Optimizing the spatial pattern of land use types in a mountainous area to minimize non-point nitrogen losses

Geoderma 360 (2020) 114016

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Optimizing the spatial pattern of land use types in a mountainous area to minimize non-point nitrogen losses

T

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Xiaoming Laia,b, Qing Zhua,b,c, , Zhiwen Zhoua,b, Kaihua Liaoa,b, Ligang Lvd a

Key Laboratory of Watershed Geographic Sciences, Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, PR China University of Chinese Academy of Sciences, Beijing 100049, PR China c Jiangsu Collaborative Innovation Center of Regional Modern Agriculture & Environmental Protection, Huaiyin Normal University, Huaian 223001, PR China d School of Public Administration, Nanjing University of Finance and Economics, Nanjing 210023, PR China b

A R T I C LE I N FO

A B S T R A C T

Handling Editor: Jan Willem Van Groenigen

Spatial patterns of land use types (mixed forest: MF, tea garden: TG, bamboo forest: BF) in a mountainous area with rapid agricultural land development were optimized to reduce NO3−-N leaching and N2O emission in this study. Firstly, a process-oriented biogeochemical model (Denitrification Decomposition, DNDC) was calibrated and validated on representative MF, TG and BF hillslopes. To upscale the simulations, hydropedological function (HPF) units were generated by overlapping maps of land use, soil organic carbon, rock fragment content and slope, which were recognized as critical factors affecting soil N cycle. The calibrated DNDC models were then adopted to simulate the soil N cycles in different HPF units and assess temporal and spatial variations of NO3−-N leaching and N2O emission risks. Lastly, spatial allocations of TG were determined respectively for minimizing NO3−-N leaching (MLN_LU) and N2O emission (MEN_LU), and balancing the reductions of both (BRN_LU). Results showed that the DNDC model had acceptable accuracies on these representative hillslopes (R2 > 0.50, NSE > 0.30). Land use of TG had the greatest N loss risks, the mean annual NO3−-N leaching and N2O flux in TG HPF units (72.11- and 3.63- kg N ha−1, respectively) were respectively 2.61 and 2.50 times of those of the entire study area. Temporal variations of NO3−-N leaching and N2O flux were both controlled by the timing of precipitation, and their spatial patterns were both primarily controlled by land use and then respectively by soil hydraulic properties (NO3−-N leaching) and soil carbon and N concentrations (N2O flux). Due to the relatively small spatial variations of soil and terrain properties and rational land use spatial patterns in this study area, the MLN_LU only reduced 7.6% of NO3−-N leaching, the MEN_LU only reduced 6.0% of N2O flux, and the BRN_LU reduced both by 3.3% and 4.1%, respectively. This study emphasized the role of land use pattern optimization in reducing the non-point N losses in mountainous area, and could provide scientific guidelines for future agricultural land developments.

Keywords: DNDC model Nitrogen cycles Hydropedological function Tea garden Taihu basin

1. Introduction Agricultural non-point nitrogen (N) losses through solute transport (e.g., NO3−-N leaching) and gaseous emission (e.g., N2O emission) can deteriorate water quality, deplete ozone, and cause global warming (Fowler et al., 2013; Klaus et al., 2018; Ouyang et al., 2017). Multiple auxiliary factors associated with the N availability and soil condition (e.g., soil and topographic properties, climatic variables) can interactively alter N loss processes (Ouyang et al., 2017; Palta et al., 2016; Zhu et al., 2018). Among these factors, land use is recognized as one of the primary factors determining the spatio-temporal variations of N losses, especially in the mountainous areas with agricultural development and intensification (Cameron et al., 2013; Foley et al., 2005; Klaus

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et al., 2018). Therefore, reducing agricultural N losses from the perspective of optimizing land use patterns has practical significance in the mountainous areas. Influences of land use types on N losses can be interpreted from two aspects. First, different plants have their specific physiological characteristics, such as water use efficiency, N use efficiency and fixation capacity, which could change the soil condition for microbial activities and soil N available for losses (Compton and Boone, 2000; Fu et al., 2010). Second, the ways and degrees of human activities disturbing soil N cycles varies among different land use types (Klaus et al., 2018). For example, numerous studies have indicated that excessive N fertilizer application was observed in agricultural land and was a critical factor inducing non-point N pollution (e.g., Cao et al., 2018; Fowler et al.,

Corresponding author at: Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, PR China E-mail address: [email protected] (Q. Zhu).

https://doi.org/10.1016/j.geoderma.2019.114016 Received 8 July 2019; Received in revised form 12 October 2019; Accepted 15 October 2019 0016-7061/ © 2019 Elsevier B.V. All rights reserved.

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Fig. 1. General information of the study area, including: (a) location of the Qing Hill, (b) the distribution of soil sampling sites and three representative land use hillslopes (tea garden, bamboo forest and mixed forest), (c) the distribution of observation sites on these three representative hillslopes, and (d) photographs of representative hillslopes and installments of zero-tension lysimeters and closed chamber.

with N losses (Cameron et al., 2013), some areas with better N retention may be more suitable to be developed into agricultural lands than the others. Therefore, spatially allocating proper land use types according to the comprehensive endowments of the land can be another feasible strategy to reduce N loss, especially at the watershed and regional scales (Fu et al., 2010). Optimizing the spatial pattern of land use types to reduce non-point source pollution is a thorny problem since few pieces of research have been conducted. To fulfill this, two major challenges need a breakthrough. One is the upscaling. As both experimental measurements and modelling simulations are generally conducted at field scale (e.g., Beaudoin et al., 2005; Li et al., 1992; Ruffatti et al., 2019; Yuan et al., 2011), the upscaling is inevitable for evaluating N loss risks at large scales. The concept of hydropedological function (HPF) units proposed by Lin (2011) can be promising in the upscaling scheme. Based on this concept, the large study area can be partitioned into different HPF units

2013; Yan et al., 2018). In addition, other agricultural management practices (e.g. irrigation and tillage) can also trigger non-point N losses (e.g., Trolove et al., 2019; Uribe et al., 2018). Comparatively, low N loss risk has been observed in forested lands (Perakis and Hedin, 2002). Cameron et al. (2013) concluded the order of N leaching risks in different land use types as: forested < cut grassland < grazed grassland, arable cropping < ploughing of pasture < horticultural crops. Correspondingly, different strategies have been applied to reduce non-point N losses. One of them is adopting rational agricultural management practices. Previous studies have demonstrated that explicit strategies, including optimizing the N fertilizer application rates (Ju et al., 2009; Ruffatti et al., 2019), applying the nitrification or urease inhibitors (Wang et al., 2019), adopting reasonable irrigation regimes (Zhuang et al., 2019), improving crop rotation systems (Beaudoin et al., 2005), would reduce the agricultural N losses. Due to that the spatial variations of soil, topography and climate are closely associated

2

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camphora (L.) Presl.). The elevation ranges from 41- to 101-m and the slope of the watershed ranges from 0° to 45°. The soil type is shallow lithosols according to the FAO soil classification (orthents according to Soil Taxonomy), and soil texture is silt loam with mean silt content > 70%. The depth to bedrock varies from < 0.3 m in the ridges to > 1.0 m in the valleys, and the parent material is weathered quartz sandstone with high water permeability.

Table 1 Statistical summaries of soil properties and terrain attributes at the 123 soil sampling sites. SOC: soil organic carbon content; SD: standard deviation; CV: coefficient of variation. Properties −1

Sand (g g ) Silt (g g−1) Clay (g g−1) SOC (g g−1) NO3−-N (mg kg−1) NH4+-N (mg kg−1) Bulk density (g cm−3) Rock fragment (m3 m−3) Slope (°) Elevation (m)

Min

Max

Mean

SD

CV (%)

0.01 0.52 0.09 0.004 1.34 1.38 0.96 0.00 0.99 46.71

0.39 0.89 0.23 0.040 149.52 398.56 1.56 0.47 32.05 98.38

0.11 0.75 0.14 0.016 11.96 17.07 1.33 0.17 8.32 62.59

0.08 0.07 0.03 0.007 19.85 46.59 0.12 0.13 5.00 9.55

75.05 9.38 18.16 46.401 165.90 272.95 9.15 80.73 60.18 15.26

2.2. Data collection In the study area, 123 sampling sites were arranged with a spatial interval of about 200 m and covering all land use types (Fig. 1b). After removing the litter layer, three soil cores (100 cm3) were collected at the surface of each site and oven-dried (105 °C for 24 h) to measure the bulk density. As low vertical variations in the upper 20 cm, these measured bulk densities were used to represent the soils in upper 20-cm depth. At each site, three soil samples (within 1 m distance) at the depth of 0–20 cm were collected and fully mixed. After air dried, ground and sieved through a 2-mm polyethylene sieve, particles larger than 2 mm were weighted to determine the gravimetric rock fragment content (RFC) (Poesen and Lavee, 1994). The volumetric RFC was then obtained based on the gravimetric RFC, soil bulk density and the density of rock fragments (2.54 g cm−3) (Lai et al., 2018). Particles passed through the 2 mm sieve were then used to determine the contents of clay (< 0.002 mm), silt (0.002–0.05 mm) and sand (0.05–2 mm), soil organic carbon (SOC), and NO3−-N and NH4+-N concentrations. The elevation and slope were determined from a digital elevation model (DEM) with 5 m spatial resolution in ArcGIS 10.0 (ESRI, Redlands, CA). The statistic summaries of the terrain and soil properties based on the 123 sampling sites were shown in Table 1. Three representative land use (MF, TG and BF) hillslopes were selected in this study area (Fig. 1b). For the TG and BF hillslopes, four observation sites were assigned along slope transects (TG01, TG02, TG03 and TG04; BF01, BF02, BF03 and BF04), while one observation site (MF01) was assigned on the MF hillslope (Fig. 1c). Soil samples at 0–20 cm depth of these observation sites were collected to determine the RFC, contents of clay, silt and sand, SOC, soil NO3−-N and NH4+-N concentrations (Table 2). Elevations and slopes of these observation sites were obtained from a local elevation survey with 1 m spatial resolution (Lai et al., 2018). Soil water content and soil temperature at 10-cm depth of these observation sites were measured using EC-5 and MPS-6 sensors (METER Group, Inc., Pullman WA, USA). Weather stations (METER Group, Inc., Pullman WA, USA) were installed on these three hillslopes to record the air temperature and throughfall under the canopies. All these measurements were collected with a frequency of 5 min, and the daily averaged data were used. Around each observation site, three zero-tension lysimeters and three closed chambers were installed to respectively collect the soil leachates (below 30-cm depth) and gas samples once or twice per month from April 2016 to March 2018. The leachate NO3−-N concentrations and the N2O concentrations of gas samples were measured respectively using the continuous flow analyzer (San++, Skalar, Breda, The Netherlands) and the gas chromatograph (7890B, Agilent Technologies, Santa Clara, California, USA). The N2O flux was then calculated according to the linear relationships of the N2O concentrations at four sampling times (0-, 10-, 20-, and 30- min after closure). The averaged values of these three measured NO3−-N concentrations and N2O fluxes per site were used as the final measurements. Totally, measurements of N2O fluxes were made on 28 dates, while due to the dry conditions in some sampling dates, only 23 measurements of NO3−N concentrations were made. Detailed descriptions about the measurements were presented in Liao et al. (2019).

according to the spatial characteristics of critical auxiliary factors controlling N losses. The critical parameters of N loss can be upscaled by constructing empirical functions between these parameters and the auxiliary topographic, pedologic and hydrologic properties in different HPF units, and then incorporated into large scale models (Zhu et al., 2018). Another obstacle is identifying areas with low risks of N losses. This can be referred to the concept of biogeochemical hot/cold spots, which are defined as the areas that show disproportionately high/low N loss rates relative to the other areas (McClain et al., 2003). Thereby, to minimize the N losses, land use types with low risks are preferred to be arranged in hot spots, while land use types with high N loss risks are suitable to be allocated in the cold spots. Specifically, as the hot spots of N loss are generally observed in regions with coarse soil texture and steep slope (Cameron et al., 2013), thus these regions are not suitable for agricultural development. Under the background of intensive agricultural land development in the southeast mountainous areas of China, tea plantation has been rapidly expanding at the expense of natural forest (e.g., mixed forest or MF, bamboo forest or BF) (Xu et al., 2017; Yan et al., 2018). As a land use type associated with excessive N fertilizer and manure inputs (around 400–500 kg N ha−1 yr−1 in China), tea garden (TG) poses severe non-point N pollution (Yan et al., 2018). Therefore, reducing N losses by optimizing the spatial pattern of TG without sacrificing tea yields (by maintaining its area) is necessary. Selecting a representative mountainous area (Qing Hill) in China as the study area, and combining the observations with simulations, objectives of this study are to: (i) upscale the hillslope observed and simulated N loss data to the whole study area; (ii) assess the risks of N losses in the study area and their controlling factors; and (iii) determine the optimized spatial patterns of land use types to minimize the N losses. By comparing the N loss risks between the actual and optimized land use patterns, guidance of properly allocating land use types (e.g., tea plantation) can be developed. In addition, approaches (including modelling and upscaling) can be proposed to determine the optimized land use patterns for controlling N losses. 2. Materials and methods 2.1. Study mountainous area The Qing Hill (119°2.42′–19°4.48′E, 31°20.72′–31°23.33′N) is located in the northwest margin of Taihu Lake Basin (Fig. 1a). This study area has an area of 747.33 ha and belongs to the transition zone between north and middle subtropical monsoon climate area. The annual mean temperature and precipitation over the period from 2008 to 2018 are 16 °C and 1120 mm, respectively. Coniferous and broad-leaved MF, TG (Camellia sinensis (L.) O. Kuntze) and BF (Phyllostachys edulis (Carr.) H. de Lehaie) are primary land use types of this area. The dominant plant species for MF are Masson pine (Pinus massoniana), Sawtooth Oak (Quercus acutissima Carruth.), and Camphor tree (Cinnamomum

2.3. The generation of HPF units In this study, land use types, SOC, RFC and slope were used to 3

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Table 2 Spatial means of parameters on the three representative land use hillslopes (MF: mixed forest; TG: tea garden; BF: Bamboo forest) and in the 55 hydropedological function (HPF) units. These parameters were used in DNDC model. Ks: soil saturated hydraulic conductivity; SOC: soil organic carbon content; SD: standard deviation; CV: coefficient of variation. Properties

Validation hillslope

−3

Bulk density (g cm ) Wilting point (m3 m−3) Field capacity (m3 m−3) Clay (g g−1) Ks (m hr−1) Porosity (m3 m−3) SOC (g g−1) NO3−-N (mg kg−1) NH4+-N (mg kg−1) Rock fragment (m3 m−3) Slope (°)

55 HPF units

MF

TG

BF

Min

Max

Mean

SD

CV (%)

1.35 0.06 0.22 0.12 0.008 0.36 0.014 10.15 17.76 0.26 0.13

1.41 0.07 0.22 0.16 0.008 0.33 0.011 23.06 28.07 0.28 7.06

1.39 0.06 0.24 0.12 0.008 0.36 0.013 14.94 16.40 0.23 4.24

1.23 0.06 0.21 0.13 0.008 0.33 0.010 5.49 5.09 0.07 2.03

1.42 0.08 0.28 0.17 0.010 0.43 0.023 23.55 25.41 0.28 17.65

1.35 0.07 0.25 0.15 0.008 0.39 0.015 9.48 9.43 0.16 8.51

0.05 0.01 0.02 0.01 0.000 0.03 0.003 3.91 3.21 0.06 3.48

3.74 7.69 6.43 5.65 5.775 6.85 22.163 41.23 34.07 36.53 40.94

Note. The minimum value for Ks in DNDC model was 0.008 m hr−1. Therefore, 0.008 m hr−1 was used when the predicted Ks was smaller than it.

surface soil parameters could be used to assess N loss risks of the entire soil profiles. When simulating soil N cycles on each representative hillslope, the soil hydraulic parameters (SHPs) (including wilting point, field capacity, porosity and saturated hydraulic conductivity or Ks) were predicted by ROSETTA (Schaap et al., 2001) with the inputs of sand, silt and clay, and then corrected by RFC. This method has been confirmed having good performances in characterizing SHPs in this study area and details of it can be referred to Lai et al. (2018). Other parameters, including bulk density, SOC, soil NO3−-N and NH4+-N concentrations, and slope were assigned according to measurements. The pH values were set as 6.5 for TG, and 6.0 for MF and BF according to our measurements. For each hillslope, mean values of soil properties and slopes of different observation sites were adopted in the modelling (Table 2). Plant physiological parameters of green tea and bamboo provided by Liao et al. (2019) and Zhang et al. (2016) were used on TG and BF hillslopes, respectively (Table 3). Plant physiological parameters for MF were derived from the pine and oak provided by Forest-Denitrification Decomposition (Li et al., 2000), and supplemented by the measured data from Yang (2004) (Table 3). On the TG hillslope, spring and basal fertilizers were applied in mid-March (urea: 209 kg N ha−1) and late October (urea: 174 kg N ha−1; organic fertilizer: 120 kg N ha−1), respectively. The N fertilizer was applied by digging shallow trenches with about 10-cm depth. No fertilizer was applied for MF and BF. The simulated N fluxes should be corrected by the volume of rock fragment (Liao et al., 2019). This was due to that rock fragments would occupy a certain volume in the soil profile, thus reduced the soils participated in N cycle, while in regular model simulation, the rock fragments were ignored and thus yielded unreasonable results (Liao et al., 2019). Validations of the soil climate simulations of DNDC model were conducted by comparing with the measured soil water content and soil temperature at 10-cm depths on each hillslope. In addition, observed leachate NO3−-N concentrations and N2O fluxes at different observation sites were used to validate the soil N simulation. The determination coefficient (R2) and Nash-Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970) were used to evaluate accuracies. The calibrated DNDC models on these three representative hillslopes were applied to simulate the N cycles in different HPF units. Climate data, plant physiological parameters and management practices adopted in a specific HPF unit were the same as those used on the representative hillslope with the same land use type. The same rate and timing of N fertilizer applications as the representative TG hillslope were used in different TG HPF units, although they could be slight varied in different TG fields. The spatial means of SHPs, soil properties and slope with in each HPF unit were used in the simulation (Table 2). Likewise, the simulated soil N losses in each HPF unit were corrected by

generate the HPF units, as these factors had large spatial variations and had critical influences on soil N losses according to previous studies (e.g., Klaus et al., 2018; Li et al., 2000; Liao et al., 2019). Firstly, land use types in the area were extracted based on QuickBird images using ENVI software (Exelis Visual Information Solutions, Inc., Boulder, CO, USA) (Fig. 2a). After that, the study area was divided into 30 sub-regions according to the interpreted land use types and the land use types in these sub-regions were verified by field survey one by one. Land for roads (occupied a small area) was merged into the adjacent land use types. In addition to the MF, TG and BF, the other land use types in this area (construction land and ponds) were excluded in the analysis. Secondly, spatial distributions of SOC and RFC were acquired by interpolating the 123 soil sampling sites using kriging interpolation in ArcGIS 10.0. As the low ratios (< 0.5) of sample spacing over spatial correlation range for the SOC and RFC, we selected ordinary kriging instead of regression kriging in spatial interpolations (Zhu and Lin, 2010). Maps of SOC and RFC were reclassified by quantile classification method based on their statistical distribution characteristics. Three categories of SOC (≤ 0.013 g g−1, 0.013–0.017 g g−1, and ≥ 0.017 g g−1) and RFC (≤ 0.13 m3 m−3, 0.13–0.18 m3 m−3, and ≥ 0.18 m3 m−3) were used in this study (Fig. 2b, c). The slope derived from the 5-m resolution DEM was reclassified into four categories (≤ 3°, 3−8°, 8−15°, and ≥ 15°) based on Standards for Classification and Gradation of Soil Erosion (MWRC, 2008) and its statistical characteristics (Fig. 2d). Finally, by superposing maps of land use, SOC, RFC and slope, HPF units were generated for the study area (Fig. 2e). To avoid highly fragmented HPF units in the study area, HPF units less than 1000 m2 were merged into the adjacent large area HPF units.

2.4. Simulation of soil N cycle In this study, the Denitrification Decomposition (DNDC) model (Li et al., 1992) was used to simulate the soil N cycles. Daily maximum and minimum temperature and throughfall monitored on the MF, TG and BF hillslopes from 2013 to 2018 were adopted to run the model for the corresponding land use types (Fig. 3). The first three years (2013–2015) were used for model spin-up, and the last three years (2016–2018) were used for the model validation and subsequent analyses. Spin-up duration of 3 years was sufficient to reduce the influences of initial soil conditions in this study, as the soil carbon and N contents were all field measured during the simulation period (Perlman et al., 2013). Since only surface soil parameters were required in DNDC model, the measured or predicted soil parameters of the upper 20-cm depth were applied to run the model. As the deep soil N content was low and nearly invariable over a long time scale, N losses were generally derived from the surface soil N. Therefore, the simulated N losses with the inputs of 4

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Fig. 2. Generation of hydropedological function (HPF) units, including (a) land use (LU) map, categorized maps of (b) soil organic carbon (SOC), (c) rock fragment content (RFC) and (d) slope, and (e) spatial distribution of the 55 HPF units. Mixed forest, tea garden and bamboo forest were respectively abbreviated as MF, TG and BF. Three categories of SOC (≤ 0.013, 0.013–0.017 and ≥ 0.017, unit: g g−1) and RFC (≤ 0.13, 0.13–0.18 and ≥ 0.18, unit: m3 m−3) were all denoted as “01”, “02” and “03”, respectively. The black patch represents the other land use types except MF, TG and BF.

calibrated DNDC model for TG. Orders of NO3−-N leaching and N2O fluxes of all HPF units were then acquired from lowest to highest under this assumption. The optimal spatial distribution of TG was identified by minimizing the NO3−-N leaching or N2O emission, or balancing the reductions of both losses. To minimize the NO3−-N leaching, HPF units with low NO3−-N leaching risks were considered to be suitable for TG. To minimize the N2O emission, the same criteria was used. To balance the reductions of both, orders of NO3−-N leaching and N2O fluxes were firstly summed for each HPF unit, then those units with low numbers of summed orders were considered suitable for TG. In addition, no matter which criteria was used, the total area of TG after optimization were maintained the same as the current. If a MF or BF HPF unit was not

its mean RFC. In addition, since small rate of N loss through surface runoff relative to that through leaching were observed in this study area (proportions < 0.5%), we did not consider it in this study. Only the NO3−-N leaching and N2O fluxes were considered since they were two major pathways (solute and gaseous) of N losses (Fowler et al., 2013; Uribe et al., 2018). 2.5. Approach for optimizing spatial patterns of land use types At first, we made an assumption that the entire study area had a single land use type of TG. Then, spatio-temporal variations of NO3−-N leaching and N2O fluxes in all HPF units were simulated by the 5

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Fig. 3. Temporal variations of the maximum and minimum daily temperature and throughfall under vegetation canopies on (a) mixed forest (MF), (b) tea garden (TG), and (c) bamboo forest (BF) hillslopes during the simulation period (2013–2018).

Specifically, large coefficients of variations (CVs) were observed for the sand, SOC, soil NO3−-N and NH4+-N concentrations, RFC, and slope (CVs > 45%). The CVs of soil NO3−-N and NH4+-N concentrations (165.90% and 272.95%, respectively) were the greatest among all considered soil and terrain properties. Fifty-five HPF units were generated with disparate land use types, soil and terrain properties (Table 2 and Fig. 2). The mean area of the 55 HPF units was 12.87 ha. The largest HPF unit had an area of 64.57 ha with land use type of MF, while the smallest HPF units was 0.47 ha with the land use of TG. The MF HPF units had an area of 409.92 ha (accounted for 54.85% of the whole area), and was generally distributed in the west and north parts (Fig. 2a). The TG occupied 28.95% (216.33 ha) of the study area and was mainly distributed in the south and east parts, while the BF occupied 10.92% (81.64 ha) and was sparsely distributed in the study area (Fig. 2a). Low SOC (< 0.013 g g−1) was mostly observed in the east of the study area, while high SOC (> 0.017 g g−1) was generally observed in the northwest and south parts (Fig. 2b). Low RFC (< 0.013 m3 m−3) was mainly found in the east and northwest parts, while high RFC (> 0.018 m3 m−3) was mainly observed along the ridges (Fig. 2c). The slope of the study area generally fell in the categories of 3−8° and 8−15°. Areas with slopes < 3° or > 15° were in the northwest margin or sparsely located in the west parts (Fig. 2d).

Table 3 The plant physiological parameters for the mixed forest (MF), tea garden (TG) and bamboo forest (BF) used in DNDC models. DM: dry matter. Plant physiological parameters

Max biomass Biomass fraction of grain Biomass fraction of leaf Biomass fraction of stem Biomass fraction of root C/N in grain C/N in leaf C/N in stem C/N in root Water requirement Nitrogen fixation index Optimum temperature

Units

Land use types

−1

−1

yr kg C ha / / / / / / / / g water g−1 DM / °C

MF

TG

BF

26608.4 0.02 0.03 0.79 0.16 93.0 35.0 200.0 150.0 185.0 1.0 25.0

1000.0 0.02 0.50 0.20 0.28 15.0 15.0 25.0 35.0 550.0 1.0 30.0

29278.8 / 0.03 0.69 0.28 / 30.6 221.7 146.0 400.0 1.0 15.0

changed to TG in the optimization processes, its original land use type was kept. If a TG HPF unit was considered not suitable for TG in the optimization, it was assigned as the MF or BF land use type by minimizing the N losses and maintaining the area of MF or BF as actuality. Thereby, four scenarios of land use spatial patterns were considered and risks of N losses were determined. These scenarios were: the actual spatial pattern of land use types (ASP_LU), the spatial patterns of land use types determined respectively by minimizing NO3−-N leaching (MLN_LU) and N2O emission (MEN_LU), and by balancing the reductions of both (BRN_LU).

3.2. Calibrations and validations of DNDC model for representative hillslopes With appropriate inputs of soil and plant physiological parameters, the soil climate, NO3−-N leaching and N2O flux on the MF, TG and BF hillslopes could be reasonably simulated by DNDC model. Firstly, acceptable performances were achieved in simulating the soil water content and soil temperature at 10-cm depth. The R2 values in simulating the soil water content at 10-cm depth were > 0.65 for all hillslopes, and the NSE were all > 0.60. In addition, the R2 values in simulating the mean soil temperature at 10-cm depth were > 0.85 for all hillslopes, and the NSE were all > 0.80. However, the simulated soil water content could not capture the peak values of the observed soil

3. Results 3.1. Characteristics of HPF units Spatial heterogeneities of soil and terrain properties in this study area could be observed from data of the 123 sampling sites (Table 1). 6

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Fig. 4. Relationships between the observed and simulated leachate NO3−-N concentrations and N2O fluxes on (a, b) the mixed forest (MF) hillslope, (c, d) the tea garden (TG) hillslope, and (e, f) the bamboo forest (BF) hillslope. Note that only one observation site was located on the MF hillslope (MF01), while four observation sites were located on the TG hillslope (TG01, TG02, TG03 and TG04) and BF hillslope (BF01, BF02, BF03 and BF04).

coefficients (r) > 0.99. Comparatively greater NO3−-N leaching (40.08 kg N ha−1) was found in 2016, followed by 2017 (23.03 kg N ha−1) and 2018 (19.51 kg N ha−1). Greater NO3−-N leaching was observed in TG HPF units (mean annual value: 72.11 kg N ha−1), which was 5.89 and 10.20 times of those in BF (12.24 kg N ha−1) and MF (7.07 kg N ha−1), respectively. In addition, differences of NO3−-N leaching rates could be observed among HPF units with the same land use types, due to the spatial heterogeneities of soil properties. For example, in 2017, the greatest NO3−-N leaching (85.48 kg N ha−1) was 1.84 times of the lowest NO3−-N leaching in the TG HPF units. The soil N2O fluxes also showed substantial spatio-temporal variations in our study area (under scenario ASP_LU) (Fig. 5d–f). The mean annual N2O flux from 2016 to 2018 was 1.45 kg N ha−1. Spatial patterns of soil N2O fluxes were consistent in 2016, 2017 and 2018, with r > 0.99. The greatest N2O flux was observed in 2016 (1.72 kg N ha−1), while the spatial mean N2O fluxes in 2017 and 2018 were similar (1.31- and 1.33-kg N ha−1, respectively). Greater N2O fluxes were observed in TG HPF units. The mean N2O fluxes of TG HPF units were 3.98-, 3.14-, and 3.75-kg N ha−1 in 2016, 2017 and 2018, respectively, which were 3.01-, 3.45- and 7.04-times of those in BF HPF units, and 6.49-, 7.58- and 17.90-times of those in MF HPF units.

water content, and the soil temperature values were overestimated by DNDC model when below 15 °C (data not shown). Secondly, acceptable performances were also observed in simulating the leachate NO3−-N concentrations and the soil N2O fluxes (Fig. 4). The R2 values in simulating the leachate NO3−-N concentrations were > 0.50 for all hillslopes, and the NSE were all > 0.30. The R2 values in simulating the soil N2O fluxes were > 0.50 for all hillslopes, and the NSE were all > 0.35. On the TG hillslope, except at TG04, the R2 values in simulating the leachate NO3−-N concentrations and N2O fluxes were > 0.50, and the NSE were > 0.35. On the BF hillslope, better accuracies in simulating the leachate NO3−-N concentrations were observed at BF01 (R2 = 0.73 and NSE = 0.64), while accuracies of the other three sites were relatively poor (R2 < 0.60 and NSE < 0.30). However, in simulating the N2O fluxes, acceptable accuracies were found at BF02, BF03 and BF04 (R2 > 0.45 and NSE > 0.25), while the accuracy was relatively poor at BF01 (R2 = 0.36 and NSE < 0). 3.3. Spatio-temporal variations of N losses Substantial spatio-temporal variations of the NO3−-N leaching were observed in the study area as simulated by DNDC model (under scenario ASP_LU) (Fig. 5a–c). The mean annual NO3−-N leaching from 2016 to 2018 was 27.54 kg N ha−1. Spatial patterns of NO3−-N leaching in different years were similar, with the correlation 7

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Fig. 5. Spatial distributions of simulated NO3−-N leaching and N2O fluxes in (a, d) 2016, (b, e) 2017, (c, f) 2018 under actual spatial pattern of land use types (mixed forest or MF, tea garden or TG, and bamboo forest or BF) in the study area. The black patch represents the other land use types except MF, TG and BF.

4. Discussion

precipitation amount was greater in 2017 (1385-mm) than in 2018 (1049-mm), the N2O flux was slightly lower in 2017 (1.31- and 1.33kg N ha−1 in 2017 and 2018, respectively). This could be attributed to the great N losses in the extreme wet year 2016 (precipitation: 1764mm), which may reduce the soil N available for emission in 2017. Many studies had also indicated that N losses initially increased and then decreased with the continuous increasing amount of precipitation because of the reduction of soil N availability (e.g., Tian et al., 2012; Zhu et al., 2011). Land use type was the determining factor of the spatial heterogeneities of NO3−-N leaching and N2O flux in the study area (Fig. 5). This was consistent with the previous studies (e.g., Fu et al., 2004; Gutlein et al., 2018; Klaus et al., 2018). TG had the highest risk of N losses. From 2016 − 2018, the mean annual NO3−-N leaching and N2O flux of TG were 2.62 and 2.50 times of the mean annual values of the

4.1. Controlling factors of NO3−-N leaching and N2O emission Precipitation was always recognized as a primary factor stimulating the NO3−-N leaching and N2O flux in previous studies (Enanga et al., 2016; Kasper et al., 2019; Tian et al., 2012; Zhu et al., 2011). This was mainly because that precipitation increased soil water content, and thus facilitated the anaerobic soil condition and subsurface water flow, which induced the denitrification (producing N2O) and provided driving force for NO3−-N leaching, respectively (Enanga et al., 2016; Iqbal et al., 2018). In this study area, the temporal trends of NO3−-N leaching and N2O flux were consistent with that of the precipitation (r = 0.58 and 0.42 between daily NO3−-N leaching, N2O flux and precipitation, respectively, with p < 0.01). However, although the 8

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Table 4 Correlation matrix of relationships between the parameters used in DNDC model and the simulated NO3−-N leaching and N2O fluxes in 2016, 2017 and 2018. The NO3−-N leaching and N2O fluxes were simulated by DNDC model under the assumption that all land in the study area was tea garden. Ks: soil saturated hydraulic conductivity; SOC: soil organic carbon content. NO3−-N leaching

Parameters

2016 −3

Bulk density (g cm ) Wilting point (m3 m−3) Field capacity (m3 m−3) Clay (g g−1) Ks (m hr−1) Porosity (m3 m−3) SOC (g g−1) NO3−-N (mg kg−1) NH4+-N (mg kg−1) Rock fragment (m3 m−3)

Note. The symbols of * and

0.14 0.60** 0.81** −0.47** 0.77** 0.78** −0.08 −0.25 −0.04 −0.83** **

N2O fluxes 2017 0.08 0.45** 0.70** −0.63** 0.82** 0.67** −0.01 −0.25 −0.03 −0.73**

2018 0.09 0.42** 0.67** −0.66** 0.81** 0.65** −0.02 −0.25 −0.03 −0.71**

mean

2016

2017

0.11 0.50** 0.73** −0.59** 0.81** 0.71** −0.04 −0.25 −0.04 −0.77**

−0.85 −0.41** −0.40** 0.12 −0.20 −0.47** 0.84** 0.74** 0.49** 0.42** **

2018

−0.87 −0.49** −0.47** 0.06 −0.21 −0.53** 0.86** 0.72** 0.46** 0.48** **

mean

−0.87 −0.30* −0.22 −0.06 −0.06 −0.30* 0.88** 0.69** 0.44** 0.23

**

−0.87** −0.41** −0.37** 0.05 −0.16 −0.44** 0.86** 0.73** 0.47** 0.38**

denoted significant correlations at p < 0.05 and p < 0.01, respectively.

entire study area. The annual NO3−-N leaching of TG in the study area (72.11 kg N ha yr−1) was comparable to the traditional rice–wheat rotation system (55.3–93.1 kg N ha yr−1) in nearby plain area (Zhao et al., 2012). The mean annual N2O flux of TG in this study area (3.63 kg N ha−1 yr−1) fell in the ranges of 1.77–11.78 kg N ha−1 yr−1 observed by Han et al. (2013) in a TG experiment site in Hangzhou City of China. In comparison, both MF and BF had low N loss risks (Fig. 5). Due to disparate soil properties, climatic features, vegetation types and managements, NO3−-N leaching and N2O flux of MF and BF in our study were not always consistent with those in previous studies. For example, due to the fertilization, the annual N2O flux reached 10.05 kg N ha−1 on a BF hillslope (Liu et al., 2011); because of low soil pH and high soil nutrients, the annual N2O flux in an evergreen latifoliate forest ranged from 0.78- to 4.68-kg N ha−1 (Han et al., 2013). Assuming the study area was all used for TG, influences of soil

properties on spatial variations of NO3−-N leaching and N2O flux were recognized (Table 4; Fig. 6b, c). The wilting point, field capacity, Ks and porosity had positive correlations (p < 0.01) with NO3−-N leaching and negative correlations (p < 0.05) with N2O flux (Table 4). This was due to that these SHPs determined the aerobic/anaerobic conditions for nitrification/denitrification processes and provided driving force for NO3−-N leaching by affecting the soil hydrology (Zhu et al., 2018). Clay would slow down the soil water movement and prevented the decomposition of organic matters (Jamali et al., 2016; Wang et al., 2003), while RFC reduced the soil cross-section area for water flow and decreased the volume of soils participated in N cycle (Liao et al., 2019). Thus, clay content and RFC were negatively correlated (p < 0.05) with NO3−-N leaching (Table 4). The RFC was positively correlated (p < 0.05) with N2O flux (Table 4). This was due to that the optimal soil water content (near to field capacity) that triggered the peak of N2O

Fig. 6. The land use patterns under different scenarios, including (a) the actual land use patterns (mixed forest or MF, tea garden or TG, and bamboo forest or BF), the optimized spatial patterns of TG by minimizing (b) NO3−-N leaching and (c) N2O emission, and (e) by balancing the reductions of NO3−-N leaching and N2O emission. The black patch represents the other land use types except MF, TG and BF. The subplot (d) illustrates the way to determine the hydropedological function units that are suitable for TG by maintaining the total area of TG as the actuality (216.33 ha) and minimizing the NO3−-N leaching and N2O emission. 9

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kg N ha−1, respectively. The accumulative N2O flux and NO3−-N leaching were 94.0% and 105.9% of those under ASP_LU, respectively (Fig. 7). When comparing the N2O flux of TG under MEN_LU with that under ASP_LU, an obvious reduction rate of 11.6% was achieved. However, both NO3−-N leaching and N2O emission could pose specific environment concerns, and no uniform criterion of evaluating their environmental damages has been proposed. Simultaneously minimizing both the NO3−-N leaching and N2O emission seemed inaccessible as the incompatible physical and biogeochemical mechanisms of them (Fowler et al., 2013). Negative correlations (r = −0.38, p < 0.01) were observed between the spatial variations of mean annual NO3−-N leaching and N2O fluxes under the assumption that all lands were TG in the study area. Therefore, the scenario BRN_LU would be an alternative by equally considering the reductions of NO3−-N leaching and N2O flux while maintaining the areas of MF, TG and BF the same as actuality (Fig. 6e). Under BRN_LU, the mean annual NO3−N leaching and N2O flux were 26.62- and 1.39-kg N ha−1, respectively. The accumulative NO3−-N leaching and N2O flux were 96.7% and 95.9% of those under ASP_LU, respectively (Fig. 7). When comparing the NO3−-N leaching and N2O flux of TG under BRN_LU with those under ASP_LU, reduction rates of 5.1% and 7.6% were observed. The reduction rates of NO3−-N leaching and N2O flux under these three scenarios were not as high as expected (Fig. 7). One reason for this was the relative small spatial variations of soil and terrain properties in the study area, which constrained the spatial heterogeneity of N losses. The CVs of NO3−-N leaching and N2O flux were respectively 16.2% and 11.7% when assuming the entire study area was used as TG (Fig. 6b, c). Another reason was the actual spatial pattern of TG lands was comparatively rational. The actual TG lands were not located in areas with high NO3−-N leaching risk (e.g. the northwest margins of the study area) (Fig. 6a, b). Although some actual TG lands were located in the areas with high N2O fluxes (Fig. 6a, c), this did not induce large reduction rates after optimizations due to the small spatial variation of N2O flux (Fig. 6c).

flux could be easier reached since the RFC reduced the soil porosity (Liao et al., 2019). Because NO3−-N leaching was mainly restrained by drainage, effects of bulk density, SOC, soil NO3−-N and NH4+-N concentrations were masked (Table 4). However, as the primary substrates for producing N2O (Li et al., 2000), SOC, NO3−-N and NH4+-N had positive correlations (p < 0.05) with N2O fluxes (Table 4). In addition, since high bulk density was associated with low soil organic matters and thus limited substrates for producing N2O (Schrumpf et al., 2011), negative relationships between bulk density and N2O fluxes were observed (Table 4). Overall, the spatial variation of NO3−-N leaching in the study area was primarily determined by SHPs, while the soil C and N concentrations had more prominent influences on N2O fluxes.

4.2. Optimizing the land use spatial patterns Assuming the study area was all used for TG, risks of NO3−-N leaching in different HPF units were identified by simulating in DNDC model (Fig. 6b). Under the MLN_LU scenario, HPF units with low NO3−-N leaching risks were considered to be suitable for TG (Fig. 6b). The optimized spatial pattern of land use types was generated by minimizing the NO3−-N leaching while maintaining the areas of MF, TG and BF as actuality. Under this scenario, the mean annual NO3−-N leaching and N2O flux in the study area were 25.44- and 1.53-kg N ha−1, respectively. The accumulative NO3−-N leaching and N2O flux were 92.4% and 105.0% of those under ASP_LU, respectively (Fig. 7). When comparing the NO3−-N leaching of TG under MLN_LU with that under ASP_LU, an obvious reduction rate of 10.8% was achieved. Similarly, based on the spatial pattern of N2O flux under the assumption that the study area was all used for TG, risks of N2O flux in different HPF units were identified (Fig. 6c). Under the MEN_LU scenario, HPF units with low risk of N2O flux were suitable to be changed into TG (Fig. 6c). Thus, the optimized spatial pattern of land use types was determined by minimizing the N2O flux while maintaining the areas of MF, TG and BF the same as actuality. Under this scenario, the mean annual NO3−-N leaching and N2O emission were 29.16- and 1.37-

Fig. 7. Accumulative NO3−-N leaching and N2O fluxes from 2016 to 2018 under the scenarios of the actual spatial pattern of land use types (ASP_LU), the optimized spatial pattern of land use types by minimizing NO3−-N leaching (MLN_LU) and N2O emission (MEN_LU), and by balancing the reductions of both (BRN_LU). The right y axis denotes the percentage of the accumulative NO3−-N leaching and N2O fluxes to those under the scenario of ASP_LU.

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5. Conclusions

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In this study, spatial pattern of land use types was optimized to reduce NO3−-N leaching and N2O emission in a mountainous area. First, the DNDC model was calibrated and validated on representative MF, TG and BF hillslopes, and acceptable performances were achieved. Second, 55 HPF units were generated by superposing land use map with SOC, RFC and slope maps. Then the calibrated DNDC models for representative land use types were applied to simulate the NO3−-N leaching and N2O fluxes in these 55 HPF units. Results showed greater NO3−-N leaching and N2O flux were found in TG than in MF and BF land use types. Precipitation was a primary factor controlling the temporal trends of N losses, while the land use types was the determining factor of the spatial heterogeneities of N losses. The SHPs were also recognized as the critical spatial influencing factors of NO3−N leaching, while the soil carbon and N concentrations had prominent influences on N2O flux. At last, optimized spatial allocations of TG were determined under the scenarios of minimizing NO3−-N leaching, minimizing N2O emission, and balancing the reductions of both. These results highlighted the influences of land use patterns on non-point N losses in the mountainous area and would provide scientific guidance for the agricultural land developments. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement This study was financially supported by the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (QYZDB-SSWDQC038), the Jiangsu Province (Key R&D Plan) (BE2017777), the Leading Edge Project of Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (NIGLAS2018GH06), and the Talent Introduction Project of Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (NIGLAS2018QD06). References Beaudoin, N., Saad, J.K., Van Laethem, C., Machet, J.M., Maucorps, J., Mary, B., 2005. Nitrate leaching in intensive agriculture in Northern France: effect of farming practices, soils and crop rotations. Agr. Ecosyst. Environ. 111 (1), 292–310. https://doi. org/10.1016/j.agee.2005.06.006. Cameron, K.C., Di, H.J., Moir, J.L., 2013. Nitrogen losses from the soil/plant system: a review. Ann. Appl. Biol. 162 (2), 145–173. https://doi.org/10.1111/aab.12014. Cao, P.Y., Lu, C.Q., Yu, Z., 2018. Historical nitrogen fertilizer use in agricultural ecosystems of the contiguous United States during 1850–2015: application rate, timing, and fertilizer types. Earth Syst. Sci. Data 10 (2), 969–984. https://doi.org/10.5194/ essd-10-969-2018. Compton, J.E., Boone, R.D., 2000. Long-term impacts of agriculture on soil carbon and nitrogen in New England forests. Ecology 81 (8), 2314–2330. https://doi.org/10. 1890/0012-9658(2000) 081[2314:LTIOAO]2.0.CO;2. Enanga, E.M., Creed, I.F., Casson, N.J., Beall, F.D., 2016. Summer storms trigger soil N2O efflux episodes in forested catchments. J. Geophys. Res. Biogeo. 121 (1), 95–108. https://doi.org/10.1002/2015jg003027. Foley, J.A., DeFries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., Daily, G.C., Gibbs, H.K., Helkowski, J.H., Holloway, T., Howard, E.A., Kucharik, C.J., Monfreda, C., Patz, J.A., Prentice, I.C., Ramankutty, N., Snyder, P.K., 2005. Global consequences of land use. Science 309 (5734), 570–574. https://doi. org/10.1126/science.1111772. Fowler, D., Coyle, M., Skiba, U., Sutton, M.A., Cape, J.N., Reis, S., Sheppard, L.J., Jenkins, A., Grizzetti, B., Galloway, J.N., Vitousek, P., Leach, A., Bouwman, A.F., ButterbachBahl, K., Dentener, F., Stevenson, D., Amann, M., Voss, M., 2013. The global nitrogen cycle in the twenty-first century. Philos. Trans. R. Soc. B-Biol. Sci. 368 (1621), 13. https://doi.org/10.1098/rstb.2013.0164. Fu, B.J., Meng, Q.H., Qiu, Y., Zhao, W.W., Zhang, Q.J., Davidson, D.A., 2004. Effects of land use on soil erosion and nitrogen loss in the hilly area of the Loess Plateau, China. Land Degrad. Dev. 15 (1), 87–96. https://doi.org/10.1002/ldr.572. Fu, X.L., Shao, M.A., Wei, X.R., Horton, R., 2010. Soil organic carbon and total nitrogen as affected by vegetation types in Northern Loess Plateau of China. Geoderma 155 (1–2), 31–35. https://doi.org/10.1016/j.geoderma.2009.11.020.

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