Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Study on characteristics of soil and nutrient losses in Sunjiagou small watershed in cold black soil area

  • Taoyan Dai,

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation College of Water Conservancy and Electric Power, Heilongjiang University, Harbin, China

    ⨯
  • Liquan Wang ,

    Roles Investigation, Writing – review & editing

    15304640067@163.com

    Affiliation College of Water Conservancy and Electric Power, Heilongjiang University, Harbin, China

    ⨯
  • Tienan Li,

    Roles Investigation, Resources, Writing – review & editing

    Affiliation Heilongjiang Province Hydraulic Research Institute, Harbin, China

    ⨯
  • Pengpeng Qiu,

    Roles Methodology, Resources, Writing – review & editing

    Affiliation Heilongjiang Province Hydraulic Research Institute, Harbin, China

    ⨯
  • Jun Wang,

    Roles Investigation, Writing – review & editing

    Affiliation Heilongjiang Province Hydraulic Research Institute, Harbin, China

    ⨯
  • Zhengjun Wang

    Roles Writing – review & editing

    Affiliation College of Water Conservancy and Electric Power, Heilongjiang University, Harbin, China

    ⨯

Abstract

Investigating the impact of different factors on soil and nutrient loss and suggesting viable control measures is currently a significant concern. This study aims to examine the variations in soil erosion, as well as nitrogen and phosphorus loss, in the core area of the typical hilly diffuse Blackland erosion control. To achieve this, runoff plots with slopes of 3° and 5° were set up in the Sunjiagou sub-basin, located in the upper reaches of the Feiketu River. These plots were subjected to various soil and water conservation measures, along with different levels of vegetation cover. This study aims to analyze the soil and nutrient loss patterns and characteristics in each runoff plot during the natural rainfall events occurring between 2020 and 2022. The results show that soil and nutrient losses are highly significantly and positively correlated with rainfall intensity. The RUSLE model demonstrates a better fit for both cross ridge tillage and bare ground. The loss of nitrogen was much more significant than that of phosphorus, and nitrate nitrogen is the main form of nitrogen loss. Nitrogen loss is mainly dominated by nitrate nitrogen (NN), which is easily soluble in water and constantly migrates with runoff due to the negatively charged NN (NN accounted for 45.2% ~ 81.8% of total nitrogen (TN)). In contrast, the positively charged ammonia nitrogen (AN) is more stable in combination with the soil; large losses only occur under severe sediment erosion. Phosphorus is easily attached to sediment, and the high sediment production leads to a more serious loss of total phosphorus (PP) in the particulate state (PP accounts for 72.7% ~ 96.2% of total phosphorus (TP)). Changing longitudinal ridge tillage to cross ridge tillage and planting vegetation with better water retention and sediment fixation as plant hedges can effectively prevent the loss of soil, runoff, nitrogen, and phosphorus.

Citation: Dai T, Wang L, Li T, Qiu P, Wang J, Wang Z (2023) Study on characteristics of soil and nutrient losses in Sunjiagou small watershed in cold black soil area. PLoS ONE 18(8): e0289479. https://doi.org/10.1371/journal.pone.0289479

Editor: Chun Liu, Jinan University, CHINA

Received: April 13, 2022; Accepted: July 19, 2023; Published: August 3, 2023

Copyright: © 2023 Dai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: 10.5061/dryad.rr4xgxdc2.

Funding: T.L.Open Project of State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (ES202116) L.W.Heilongjiang Province Key Research and Development Program Guidance Category Project (GZ20210061) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The role played by the funder is shown in the author's contribution.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Non-point source pollution has become a significant source of water pollution and is more difficult to prevent. Nitrogen and phosphorus loss caused by soil erosion reduces soil productivity [1] and leads to eutrophication of water bodies [2, 3]. Non-point source pollution is a global problem and has been the subject of much research in countries worldwide [4–8]. In China, agriculture has surpassed industry as the primary source of non-point source pollution. Erosion and sediment-related chemical transport should be given more attention in rural China [9]. Due to soil erosion from cultivated land, nitrogen and phosphorus are exported in the dissolved runoff or the particulate state of sediments, thus polluting water bodies.

As one of the four major black soil regions globally, the Northeast Black Soil Region is the stabilizer of China’s food market. Black soil has also been compared to "the giant panda of cultivated land". However, with the interaction of natural and human factors in the black soil area, soil erosion has become more and more serious, leading to the thinning of the black soil layer and the decrease of soil fertility [10]; coupled with a large amount of chemical fertilizer input and the scientific arable land management system is not perfect, leading to nutrient loss and water pollution [11–13]. At present, the surface soil of sloping arable land in black soil areas is lost at a rate of 2~3 mm·a-1, and the organic matter content of the surface soil decreases at a rate of 5‰ per year.

A number of studies have been conducted on the issue of nonpoint source pollution. Zhu et al. [14] analyzed the spatial and temporal evolution of the output risk probability of agricultural nonpoint source pollution in the Fuling district using the SWAT model and output risk model. Huang et al. [15] identified the critical source areas of nonpoint source pollution more accurately through a rainfall-driven correlation-based mapping method (PCM). However, such studies have been carried out within the catchment scale [6, 8, 16–18], Only a few studies on soil and nutrient loss characteristics at the plot scale [19]. Soil erosion and nutrient loss studies using runoff plots have been more effectively utilized in China, the United States and Europe [20–22]. Runoff plots were used to explore the effects of no-till practices on reducing agricultural nonpoint source pollution [23] and the effects of variable versus uniform fertilizer application on nutrient loss [24]. The variables in runoff plots are easier to control than in small watersheds. Setting up runoff plots in small watersheds ensures that climate, soil, and other factors are the same. The experiments in runoff plots have led to the scientific and rational use of engineering and biological measures to control nutrient loss, which provides a basis for reducing nonpoint source pollution in small watersheds.

Nowadays, the problem of soil erosion is studied mainly using artificial rainfall and remote sensing image analysis [25–28]. Wang et al. [29] analyzed the soil erosion characteristics of the black soil area in the middle and lower reaches of the Mu Shi River based on GIS and RS technology. Still, this type of study lacks calibration and verification of the results and analysis due to the lack of ground monitoring in the study area. Li et al. [30] analyzed the soil erosion in the red soil area of southern China by simulating rainfall. Although the erosion mechanism can be understood more clearly, it is different from the natural rain. This leads to differences with the erosion characteristics under natural rainfall.

The study area is a typical hilly, where the process of nonpoint source pollution is complex and difficult to prevent. However, most studies on nonpoint source pollution have been conducted in red soil areas, loess areas and purple soil areas, and the analysis of nitrogen and phosphorus losses and their influencing factors in black soil areas has rarely been covered. The objectives of this study are (i) to characterize the variations in runoff, soil erosion, and the loss of each form of nitrogen and phosphorus; (ii) exploring the fitting effect of RUSLE on soil erosion under different conditions; and (iii) to study the response of soil and nutrient loss to soil and water conservation measures. In the end, some suggestions are made to manage soil and nutrient loss in black soil areas.

Materials and methods

Study area

The Sunjiagou small watershed is located in Bin County, Harbin City, Heilongjiang Province. The runoff field of Bin County Water Conservation Science and Technology Park is the ground monitoring station of the Sunjiagou small watershed, located at 127°24′47″E and 45°44′57″N. Bin County Water Conservation Science and Technology Park is also a national soil and water conservation monitoring station (Fig 1). The watershed is located in the mid-temperate continental monsoon climate zone, with a multi-year average rainfall of 681mm and an average annual temperature of 4.1°C. Rainfall is unevenly distributed within the year, mainly concentrated in June-September, with precipitation in July and August in summer accounting for about 60% of the year. A total of 91 rainfall events were monitored from 2020 to 2022, and the number of rainfall events that resulted in runoff generation was 31, with a total annual rainfall of 600.6 mm and a rainfall erosion force of 2900.86 MJ·mm·(hm2·h)-1. Soil types are mainly black soils, but also include white pulpy soils, meadow soils, and dark brown soils. Soil thickness was 20 cm and bulk weight of 1.33~1.35g-cm-3. The content of micro agglomerates (agglomerated structural units 0.05–0.25 mm in diameter) ranged from 73.23% to 78.52% and 61.68% to 70.84% in bare land and cultivated land, respectively. Mainly planted with Zea mays, Oryza sativa L, and Glycine max, and soil erosion occurs primarily on sloping land and wasteland.

thumbnail
Download:
Fig 1. Location of the study area.

https://doi.org/10.1371/journal.pone.0289479.g001

Runoff plots

Six runoff plots in the runoff field were selected as research objects, and the specific layout is shown in Table 1. The form requires that their slope, soil and water conservation measures, vegetation type, and other factors represent the local area, and all farming activities are carried out according to the farming habits of local farmers. For each runoff plot, 15 kg of compound fertilizer was applied as a base fertilizer before planting. The ratio of N, P2O5, and K2O in the compound fertilizer is 15:35:10. The seeds were sown on May 18, at a depth of 10 cm, and spray pesticides on May 11, June 11, July 16 and August 27. The size of the runoff plots was 5 m × 20 m; the thickness of the black soil layer was about 20 cm. Most of the sloping land in northeast China is dominated by gentle slopes; therefore, 3° and 5° runoff plots were used in this study. Amorpha fruticosa Linn. was used as a plant hedge in this study because it is a common plant in northeastern China and has good water retention and sediment fixation effects. And Glycine max, a major crop in the Northeast, was selected for planting in the runoff plots. Soil and water and nutrient loss were analyzed from various aspects such as slope, soil and water conservation measures (Fig 2), and vegetation cover.

thumbnail
Download:
Fig 2.

Two types of tillage: (a) longitudinal ridge tillage, (b) cross ridge tillage.

https://doi.org/10.1371/journal.pone.0289479.g002

thumbnail
Download:
Table 1. Information of the study runoff plots.

https://doi.org/10.1371/journal.pone.0289479.t001

Data collection

The runoff volume is measured by the volume of water in the diversion bucket and the runoff bucket, and the sediment production is measured by the total amount of sediment in the diversion bucket as well as in the catch basin. The precipitation observation equipment uses fully automatic weather stations and siphon type self-registering rain gauges to monitor rainfall, I30, and rainfall erosion force indicators. Instantaneous water samples were collected at 0.5h intervals during the first two hours of rainfall and then at one-hour intervals until the end of the rain, collected in 500ml plastic bottles, stored in a 4°C refrigerator, and returned to the laboratory in time for timely testing. Test items include total phosphorus (TP), total nitrogen (TN), total soluble phosphorus (DTP), total particulate phosphorus (PP), total soluble nitrogen (DTN), ammonia nitrogen (AN), nitrate-nitrogen (NN). The TN and TP indicators were measured by shaking the water sample and taking a small amount of liquid directly, while the AN, NN, DTN and DTP indicators needed to be measured by filtering the water sample with 0.45 μm filter membrane. The specific operations were referred to the corresponding national standards (Table 2) and finally measured using a UV-Vis spectrophotometer (Evolution 220; Thermo Fisher, USA).

thumbnail
Download:
Table 2. Water quality test indexes and methods.

https://doi.org/10.1371/journal.pone.0289479.t002

Data processing

Event mean concentration (EMC) is used to calculate the average concentration of each rainfall, and EMC (mg·L-1) is the weighted average concentration of runoff pollutants, which can be expressed as: (1)

Where n is the number of samples; △t is the time interval between two adjacent samples (s); ci and ci+1 are the concentration of this nutrient at stage i and i+1 sampling (mg·L-1); Qi and Qi+1 are the runoff rates at the adjacent monitoring moments (m3·s-1).

In this paper, the erosion of rainfall is estimated by modifying the universal soil loss equation (RUSLE) with the following equation: (2)

A is the soil erosion modulus (t km-2); R is the rainfall erosion force factor (MJ mm (hm2 h)-1); K is the soil erodibility factor ((t hm2 h)/(hm2 MJ mm)); L is the slope length factor; S is the slope factor; C is the vegetation cover and crop management factor; and P is the soil and water conservation factor.

Referring to the method of Zhang et al. [31] to calculate rainfall erosion forces by rainfall volume and intensity: (3)

P is the rainfall amount, I10 is the maximum 10 min rain intensity, and PI10 unit is (mm2 h-1). The slope length factor L was calculated by referring to the study of Fu et al. [32]. The slope length factor S was calculated using the method of McCool et al. [33] in the area below 10° and Liu et al. [34] in the area above 10°. The equations are as follows: (4) (5)

λ is the slope length; m is the slope length index; and θ is the slope degree (°). Other factors were calculated with reference to the study of Zhang et al. [35] in Heilongjiang Province. The vegetation cover and crop management factor C for soybean was determined based on the weighted average of C values for each growth period of soybean. the C value for runoff plots with soybean cover was 0.2626 and for bare ground was 1. the P value for longitudinal ridge tillage was 1 and for cross ridge tillage was 0.352. the K value for black soil was taken as 0.26.

The experimental data of this study were counted and calculated using Excel 2016, and Pearson correlation analysis was performed using SPSS 25.0 and plotted with OriginPro 2021.

Results and analysis

Surface runoff and soil loss

The trends of runoff production, sediment production, and rainfall in each plot were similar (Fig 3). Under the typical rainfall of each field, the order of flow production, as well as the size of sediment production in each runoff plot, is basically: 5° cross-slope furrow tillage with a planted hedge (plot B2) < 3° cross-slope furrow tillage (plot A2) < 3° down-slope furrow tillage (plot A1) < 5° down-slope furrow tillage (plot B1) < 3° bare land (plot A3) < 5° bare land (plot B3).

thumbnail
Download:
Fig 3.

Rainfall (a), runoff volume (b), soil erosion rate (c) and RUSLE predictions (d) for each runoff plot.

https://doi.org/10.1371/journal.pone.0289479.g003

Nitrogen and phosphorus loss

Event mean concentration (EMC) was used to calculate the average concentration of AN, NN, TN, and TP in each rainfall event and the amount lost in a single rainfall event (Fig 4).

thumbnail
Download:
Fig 4. Loss concentrations of ammonia nitrogen, nitrate nitrogen, total nitrogen and total phosphorus in each runoff plot.

https://doi.org/10.1371/journal.pone.0289479.g004

AN, NN, and TN have relatively similar loss characteristics and fit well with rainfall (Fig 4). The nitrogen of each form varied widely: 0.14 mg·L-1 ~ 1.67 mg·L-1 for AN, 0.86 mg·L-1 ~ 7.12 mg·L-1 for NN, and 1.86 mg·L-1 ~ 10.23 mg·L-1 for TN. Loss fluxes of each form of nitrogen: 0.26 kg km-2 ~13.51 kg km-2 for AN, 1.06 kg km-2 ~71.61 kg km-2 for NN, and 2.12 kg km-2 ~101.98 kg km-2 for TN.

The trend of phosphorus loss is similar to that of rainfall, and the trend of TP is relatively stable due to the strong fixation capacity of phosphorus (Fig 5). The loss concentration of TP ranges from 0.30 mg·L-1 ~ 2.98 mg·L-1 and the flux of TP ranges from 0.36 kg km-2 ~ 32.85 kg km-2.

thumbnail
Download:
Fig 5. Loss fluxes of ammonia nitrogen, nitrate nitrogen, total nitrogen and total phosphorus in each runoff plot.

https://doi.org/10.1371/journal.pone.0289479.g005

Under different soil and water conservation measures, the loss of DTN accounts for 60.1% ~ 92.6% of TN; the loss of NN accounts for 45.2% ~ 81.8% of TN; and the loss of AN accounts for 6.3% ~ 16.1% of TN (Fig 6A). The variability of PP and DTP was small in five typical rainfall events (Fig 6B). The loss flux of PP accounted for 72.7% ~ 96.0% of TP.

thumbnail
Download:
Fig 6.

Percentage of each form of nitrogen (a) and phosphorus (b).

https://doi.org/10.1371/journal.pone.0289479.g006

Correlation analysis of nutrient loss and soil erosion

Pearson correlation analysis was performed on each form’s rainfall, runoff, sediment production, and nitrogen and phosphorus loss (Table 3). The correlation coefficients between rainfall and nitrogen and phosphorus loss were greater than 0.9. The relationship between AN, NN and TN and runoff is more obvious.

thumbnail
Download:
Table 3. Pearson correlation coefficients between each form of nitrogen and phosphorus and rainfall, runoff, and soil erosion rate (SLR) in six runoff plots.

https://doi.org/10.1371/journal.pone.0289479.t003

Discussion

Analysis of the factors influencing soil erosion

Rainfall and runoff are the driving force of soil erosion, and rainfall amount, duration, and intensity affect the flow and sediment production of each runoff plot [36]. The short duration and high volume of rainfall produced changes in soil structure that led to more severe erosion [37]. Continuous rainfall makes the soil wetter, resulting in increased soil water content and more likely to produce runoff, thus causing more severe erosion [38].

Plant cover also significantly affected soil erosion, especially under heavy rainfall. Some scholars have suggested that longitudinal ridge tillage will increase soil erosion, which is different from the present study. In the present study, A1 and B1 plots with longitudinal ridge tillage had a certain effect on water retention and sediment fixation compared with bare land, mainly because the vegetation cover of A1 and B1 plots played a key role. Due to the rapid growth of Glycine max in the rainy season, the vegetation cover ranged from 20%-35% in late June to 70%-80% in mid-to early July, and even up to 90% in August. The high vegetation cover dramatically reduces soil erosion [39]. The stems and leaves of the vegetation had an enhanced ability to intercept rainfall, reducing the kinetic energy of raindrops and reducing the direct splash of raindrops on the soil [40]; the surface apoplast protects the soil surface from direct raindrop impact and significantly enhances infiltration [41]. The plant roots were continuously developed, improving the physical and chemical properties of the soil, enhancing the water-holding capacity, and enriching the structure of the aggregates, which led to an enhanced water retention function of the vegetation [42, 43]. It can be seen that the former dominates the effect of vegetation cover and whether or not longitudinal ridge tillage on soil erosion in this study area under the weather of heavy rainfall.

The flow and sediment production in plot A2 and plot B2 are relatively flat compared with those of other runoff cells and are much lower than those of other runoff cells. This indicates that the flow and sediment production in A2 and B2 plots are much lower than those in other runoff plots, which play a perfect role in water retention and sediment fixation and significantly reduce the soil erosion in black land. In multiple rainfall events, plot A2 and plot B2 did not produce runoff, indicating that the cross ridge tillage measures were more prominent in the light rainfall weather. In plot A2, compared with plot A1, the cross ridge tillage reduced runoff losses by 88.8% to 95.0% and sediment losses by 90.2% to 93.5% compared with the longitudinal ridge tillage. Compared with the longitudinal ridge tillage and cross ridge tillage, the ridges of the cross ridge tillage intercept runoff and increases the contact time between runoff and soil, which makes most of the runoff infiltrate into the ground [44]. Unlike plot A2, plot B2 was planted with Amorpha fruticosa Linn. as a plant hedge based on cross ridge tillage. In the horizontal comparison of plot B1, the runoff loss was reduced by 91.3%~96.7%, and the sediment loss was reduced by 92.7%~94.5%; in terms of annual rainfall, the runoff loss was reduced by 96.5%, and the sediment loss was reduced by 97.5%. Planting purple acacia as a plant hedge is more effective in reducing soil erosion; purple acacia is a typical soil and water conservation crop; its root system is conducive to improving soil structure to play a better role in water retention and sediment fixation [45]. Yuan et al. [46] concluded that cross ridge tillage could reduce sediment loss by 43.43%~93.9% compared with longitudinal ridge tillage; the water retention and sediment fixation capacity of the cross ridge tillage was not as outstanding as in the present study. This is due to the low rainfall and gentle slope in this study area, where the monopoles can intercept most of the loss without causing the ridges to break [38]. Numerous scholars [47–49] have obtained similar results and values in different experimental studies and concluded that cross ridge tillage and increased plant hedges could significantly reduce soil erosion.

The RUSLE model is commonly used for soil erosion estimation. Therefore, it is important to investigate whether the RUSLE model fits accurately under different land use types, soil and water conservation measures, and other factors. The estimated values of RUSLE for cross ridge tillage (plots A2, B2) and bare land plots (A3, B3) are closer to the measured values and can be used for estimating soil loss in this terrain in the watershed (Fig 3C and 3D). However, the difference between the estimated value and the measured value of the longitudinal ridge tillage (A1 and B1 plots) is relatively large. The reason may be that the value of soil and water conservation factor P is small under the condition of longitudinal ridge tillage. Future studies should focus on fitting RUSLE in the longitudinal ridge tillage plots and explore the value of soil and water conservation factors.

Analysis of factors influencing nitrogen and phosphorus loss

Different soil and water conservation measures also affected nutrient loss. Excluding the two rainfall events in which the cross ridge tillage did not produce flow, the concentration of AN, NN, and TN loss in plot A1 was much greater than that in the cross ridge tillage land and bare ground (Fig 4). Compared with longitudinal ridge tillage and bare ground, the monopod of cross ridge tillage intercepts runoff. It prolongs the contact time between runoff and topsoil, while nitrogen is easily dissolved in water and primarily lost in dissolved form. The amount of sediment produced by cross ridge tillage land is much smaller than that of longitudinal ridge tillage land and bare land, even though the loss concentration is higher; in contrast, the amount of loss of cross ridge tillage land is much smaller than that of longitudinal ridge tillage land and bare land. Compared with the longitudinal ridge tillage plot A1, the loss of AN, NN, and TN in the cross ridge tillage plot A2 was reduced by 84.9%~94.3%, 82.2%~94.9%, and 84.1%~94.2%, respectively. Fang [50] showed that contour tillage could significantly reduce TN and TP losses and indicated that cross ridge tillage is desirable for gentle slopes. Xi et al. [51] showed that cross ridge tillage cut nitrogen loss by 47.09% to 78.45% compared to longitudinal ridge tillage. Compared to this study, the proportion of nitrogen reduction in this paper was greater due to the simulated rainfall of 60 mm·h-1 to 120 mm·h-1 in Xi et al. The rainfall in this study was natural, much less than its simulated rainfall, and its study also showed that the effect of cross ridge tillage on nitrogen loss was more pronounced under minor rainfall intensity conditions.

Compared with longitudinal ridge tillage, the concentration and amount of loss of AN, NN, and TN from bare land were slightly more significant than in longitudinal ridge tillage. Due to the longitudinal ridge tillage planting Glycine max, particulate nitrogen is intercepted by soybean stalks and fixed by soil adsorption [45]; likewise, Glycine max will have consumed some nutrients during the growth process. Due to the high vegetation cover, soil erosion in longitudinal ridge tillage is slightly better than bare ground, resulting in slightly lower nitrogen loss than bare ground [52]. Raindrops directly splash bare ground, and nutrients on soil particles and micro aggregates are broken down and absorbed, thus increasing the amount of nitrogen in the runoff [53].

The concentration and amount of TP lost by the extreme rainfall of 8–25 were much higher than other rainfall, and the loss of TP from bare land was severe (Fig 5); as phosphorus was primarily lost in the granular state, and the rainfall in this field caused more severe soil erosion [54]. Among them, the soil erosion of bare land is extremely serious, and its sediment production is twice as much as that of longitudinal ridge tillage and tens of times that of cross ridge tillage, leading to more severe TP loss. Fang [55] and Tang et al. [56] also considered bare land the central TP loss area. Compared with plot A1, the TP loss in plot A2 is reduced by 92.4~97.4%; compared with plot B1, the TP loss in cell B2 is reduced by 94.8%~98.7%. Since PP makes up a large percentage of TP and tends to adsorb to sediments, cross ridge tillage can reduce soil losses and thus significantly reduce TP losses. It can be seen that agricultural non-point source pollution is mostly produced by soil erosion as a carrier, and slowing down soil erosion can effectively prevent nitrogen and phosphorus loss.

Percentage of loss of different forms of nitrogen and phosphorus

The loss of N is mainly in the dissolved state, i.e., the loss of N will respond more sensitively to the variations of runoff volume. The relationship between AN, NN, TN, and runoff was more significant, especially in the two bare land plots of A3 and B3 were highly significantly correlated, and the correlation coefficient was higher than 0.95. Nitrogen loss is mainly dominated by NN due to the negatively charged NN, which is easily soluble in water and constantly migrates with runoff [57], while the positively charged AN is more stable in combination with the soil and the more considerable loss occurs only under severe sediment erosion [58].

Due to the strong nitrification of AN under dry crop conditions, although the loss concentration and loss of AN and NN fluctuate with the variations of rainfall-runoff, on the contrary, the proportion of AN and NN loss to TN loss is more stable. After a long period of nitrification, the percentage of AN loss decreased from 10.9% ~ 14.9% to 6.3% ~ 8.1%, and the percentage of NN loss increased from 45.2% ~ 63.7% to 70.1% ~ 81.8%.

The loss flux of PP accounted for 72.7% to 96.2% of TP (Fig 6). PP and TP were highly correlated with SLR. The percentage of PP loss is more minor in cross ridge tillage and larger in bare land; compared with cross ridge tillage, bare land has a more significant flow-producing sediment production and severe soil erosion, and phosphorus bound to soil particles is easily lost with runoff [50]. The opposite result was obtained by He et al. [59], whose study showed that the loss flux of DTP was a more significant proportion of TP in surface runoff under cross ridge tillage in purple soil area. Cao et al. [60] explained the high rate of DTP output during the late rainfall period in their study, which concluded that the leaching force of runoff gradually increases with the increase of loamy midstream flow, thus leading to the phenomenon. In contrast, this study did not occur due to the fact that the rain intensity of natural rainfall was much less than that of simulated rainfall, followed by the thicker and more fertile soils in the black soil area.

Conclusion

This study advances the study of agricultural nonpoint source pollution in black soil areas of China by exploring the soil erosion and nutrient loss characteristics of sloping cropland under different soil and water conservation measures. The RUSLE model has a better fit in the cross ridge tillage as well as in the bare ground. Nitrogen is mainly lost in the dissolved state with runoff. In contrast, phosphorus is easily adsorbed to sediments and thus lost with soil. The intensity of rainfall, slope, and vegetation cover significantly influence soil and nutrient loss. Changing longitudinal ridge tillage to cross ridge tillage is the most effective measure to prevent soil and nutrient loss, and this method has resulted in a reduced rate of over 90%. Planting plants with better water-holding and sediment-fixing properties, such as Amorpha fruticosa Linn., can achieve better results. Such measures are particularly effective in black soil areas with less rainfall intensity and slope gradient. Bare land and longitudinal ridge tillage land are still the focus of soil and nutrient loss control in black soil areas.

In future studies, the time span of the experiments needs to be increased and the parameters of the RUSLE model need to be determined more accurately. Similarly, different soil and water conservation measures need to be added to test the applicability of the RUSLE model in multiple situations. The ability of water retention and sediment fixation needs to be studied from different periods of crop growth. Soil and nutrient loss under different vegetation covers can also be explored. We also need to think about how to apply the data obtained from runoff plots to hydrological models.

Acknowledgments

The authors are grateful to the editors and the anonymous reviewers for their insightful comments and suggestions.

References

  1. 1. Pimentel D, Harvey C, Resosudarmo P, Sinclair K, Kurz D, McNair M, et al. Environmental and economic costs of soil erosion and conservation benefits. Science (New York, NY). 1995;267(5201):1117–23 pmid:17789193
  2. 2. Hua L, Li W, Zhai L, Yen H, Lei Q, Liu H, et al. An innovative approach to identifying agricultural pollution sources and loads by using nutrient export coefficients in watershed modeling. Journal of Hydrology. 2019;571:322–31
  3. 3. Li J, Li H, Shen B, Li Y. Effect of non-point source pollution on water quality of the Weihe River. International Journal of Sediment Research. 2011;26(1):50–61
  4. 4. Leon LF, Soulis ED, Kouwen N, Farquhar GJ. Nonpoint source pollution: a distributed water quality modeling approach. Water research. 2001;35(4):997–1007 pmid:11235895
  5. 5. Tong STY, Chen W. Modeling the relationship between land use and surface water quality. Journal of environmental management. 2002;66(4):377–93 pmid:12503494
  6. 6. Recanatesi F, Ripa MN, Leone A, Luigi P, Luca S. Land Use, Climate and Transport of Nutrients: Evidence Emerging from the Lake Vicocase Study. Environmental Management. 2013;52(2):503–13
  7. 7. Kibena J, Nhapi I, Gumindoga W. Assessing the relationship between water quality parameters and changes in landuse patterns in the Upper Manyame River, Zimbabwe. Physics and Chemistry of the Earth. 2014;67–69:153–63
  8. 8. Liu R, Xu F, Zhang P, Yu W, Men C. Identifying non-point source critical source areas based on multi-factors at a basin scale with SWAT. Journal of Hydrology. 2016;533:379–88
  9. 9. Ongley ED, Zhang X, Yu T. Current status of agricultural and rural non-point source Pollution assessment in China. ENVIRONMENTAL POLLUTION. 2010;158(5):1159–68 pmid:19931958
  10. 10. Fang H, Sun L, Qi D, Cai Q. Using Cs-137 technique to quantify soil erosion and deposition rates in an agricultural catchment in the black soil region, Northeast China. Geomorphology. 2012;169:142–50
  11. 11. Zhang X, Li Z, Tang Z, Zeng G, Huang J, Guo W, et al. Effects of water erosion on the redistribution of soil organic carbon in the hilly red soil region of southern China. Geomorphology. 2013;197:137–44
  12. 12. Ma W, Li Z, Ding K, Huang J, Nie X, Zeng G, et al. Effect of soil erosion on dissolved organic carbon redistribution in subtropical red soil under rainfall simulation. Geomorphology. 2014;226:217–25
  13. 13. Haiyan F. Temporal variations of sediment source from a reservoir catchment in the black soil region, Northeast China. Soil & Tillage Research. 2015;153:59–65
  14. 14. Zhu K-W, Chen Y-C, Zhang S, Yang Z-M, Huang L, Li L, et al. Output risk evolution analysis of agricultural non-point source pollution under different scenarios based on multi-model. Global Ecology and Conservation. 2020;23
  15. 15. Huang JJ, Lin X, Wang J, Wang H. The precipitation driven correlation based mapping method (PCM) for identifying the critical source areas of non-point source pollution. Journal of Hydrology. 2015;524:100–10
  16. 16. Chen X, Wo F, Chen C, Fang K. Seasonal changes in the concentrations of nitrogen and phosphorus in farmland drainage and groundwater of the Taihu Lake region of China. Environmental Monitoring and Assessment. 2010;169(1–4):159–68 pmid:19757114
  17. 17. Du X, Su J, Li X, Zhang W. Modeling and Evaluating of Non-Point Source Pollution in a Semi-Arid Watershed: Implications for Watershed Management. Clean-Soil Air Water. 2016;44(3):247–55
  18. 18. Zhang B-l, Cui B-h, Zhang S-m, Wu Q-y, Yao L. Source apportionment of nitrogen and phosphorus from non-point source pollution in Nansi Lake Basin, China. Environmental Science and Pollution Research. 2018;25(19):19101–13 pmid:29725920
  19. 19. Liu L, Li Z, Xiao H, Wang B, Nie X, Liu C, et al. The transport of aggregates associated with soil organic carbon under the rain-induced overland flow on the Chinese Loess Plateau. Earth Surface Processes and Landforms. 2019;44(10):1895–909
  20. 20. Zhang X, Song J, Wang Y, Deng W, Liu Y. Effects of land use on slope runoff and soil loss in the Loess Plateau of China: A meta-analysis. Science of the Total Environment. 2021;755 pmid:33035990
  21. 21. Liang Y, Jiao J, Tang B, Cao B, Li H. Response of runoff and soil erosion to erosive rainstorm events and vegetation restoration on abandoned slope farmland in the Loess Plateau region, China. Journal of Hydrology. 2020;584
  22. 22. Anache JAA, Wendland EC, Oliveira PTS, Flanagan DC, Nearing MA. Runoff and soil erosion plot-scale studies under natural rainfall: A meta-analysis of the Brazilian experience. Catena. 2017;152:29–39
  23. 23. Choi Y, Won C, Shin M, Park W, Lee S, Shin Y, et al. Effect of No-Till Practice on Runoff and Nonpoint Source Pollution from an Intensively Farmed Field in Korea. Irrigation and Drainage. 2016;65:175–81
  24. 24. Saleem SR, Zaman QU, Schumann AW, Madani A, Chang YK, Farooque AA. IMPACT OF VARIABLE RATE FERTILIZATION ON NUTRIENTS LOSSES IN SURFACE RUNOFF FOR WILD BLUEBERRY FIELDS. Applied Engineering in Agriculture. 2014;30(2):179–85
  25. 25. Alavinia M, Saleh FN, Asadi H. Effects of rainfall patterns on runoff and rainfall-induced erosion. International Journal of Sediment Research. 2019;34(3):270–8
  26. 26. Yue-Qing X, Xiao-Mei S, Xiang-Bin K, Jian P, Yun-Long C. Adapting the RUSLE and GIS to model soil erosion risk in a mountains karst watershed, Guizhou Province, China. Environmental Monitoring and Assessment. 2008;141(1–3):275–86 pmid:17879137
  27. 27. Rahman MR, Shi ZH, Cai C, Zhu D. Assessing soil erosion hazard -a raster based GIS approach with spatial principal component analysis (SPCA). Earth Science Informatics. 2015;8(4):853–65
  28. 28. Prasannakumar V, Vijith H, Abinod S, Geetha N. Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) and geo-information technology. Geoscience Frontiers. 2012;3(2):209–15
  29. 29. Wang R, Zhang S, Yang J, Pu L, Yang C, Yu L, et al. Integrated Use of GCM, RS, and GIS for the Assessment of Hillslope and Gully Erosion in the Mushi River Sub-Catchment, Northeast China. Sustainability. 2016;8(4)
  30. 30. Li Z, Huang J, Zeng G, Nie X, Ma W, Yu W, et al. Effect of Erosion on Productivity in Subtropical Red Soil Hilly Region: A Multi-Scale Spatio-Temporal Study by Simulated Rainfall. Plos One. 2013;8(10) pmid:24147090
  31. 31. Wen-bo Z, Yun X, Bao-yuan L. Estimation of rainfall erosivity using rainfall amount and rainfall intensity. Geographical Research. 2002(03):384–90
  32. 32. Suhua F, Baoyuan L, Guiyun Z, Zhongxuan S, Xiaoli Z. Calculation tool of topographic factors. Science of Soil and Water Conservation. 2015;13(05):105–10
  33. 33. McCool D K., Brown L C., Foster G R., Mutchler C K., Meyer L D. Revised Slope Steepness Factor for the Universal Soil Loss Equation. Transactions of the ASAE. 1987;30(5):1387–96
  34. 34. Liu BY, Nearing MA, Risse LM. Slope Gradient Effects on Soil Loss for Steep Slopes. Transactions of the ASAE. 1994;37(6):1835–40
  35. 35. Xiankui Z, Jinghua X, Xiuqin L, Yujiang D, Dewu G. A Study on the Soil Loss Equation in Heilongjiang Province. Bulletin of Soil and Water Conservation. 1992(04):1–9+18
  36. 36. Wei X, Li X, Wei N. Reducing runoff and soil loss using corn stalk juice at plot scale. Soil Tillage Research. 2017;168
  37. 37. Zheng M, Cai Q, Cheng QJG. Modelling the runoff-sediment yield relationship using a proportional function in hilly areas of the Loess Plateau, North China. 2008;93(3–4):288–301
  38. 38. Dai T, Wang L, Li T, Qiu P, Wang J. Study on the Characteristics of Soil Erosion in the Black Soil Area of Northeast China under Natural Rainfall Conditions: The Case of Sunjiagou Small Watershed. Sustainability. 2022;14(14):8284
  39. 39. Keesstra SD. Impact of natural reforestation on floodplain sedimentation in the Dragonja basin, SW Slovenia. Earth Surface Processes and Landforms. 2007;32(1)
  40. 40. Bryan RB. Soil erodibility and processes of water erosion on hillslope. Geomorphology. 2000
  41. 41. Lane LJ, Hernandez M, Nichols M. Processes controlling sediment yield from watersheds as functions of spatial scale. Environmental Modelling Software. 1997;12(4):355–69
  42. 42. Deuchars SA, Townend J, Aitkenhead MJ, Fitzpatrick EA. Changes in soil structure and hydraulic properties in regenerating rain forest. Soil Use Management. 1999
  43. 43. Zuazo VHD, Rodriguez Pleguezuelo CRJSaEL. Soil-erosion and runoff prevention by plant covers: a review. 2009
  44. 44. Fang H. Impacts of soil conservation measures on runoff and soil loss in a hilly region, Northern China. AGRICULTURAL WATER MANAGEMENT. 2021;247
  45. 45. De Baets S, Poesen J, Knapen A, Barbera GG, Navarro JA. Root characteristics of representative Mediterranean plant species and their erosion-reducing potential during concentrated runoff. Plant and Soil. 2007;294(1–2):169–83
  46. 46. Yuan L, Yue K, Gu Z, Chen H, Chi Y. Analysis of Rainfall Factors and Soil Erosion in Different Soil and Water Conservation Measures in the Karst Plateau-Mountain. Polish Journal of Environmental Studies. 2021;30(6):5343–9
  47. 47. He J, Cai Q, Li G, Wang Z. Integrated erosion control measures and environmental effects in rocky mountainous areas in northern China. International Journal of Sediment Research. 2010;25(3):294–303
  48. 48. Fang H. Responses of Runoff and Soil Loss on Slopes to Land Use Management and Rainfall Characteristics in Northern China. International Journal of Environmental Research and Public Health. 2021;18(18) pmid:34574507
  49. 49. Wang S, Fu B, Gao G, Liu Y, Zhou J. Responses of soil moisture in different land cover types to rainfall events in a re-vegetation catchment area of the Loess Plateau, China. Catena. 2013;101:122–8
  50. 50. Fang H. Effect of soil conservation measures and slope on runoff, soil, TN, and TP losses from cultivated lands in northern China. Ecological Indicators. 2021;126
  51. 51. Peidong X, Pengcheng Z, Weiyuan H, Bingzhe T, Binghui H, Tianyang L. Response of nitrogen and phosphorus losses to different farming treatments in purple sloping farmland under simulated rainfall. Science of Soil and Water Conservation. 2021;19(06):69–76
  52. 52. Lin Q, Xu Q, Wu F, Li T. Effects of wheat in regulating runoff and sediment on different slope gradients and under different rainfall intensities. Catena. 2019;183
  53. 53. Wan Y, El-Swaify SA. Sediment Enrichment Mechanisms of Organic Carbon and Phosphorus in a Well-Aggregated Oxisol. Journal of Environmental Quality. 1998;27(1):132–8
  54. 54. Li W, Lei Q, Yen H, Zhai L, Liu H. Investigation of watershed nutrient export affected by extreme events and the corresponding sampling frequency. Journal of Environmental Management. 2019;250:109477 pmid:31479934
  55. 55. Fang H. The Effect of Soil Conservation Measures on Runoff, Soil Erosion, TN, and TP Losses Based on Experimental Runoff Plots in Northern China. WATER. 2021;13(17)
  56. 56. Tang L, Yang D, Hu H, Gao B. Detecting the effect of land-use change on streamflow, sediment and nutrient losses by distributed hydrological simulation. Journal of Hydrology. 2011;409(1–2):172–82
  57. 57. Chen S-Q, Long Y, Yan D-C, Gao M, Li J-C, Xu G-X, et al. Characteristics of Nitrogen and Phosphorus Output and Loss Flux in the Shipanqiu Watershed, Three Gorges Reservoir Area. Environmental Science. 2020;41(3):1276–85 pmid:32608629
  58. 58. Hatt BE, Fletcher TD, Deletic A. Hydrologic and pollutant removal performance of stormwater biofiltration systems at the field scale. Journal of Hydrology. 2009;365(3–4):310–21
  59. 59. He X-L, Zheng Z-c, Li T-x. Effects of Tillage Practices on Soil Erosion and Phosphorus Loss in Sloping Cropland of Purple Soil. Scientia Agricultura Sinica. 2013;46(12):2492–500
  60. 60. Rui-xia C, Jing L, Kai-kai D, Yu X, Wei W, Fang F, et al. Characteristics of Nitrogen and Phosphorus Losses and Runoff in a Typical Purple Soil Watershed in the Three Gorges Reservoir Area. Environmental Science. 2019;40(12):5330–9