., Dunn, G., Peterson, G. A., Kolberg, R. L. 2002. Inorganic C analysis by modified pressure-calcimeter method. Soil Sci. Am. J., 66, 299–305. https://doi.org/10.2136/sssaj2002.2990. Shi, P., Zhang, Y., Li, P., Li, Z., Yu, K., Ren, Z., Xu, G., Cheng, S., Wang, F., Ma, Y. 2019a. Distribution of soil organic carbon impacted by land-use changes in a hilly watershed of the Loess Plateau, China. Sci. Total Environ. 652, 505–512. https://doi.org/10.1016/j.scitotenv.2018.10.172.

Journal Pre-proof

Shi, P., Zhang, Y., Zhang, Y., Yu, Y., Li, P., Li, Z., Xiao, L., Xu, G., Zhu, T. 2019b. Land-use types and slope topography affect the soil labile carbon fractions in the Loess

hilly-gully

area

of

Shaanxi,

China.

Arch.

Agron.

Soil

Sci.

https://doi.org/10.1080/03650340.2019.1630824. Shi, P., Zhang, Y., Ren, Z., Yu, Y., Li, P., Gong, J. 2019c. Land-use changes and check

of

dams reducing runoff and sediment yield on the Loess Plateau of China. Sci. Total Environ. 664, 984–994. https://doi.org/10.1016/j.scitotenv.2019.01.430.

ro

Shi, P., Zhang, Y., Song, J., Li, P., Wang, Y., Zhang, X., Li, Z., Bi, Z., Zhang, X., Qin,

-p

Y., Zhu, T. 2019d. Response of nitrogen pollution in surface water to land use

re

and social economic factors in the Weihe River watershed, northwest China.

lP

Sustain. Cities Soc. 50, 101658. https://doi.org/10.1016/j.scs.2019.101658.

na

Shi, W., Yan, M., Zhang, J., Guan, J., Du, S. 2014. Soil CO2 emissions from five different types of land use on the semiarid Loess Plateau of China, with emphasis

Jo ur

on the contribution of winter soil respiration. Atmos. Environ. 88, 74–82. https://doi.org/10.1016/j.atmosenv.2014.01.066. Sims, J.R., Haby, V.A., 1971. Simplified colorimetric determination of soil organic matter.

Soil

Sci.

112,

137–141.

https://doi.org/10.1097/00010694-197108000-00007. Song, W., Tong, X., Zhang, J., Meng, P. 2016. Three-source partitioning of soil respiration by

13

C natural abundance and its variation with soil depth in a

plantation. J. For. Res. 27, 533–540. https://doi.org/10.1007/s11676-015-0206-x. Villarino, S.H., Studdert, G.A., Baldassini, P., Cendoya, M.G., Ciuffoli, L.,

Journal Pre-proof

Mastrángelo, M., Piñeiro, G. 2017. Deforestation impacts on soil organic carbon stocks in the Semiarid Chaco Region, Argentina. Sci. Total Environ. 575, 1056–1065. https://doi.org/1056–1065. 10.1016/j.scitotenv.2016.09.175. Wang, S., Fu, B., Piao, S., Lü, Y., Ciais, P., Feng, X., Wang, Y. 2016. Reduced sediment transport in the Yellow River due to anthropogenic changes. Nat.

of

Geosci. 9, 38. https://doi.org/10.1038/ngeo2602. Wang, T., Li, P., Li, Z., Hou, J., Xiao, L., Ren, Z., Xu G., Yu K. Su, Y. 2019. The

the

Loess

Plateau,

China.

Sci.

Total

-p

on

ro

effects of freeze-thaw process on soil water migration in dam and slope farmland Environ.

666,

721–730.

re

https://doi.org/10.1016/j.scitotenv.2019.02.284.

lP

Xiao, L., Liu, G., Li, P., Xue, S. 2019. Direct and indirect effects of elevated CO2 and

Bothriochloa

na

nitrogen addition on soil microbial communities in the rhizosphere of ischaemum.

J.

Soil.

Sediment.

Jo ur

https://doi.org/10.1007/s11368-019-02336-0. Xu, G., Zhang, J., Li, P., Li, Z., Lu, K., Wang, X., Wang, F., Cheng, Y., Wang, B. 2018. Vegetation restoration projects and their influence on runoff and sediment in China. Ecol. Indic. 95, 233–241. https://doi.org/10.1016/j.ecolind.2018.07.047. Yao, X., Yu, K., Wang, G., Deng, Y., Lai, Z., Chen, Y., Jiang, Y., Liu, J. 2019. Effects of soil erosion and reforestation on soil respiration, organic carbon and nitrogen stocks in an eroded area of Southern China. Sci. Total Environ. 683, 98–108. https://doi.org/10.1016/j.scitotenv.2019.05.221. Yang, Y., Li, P., Ding, J., Zhao, X., Ma, W., Ji, C., Fang, J. 2014. Increased topsoil

Journal Pre-proof

carbon stock across China's forests. Global Change Biol. 20, 2687–2696. https://doi.org/10.1111/gcb.12536. Yu, K., Yao, X., Deng, Y., Lai, Z., Lin, L., Liu, J. 2019a. Effects of stand age on soil respiration in Pinus massoniana plantations in the hilly red soil region of Southern

China.

Catena

178,

313–321.

of

https://doi.org/10.1016/j.catena.2019.03.038. Yu, Y., Wei, W., Chen, L., Feng, T., Daryanto, S. 2019b. Quantifying the effects of

ro

precipitation, vegetation, and land preparation techniques on runoff and soil

-p

erosion in a Loess watershed of China. Sci. Total Environ. 652, 755–764.

re

https://doi.org/10.1016/j.scitotenv.2018.10.255.

lP

Yu, Y., Wei, W., Chen, L., Feng, T., Daryanto, S., Wang, L. 2017. Land preparation

na

and vegetation type jointly determine soil conditions after long-term land stabilization measures in a typical hilly catchment, Loess Plateau of China. J.

Jo ur

Soils Sediments 17, 144–156. https://doi.org/10.1007/s11368-016-1494-2. Yu, Y., Wei, W., Chen, L., Jia, F., Yang, L., Zhang, H., Feng, T. 2015. Responses of vertical soil moisture to rainfall pulses and land uses in a typical loess hilly area, China. Solid Earth 6, 595–608. https://doi.org/10.5194/se-6-595-2015. Zhang, Y., Shi, P., Song, J., Li, Q. 2019. Application of nitrogen and oxygen isotopes for source and fate identification of nitrate pollution in surface water: A review. Appl. Sci. 9, 18. https://doi.org/10.3390/app9010018. Zhang, Y., Shi, P., Li, F., Wei, A., Song, J., Ma, J. 2018. Quantification of nitrate sources and fates in rivers in an irrigated agricultural area using environmental

Journal Pre-proof

isotopes and a Bayesian isotope mixing model. Chemosphere 208, 493–501. https://doi.org/10.1016/j.chemosphere.2018.05.164. Zhang, Y., Guo, S., Liu, Q., Jiang, J., Wang, R., Li, N. 2015. Responses of soil respiration to land use conversions in degraded ecosystem of the semi-arid Loess Plateau. Ecol. Eng. 74, 196–205. https://doi.org/10.1016/j.ecoleng.2014.10.003.

of

Zhang, H., Kang, X., Wang, X., Zhang, J., Chen, G. 2020. Quantitative identification of nitrate sources in the surface runoff of three dominant forest types in China

based

on

Bayesian

model.

ro

subtropical

Sci.

Total

Environ.

-p

https://doi.org/10.1016/j.scitotenv.2019.135074.

re

Zhao, W., Zhang, R., Huang, C., Wang, B., Cao, H., Koopal, L.K., Tan, W. 2016.

lP

Effect of different vegetation cover on the vertical distribution of soil organic and

na

inorganic carbon in the Zhifanggou Watershed on the loess plateau. Catena 139, 191–198. http://doi.org/10.1016/j.catena.2016.01.003.

Jo ur

Zheng, H., Wang, L., Wu, T. 2019. Coordinating ecosystem service trade-offs to achieve win-win outcomes: A review of the approaches. J. Environ. Sci-China 82, 103–112. https://doi.org/10.1016/j.jes.2019.02.030.

Figure Captions

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Figure 1 Location of the study area and sample sites. Figure 2 The monthly precipitation and temperature for the Zhifanggou watershed in 2016. Figure 3 Land-use types of the study area in 2000 and 2015. Figure 4 Soil respiration changes in different land-use types in the dry and rainy seasons.

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Figure 5 Changes in δ13C from soil respiration in different land-use types in the dry

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and rainy seasons.

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Conflict of Interest Statement The authors declared that they have no conflicts of interest to this manuscript . We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the manuscript submitted. The contents of this manuscript are not now under consideration for publication

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elsewhere. Funding sources are listed in the a separate section of the manuscript and placed

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Peng Li on behalf of all authors.

-p

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before the References.

Journal Pre-proof Table 1 The sample site description. Slop e (°)

Altitud e (m)

Croplan d

Soybean

25

1125

30

1137

10

1159

26

1128

Forestla nd Shrublan d Grasslan d

Robinia pseudoacac ia Caragana korshinskii Medicago sativa

Soil total phosphor us (g kg-1)

Bulk Densit y (g cm-3)

12.71±1. 42 b

0.39±0. 10 c

0.50±0.0 6a

1.33± 0.14 a

10.31±0. 35 a

14.22±1. 58 a

0.65±0. 06 a

0.57±0.0 3a

1.17± 0.12 b

7.79 ± 0.49 b 7.56 ± 0.45 b

13.36±0. 94 b 13.30±0. 68 b

0.52±0. 05 b 0.42±0. 11 c

0.51±0.0 2a 0.59±0.0 5a

1.14± 0.15 b 1.08± 0.09 b

4.83±0.4 2c

of

Vegetation type

Soil total nitrogen (g kg-1)

ro

Land-us e type

Soil inorganic carbon (g kg-1)

Soil organic carbon (g kg-1)

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na

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re

-p

Values followed by the same letter are not significantly different at the 0.05 level among various land use types.

Journal Pre-proof Table 2 Land-use changes from 2000–2015. Forestland

Grassland

Orchard

4.49 53.72 0.50 5.97

0.89 10.62 3.27 39.07

0.04 0.50 0.42 5.02

2.84 33.97 4.07 48.63

0.10 1.20 0.11 1.31

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re

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of

Shrubland

na

2015

Area (km ) Percentage (%) Area (km2) Percentage (%)

Cropland

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2000

2

35

Journal Pre-proof Table 3 Variation in soil respiration rates in different land-use types in the dry and rainy seasons (µmol m−2 s−1). Land-use types Cropland Grassland Shrubland Forestland

Dry Season

Rainy Season

Mean

Min

Max

Mean

Min

Max

0.72 b 0.33 c 1.04 a 0.44 c

0.42 0.29 0.78 0.27

1.03 0.42 1.33 0.67

1.56 b 5.12 a 1.72 b 4.12 a

1.02 4.01 1.00 3.24

2.19 6.51 3.38 5.92

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na

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re

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Values followed by the same letter are not significantly different at the 0.05 level among various land use types. The bold season is significantly different at P < 0.05.

36

Journal Pre-proof Table 4 The variance of δ13C from soil respiration in different land-use types in the dry and rainy seasons (‰). Land-use types Cropland Grassland Shrubland Forestland

Dry Season

Rainy Season

Mean

Min

Max

Mean

Min

Max

-26.81 b -24.31 a -24.15 a -23.86 a

-27.24 -25.03 -24.75 -24.80

-26.27 -23.33 -23.08 -23.23

-27.13 b -25.04 a -24.31 a -25.17 a

-27.98 -25.65 -24.89 -26.02

-26.54 -24.35 -23.38 -24.39

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na

lP

re

-p

ro

of

Values followed by the same letter are not significantly different at the 0.05 level among various land use types.

37

Journal Pre-proof Table 5 Variation in δ13C from root, litter, and soil microorganisms in different land-use types in the dry and rainy seasons (‰). Dry Season

Rainy Season

Land-use types

Root

Litter

Soil microorganism

Root

Litter

Soil microorganism

Cropland Grassland Shrubland Forestland

-27.02 b -23.64 a -23.09 a -22.70 a

/ -27.95 a -28.73 a -27.23 a

-25.61 b -23.99 a -23.08 a -22.63 a

-26.65 b -24.34 a -23.61 a -24.07 a

/ -29.22 a -29.21 a -28.55 a

-26.70 b -25.44 ab -24.47 a -23.84 a

Jo ur

na

lP

re

-p

ro

of

Values followed by the same letter are not significantly different at the 0.05 level among various land use types.

38

Journal Pre-proof Table 6 Carbon sources of soil respiration in different land-use types in the dry and rainy seasons (%). Rainy Season

Litter

Soil microorganism

Root

Litter

Soil microorganism

93.76 25.02 28.57 19.96

/ 58.52 51.73 68.02

6.24 16.45 19.69 12.01

73.79 26.74 29.53 19.96

/ 47.77 41.64 68.14

26.21 25.49 28.83 11.90

lP

re

-p

ro

of

Root

na

Cropland Grassland Shrubland Forestland

Dry Season

Jo ur

Land-use types

39

Journal Pre-proof Highlights 1. Soil respiration in the rainy season was higher than that in the dry season. 2. Vegetation restoration increased soil respiration in the rainy season. 3. Roots were the main source of soil respiration in cropland.

Jo ur

na

lP

re

-p

ro

of

4. Litter was the most important source to soil respiration in the vegetation land.

40

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5