Next Article in Journal
The Key to the Sustainability and Conservation of Extractive Reserves in the Amazon
Previous Article in Journal
The Impact of Energy Poverty Alleviation on Carbon Emissions in Countries along the Belt and Road Initiative
Previous Article in Special Issue
Internal Force Mechanism of Pisha Sandstone as a Soil Amendment to Improve Sandy Soil Structural Stability in Mu Us Sandy Land
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 11
10.3390/su16114683
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Influence of Rural Urbanization on the Change in Soil Organic Matter of Farmland in Northeast China

by
Xiaodong Wang
1,
Long Fei
1,
Yu An
2,
Xiaohui Liu
2 and
Mei Zhang
1,*
1
College of Geographical Sciences, Changchun Normal University, Changchun 130032, China
2
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(11), 4683; https://doi.org/10.3390/su16114683
Submission received: 5 April 2024 / Revised: 23 May 2024 / Accepted: 27 May 2024 / Published: 31 May 2024
(This article belongs to the Special Issue Soil Quality and Innovation in Agriculture: Dynamics, Indicators, and Sustainability)
Browse Figures
Versions Notes

Abstract

:
Studying the impact of urbanization on changes in the soil organic matter (SOM) content of farmland plays an important role in determining the influence mechanism of urbanization regarding regional environmental change. Taking the farmland in Yushu City, northeast China, as the research area, in May 2019, 68,393 sample plots (each plot: 60 m × 60 m) were set up in farmland and sampled to measure the SOM content of each plot while combining image data from the same period in the study area (resolution: 60 m). This investigation was based on 17 levels divided by the size of areas occupied by residences, using residential areas as the center and a radius of 60 m. Through a gradually buffered extrapolation method combined with mathematical functions, the influence of rural urbanization on the changes in SOM content was revealed. These results showed that the slope of the linear function between the SOM content and the residential area level was greater than zero and that with the continuous advancement of urbanization, the SOM content had an increasing trend. When urbanization advanced to the stage of larger cities, large-scale mechanized production led to land degradation. When urbanization advanced to the stage of towns, intensive cultivation was beneficial for land restoration. The findings of this study provide a reference basis for future studies of the relationship between rural urbanization and agricultural mechanization around the world.

Graphical Abstract

1. Introduction

Much of the rural population has shifted to urban areas, and methods of agricultural production have also changed in various regions in the world with global urbanization. Meanwhile, the content of soil organic matter (SOM) is also constantly changing in farmland [1]. In this context, it is important to study the relationship between the SOM content and the process of rural urbanization, in order to reveal the reasons for the continuous decline in farmland quality and to maintain sustainable agricultural development [2,3]. By applying the PARAFAC model, one such study revealed that the SOM content surrounding suburbs decreased with the urbanization of Beijing, China, by 2015 [4]. Elsewhere, with the advancement of rural urbanization in the Heilongjiang Reclamation Area of China in 2014, based on data from 113 agricultural pastures, it was revealed that agricultural mechanization was extensively used, and fertilizer was excessively applied, which caused soil compaction, soil degradation, and a rapid reduction in SOM [5]. Likewise, in Harbin, China, the rural urbanization was found to have changed the soil properties, reduced the SOM content by 59 g/kg to 38 g/kg, and produced severe soil degradation [6]. This phenomenon is not unique to China; in the Nile Delta, the urban area increased from 452 km2 in 1972 to 2644 km2 in 2017, and over the same period, the loss of SOM (cultivation layer: 0–75 cm) from farmland increased from 25,000 Mg/km2 to 141,000 Mg/km2 [7]. Similarly, in the Tombel region of southwestern Cameroon, the SOM content of farmland decreased from 2.77 ± 1.09 kg/m2 to 2.16 ± 0.93 kg/m2, while the urban area expanded by 83% from 1985 to 2017 [8]. The results of these studies have demonstrated that as urbanization has steadily advanced, SOM has continually decreased, and farmland degradation has gradually intensified.
The influence of changes in the methods of agricultural production on the SOM content has great significance for sustainable agricultural development during the process of rural urbanization [9,10]. Rural urbanization has expanded the scale of centralized agricultural centralized management, and the mechanization rate has also increased in China. At the same time, the agricultural environment has declined as the SOM has decreased on farmland [11]. For instance, in Yunnan Province, China, mechanization led to a decrease in farmland fertility, an SOM decrease, and accelerated nutrient loss, while the land occupation of urban construction increased by 49.8% from 1989 to 2018, paired with increases in agricultural product and fertilizer application [12]. Similarly, in Shanghai, China, changes in the crop structure and farming system have led to a decrease in the SOM content of farmland with rapid urbanization, which has brought about the conversion of the soil carbon sink to a carbon source since 2004 [13]. Elsewhere, in West Java, Indonesia, a large amount of chemical fertilizer has been applied to inhibit microbial activity, and the SOM content has reduced in tandem, creating an urgent need to alter the islanders’ methods of land cultivation [14]. These cases exemplify how, with the urbanization of rural areas and the transformation of methods of agricultural production, the SOM content of farmland has decreased. However, there are other cases where the SOM content of farmland has shown an increasing trend with the application of modern technology in certain areas of the world. For instance, there was one report that minimal tillage (MT) and no tillage (NT) techniques significantly reduced SOM loss by 17% and 63%, respectively, compared to traditional tillage [15]. Furthermore, in soybean growing areas in Europe, low-input organic agriculture enhanced the SOM content of farmland compared to traditional mechanization and maintained sustainable agricultural development [16]. Therefore, it can be said that agricultural mechanization has a very complex impact on the SOM of farmland, while the process of rural urbanization has a profound impact. Nevertheless, existing research on the impact of urbanization on the SOM content has mainly concentrated on certain specific stages of the urbanization process. There is a lack of research on the continuous changes in SOM content during the process of continuous urbanization in rural areas.
With the continuous advancement of urbanization, the SOM content is also constantly changing. Accordingly, it seems necessary to distinguish at which stage of urbanization the SOM content is highest. Different levels of urbanization resulted in differences in the cultivation methods of surrounding farmland [17], and further clarification is needed of how differences in agricultural production have affected soil quality. Based on this, we took the farmland distribution in Yushu City, China, as the research area, and we used buffer analysis based on a field investigation in order to study the relationship between different residential levels and SOM content, understand the changes in SOM content during the process of urbanization, and identify the interrelationships between the urbanization process, agricultural production, and SOM content. Specifically, the main scientific problem addressed in this study is to determine the effectiveness of the following methods:
(1) Understanding the change in the SOM content of farmland with an increase in residential area level.
(2) Revealing the variation characteristics of SOM content extending outward from residential areas within each residential area level.
(3) Analyzing the impacts of alteration in the methods of agricultural production on the change in the SOM content with the advancement of rural urbanization. We can provide suitable research methods for future studies that will support the effective protection and utilization of farmland resources and establishing sustainable development models suitable for local agriculture.

2. Materials and Methods

2.1. Study Area

Northeast China is the main grain producing area nationally, with the highest yield in China [18]. However, due to its the prolonged cultivation, the quality of the farmland has significantly decreased [19]. Meanwhile, the urbanization level in Northeast China has increased (e.g., from 27.61% in 1952 to 63.15% in 2019). The advancement of urbanization has led to continuous improvements in agricultural mechanization and great changes in the methods of farming. As such, this is an ideal area for studying the impacts of rural urbanization on SOM changes on farmland [20]. The study area is located at Yushu City and belongs to the center of the Northeast China Plain (126°01′44″–127°05′09″ E, 46°30′57″–45°15′02″ N), covering an area of 4712 km2 [21] (Figure 1). The soil type is mainly black soil in the study area [22], matching the most typical black soil distribution in Northeast China. Its high grain yield and fertile soil have made the area a national commodity grain base in China [23,24].

2.2. Data Collection and Processing

In May 2019, soil sampling was conducted in the field. At this point, frozen soil had just melted, and spring plowing had not yet begun, so it was convenient for field sampling. Each plot measured 60 m × 60 m, matching the minimum resolution of the imagery we accessed. Firstly, GPS was used to measure the longitude and latitude at the center of a plot. Plots were set up in all field parcels of Yushu City to ensure the coverage of all farmlands distributed in our study area. Then, five samples (each sample: 0.5 kg) were taken at the four corners and center points of each plot from the cultivated soil layer. Last, these five samples were mixed into one sample, and we took 1 kg of that mixture as our single one soil sample for each plot. All soil samples were sealed and brought to the laboratory. After drying them and removing impurities, the soil samples were weighed, ground, and passed through a 0.1 mm sieve. The SOM content of each soil sample was determined using the potassium dichromate sulfuric acid method [25]. Finally, the SOM content of each plot in the study area (totaling 68,393) was obtained. The SOM content ranged from 1.9% to 3.9%, with a mean of 2.54% and a standard deviation (STD) of 0.25% among 68,393 plots. The Mesoscale Landsat 8 Operational Land Imager (OLI) was used to collect the remote sensing data. The imaging period was May 2019, which was consistent with the soil sampling time. The data were then subjected to data importation (import), multi-band image blending (utilities), image cropping (subset), and geometric correction for images (geometric correction). All residential areas and farmland were classified in the study area using the random forest classification method in the ENVI environment. After comparing the longitude and latitude of each plot in its image with the data from our field investigations, an SOM content was assigned to each plot to form a new data layer using the ArcGIS model.

2.3. Data Analysis

2.3.1. Buffer Analysis

In the new data layer, according to the Sturges formula, all residential areas were evenly divided into 15 levels based on land occupation scale, with a difference of 145,000 square meters between adjacent levels [26,27,28]. The large land area of Yushu Development Zone (only one) and the location of the municipal government (urban area) were not included in the formula calculation. Instead, the two areas were separately classified as the 16th (municipal government) and the 17th (development zone) level, and so a total of 17 levels of residential areas were determined. Because the 14th level had no residential areas, it was not included in the following analysis (Table 1).
Within each level, buffer analysis was conducted on surrounding farmland, centered around residential areas and gradually expanded from the inside out in farmland. Because the minimum resolution of the plot was 60 m, the buffer radius was also chosen as 60 m. Using the 1st level of residential areas as an example, buffer analysis was introduced: first, we centered around the residential area of the 1st level, circled the area of a farmland with a radius of 60 m in the outer farmland, and the mean was calculated of the SOM content of farmland in the circled area. Then, we took the boundary of the circled area in the first round as the starting line, circled again a new farmland area with a radius of 60 m, and calculated the mean of the SOM content within the new circled area. Thus, this proceeded, with continuous buffer analysis outward until all farmland was covered in the study area. At last, a series of the circled areas was formed, centered around the 1st level of residential area. Following this, buffer analysis was performed on the 2nd level of residential area to form another series of the circled areas, and so on. Each level formed a corresponding circled areas series, with a total of 16 series, meaning that each level of residential area was a series of circled areas.

2.3.2. Function Fitting and Step-By-Step Elimination

The 1st (linear) to the 6th functions were performed, using the residential area level as the independent variable and the SOM mean of the residential area level as the dependent variable. In addition to the linear function, the 6th function (R2 = 0.5457, p < 0.05) (Figure 2) was selected as appropriate to reveal the relationship between the level of residential area and the SOM content according to the change characteristics of the 6th function curve. At each residential area level, the distance (circled area and residential area) was taken as the independent variable and the SOM mean in the circled area as the dependent variable; the linear function was performed, and we calculated the slope of each function (Table 1). Then, using these slopes as the dependent variable and the residential area level as the independent variable, six functions were fitted from the linear to the 6th function. Finally, since the 4th function had the best fitting effect (R2 = 0.6489, p < 0.05) (Figure 3), we selected the linear function and the 4th function with which to analyze the relationship between the SOM content and distance (circled area and residential area) at different residential area levels.
At each level of residential area, the distance (circled area and residential area) was taken as the independent variable, the SOM mean in the circled area was taken as the dependent variable, and the six functions (from the 1st to the 6th) were fitted separately. The function was selected with the biggest R2, and we calculated the SOM maximum of the function curve. In addition, the distance corresponding to the SOM maximum of the function was computed too (Table 1). Then, the SOM maximum of each function with the biggest R2 for each level was taken as the dependent variable, the residential area level was taken as the independent variable, the linear function was performed, and we calculated the slope of the function (Figure 4). Using the same method, another linear function was also obtained, and the slope of the function was also calculated, where another function referred to the distance (circled area and residential area) as the independent variable and the SOM mean in the circled area as the dependent variable (Figure 4).
Finally, the eliminated correlation coefficient (R) for the SOM maximum and the distance corresponding to the SOM maximum was calculated by a step-by-step elimination analysis. In the first round of calculation, the pair of data numbered as 1 among the 16 pairs for SOM maximum and the distance corresponding to SOM maximum, which represented SOM maximum and the distance corresponding to SOM maximum of the residential area as 1, was eliminated, and the R of the remaining 15 pairs of the residential area levels was calculated. Then, the pair of the residential area level numbered as 1 was returned, while the pair numbered as 2 was removed, and the R of the remaining residential area levels was calculated again. Following this process, 16 Rs were obtained. From these, the level with the biggest R among the 16 was eliminated in the first round. The same elimination method was then used in the second round for the remaining 15 levels. The elimination process was considered complete after the second round since the R reached the level of significance (p < 0.01) in the second round. With the same method of step-by-step elimination, the Rs were calculated of SOM minimum and the distance corresponding to SOM minimum. It was only in step six that a significant positive R was achieved (eliminated R: 0.64, p < 0.05), and in step five that a significant negative R was achieved (eliminated R: −0.67, p < 0.05). In doing so, the relationship was revealed between the residential area level and SOM maximum based on the slope of the linear function and these step-by-step elimination processes. With the same methods, the SOM minimum and the distance corresponding to the SOM minimum were calculated for each level’s function curve, using the SOM minimum as the dependent variable and the distance (circled area and residential areas) as the independent variable for the linear function. The impact of urbanization on the SOM content was revealed by the slope of the linear function and by the eliminated R between the SOM minimum and the distance corresponding to the SOM minimum.

3. Results

3.1. Urbanization Process

The rural population decreased from 1.04 million in 1991 to 0.73 million in 2018, a decrease of 29.8%. The urban population, meanwhile, increased from 0.13 million to 0.15 million, an increase of 8.3%, while the urbanization rate increased from 12.9% to 21.2% in Yushu City [29,30]. Alongside the processes of urbanization in the study area, the comprehensive input–output efficiency of farmland increased from 0.54 in 2006 to 1 in 2015 [31]. The intensity of soil development also continually increased, while the SOM content decreased by 2.46% to 5.46% compared to the initial background [32]. The results of our analysis of variance of the SOM content in different plots (p < 0.01) indicated that severe differential degradation occurred in Yushu City.

3.2. The Change in Means of SOM Contents among Different Residential Area Levels

The slope of the linear function was greater than 0 (k = 0.0112) (Figure 2) between the level of residential areas (independent variable) and the mean of SOM content in all circled areas (dependent variable), with an upward trend, indicating that as the level of residential areas increased, the SOM content had an increasing trend, and the urbanization was beneficial for soil recovery. The sixth function with the best fitting effect (R2 = 0.5457) had a downward trend at a level of less than 3.79 (Figure 2), indicating that once farmland was cultivated, soil degradation immediately began. Between the levels of 3.79 and 8.30 (Figure 2), the curve showed an upward trend, indicating that with the concentration of the population towards the town, the intensity of farmland remediation increased, and soil was restored to a certain extent. However, between the levels of 8.30 and 12.71 (Figure 2), the curve had a downward trend, indicating that when urbanization reached this stage, the increase in the development intensity of farmland led to a degradation trend in soil. After the 12.71 level (Figure 2), the curve had an upward trend again, indicating that when the urbanization scale reached this stage, the economic and technological strengths were enhanced, which led to the use of advanced agricultural production methods, raising the improvement ability and quality of soil.

3.3. The Changes in SOM Content Extending Outward from Residential Areas

3.3.1. Linear Trend Change

The slope of the linear function was greater than 0 (k = 0.0007) (Figure 3) between the residential area level (independent variable) and the slope (dependent variable) of the function. The function refers to the distance (circled area and residential area) as the independent variable and the SOM mean in the circled area as the dependent variable, with an upward trend. As the residential area level increased, the SOM content increased with distance, indicating that the advancement of urbanization has continuously expanded the spatial scope of soil restoration. The variation in the sixth function with the best fitting effect between the residential area level (independent variable) and the slope (dependent variable) of the function showed that when the residential area was less than the 4.14 level, the slope was negative (from −0.024 to 0) (Figure 3), indicating that in the early stage of farmland cultivation, the farther away from the residential area, the more extensive the farmland development, and the more severe the soil degradation. Between the levels of 4.14 and 6.89, the slope was positive (from 0 to 0.001) (Figure 3), indicating that as the population concentrated towards the town, the farther away from the residential area, the higher the SOM content. The high-intensity use of farmland around the residential area led to rapid soil degradation near the town. At levels 6.89 to 12.75, the slope was negative (from −0.002 to 0) (Figure 3), indicating that when urbanization reached this stage, people began to pay attention to land degradation; however, land restoration was focused on the farmland near high-level towns, which the farmland near the high-level town could restore to a certain extent. Between levels 12.75 and 15.76, the slope was positive (from 0 to 0.0005) (Figure 3), which indicated that when urbanization was reached at this stage, the advanced management mode expanded the scope of farmland restoration to areas far away from towns. After the 15.76 level, the slope was negative (from −0.003 to 0) (Figure 3), indicating that when urbanization expanded to the urban stage, the surrounding farmland shifted towards intensive farming and that the increased farmland input significantly increased the SOM content.

3.3.2. The Change in SOM Maximum

The slope of the linear function was greater than 0 (k = 0.0076) (Figure 4) between the residential area level (independent variable) and the SOM maximum of the function (dependent variable), where the function referred to the distance (circled area and residential area) as the independent variable and the SOM mean in the circled area as the dependent variable. The slope of the linear function was 1554 (Figure 4) between the residential area level (independent variable) and the distance corresponding to the SOM maximum of the function (dependent variable) with the best fitting effect, which were all positive, indicating that as the residential area level increased, the SOM maximum continued to increase and the distance corresponding to the SOM maximum also continued to increase. Urbanization has continuously expanded the spatial scope of surrounding farmland to improve the SOM content. The SOM maximum continued to rise, and the distance corresponding to the SOM maximum continually moved away from residential areas. According to the step-by-step elimination analysis, except for the 17th and 8th levels in the first and second rounds, the R was 0.66 (p < 0.01) between the residential area level and the SOM maximum fitting curve of each level, indicating that except for these two levels, the SOM maximum continued to increase with the distance from the residential area in other residential area levels. This shows that advancements in urbanization could improve the soil quality and that the intensity and spatial scope of improvement constantly expand. The SOM maximum was 2.47% of the 17th level for the function with the best fitting effect between the distance (circled area and residential area) as the independent variable and the SOM mean in the circled area as the dependent variable (Figure 4). The distance corresponding to the SOM maximum of the function of the 17th level was 60 m (Figure 4), indicating that the farmland adjacent to the residential area was a production base for agricultural food. The result also indicated that only when urbanization was advanced to this level, a specialized agricultural production base would emerge in the surrounding areas of urban areas.

3.3.3. The Change in SOM Minimum

The slope of the linear function was negative (k = −0.0455) (Figure 5) between the residential area level (independent variable) and the SOM minimum of the function, where the function referred to the distance (circled area and residential area) as the independent variable and the SOM mean in the circled area as the dependent variable with the best fitting effect. The slope of the linear function was positive (k = 1198) (Figure 5) between the residential area level (independent variable) and the distance corresponding to the SOM minimum of the function with the best fitting effect (dependent variable), indicating that as the spatial scope of soil restoration continued to expand, the interference with soil also continued to expand. The slope (k = 1554) of the linear function for the distance corresponding to the SOM maximum of the function was greater than that (k = 1198) of the function for the distance corresponding to the SOM minimum of the function (Figure 4 and Figure 5), indicating that as the level of the residential areas increased, the intensity of soil improvement was greater than the intensity of soil interference. As such, urbanization had a promoting effect on soil recovery. In addition, the R was 0.05 between the residential area level and the SOM minimum function of each level, which showed that there was a weak positive correlation. This revealed that urbanization is beneficial for soil restoration. The results of the step-by-step elimination analysis showed that a significant negative correlation was only reached in step 5, and a significant positive correlation was only reached in step 6. With the continuous advancement of urbanization, the contradiction between the improvement and degradation of soil was very complex, with the soil moving in the direction of recovery in a tortuous process.

4. Discussion

4.1. The Damage of Cultivation and Reclamation

Our results indicated that once the land was cultivated, soil degradation occurred immediately. Specifically, as land was cultivated, surface vegetation was destroyed, soil erosion increased, SOM loss accelerated, land degradation accelerated, and recovery was slow [33]. This phenomenon occurs around the world. For instance, in the semi-arid grassland area of Nigeria, after land reclamation, the SOM content rapidly decreased [34]. Elsewhere, in the northern region of Ethiopia, the SOM content decreased significantly after forest land reclamation, and the decrease in surface soil was greater than that in deep soil [35]. Meanwhile, in southern Belgium, when forest and grassland were converted into farmland, the SOM content decreased by 14.4 t C/ha and 23.4 t C/ha, respectively [36]. Therefore, we propose that the first development of farmland should be particularly cautious, and that it is necessary to monitor and maintain nutrients to prevent a rapid decline in soil quality and even irreversible situations after reclamation.

4.2. Non-Universal Application of Large-Scale Mechanized Production

The maximum and minimum of the best effect functions for the 13th level were 2.58% and 2.17% between the distance (circled area and residential area) (independent variable) and the SOM mean (dependent variable), which were the lowest in all residential area levels. When rural urbanization reached this stage (13th level), the intensity of soil improvement was the smallest, the interference was the greatest, and soil degradation was the most significant. This stage was a key stage in the transition from rural to urban areas, where a large number of farmers had left agricultural production and entered other industries in urban areas. People’s production and lifestyle underwent fundamental changes [37]. With the concentration of the population towards cities, the number of people engaged in agricultural activities in rural areas has decreased; however, the demand for rural land output is higher so as to support more citizens [38]. Therefore, the production mode was mechanized, and in order to improve crop yield, the application of fertilizers and pesticides also rapidly increased [39]. With the popularization of mechanization, the application of fertilizers and pesticides became more extensive, ultimately leading to a significant decrease in SOM content in farmland [40]. The current production model of agricultural mechanization has been continuously popularized worldwide [41], but the agricultural production model of large machinery is not applicable to all regions of the world. In previous research, it was found that, in Eastern China, the coupling degree was between 0.30 and 0.50 between new urbanization and the agricultural ecological environment. At this time, a large number of farmers entered cities, the rural mechanization of production was fully promoted, and farmland degradation was the most severe [42]. Elsewhere, after six years of the implementation of mechanization in South Pampas, Argentina, the SOM content in farmland had decreased by 0.7% [43]. Meanwhile, in a province of Iran, the SOM elasticity was −1.58 under long-term agricultural mechanization, and the SOM elasticity was −1.58, posing a threat to farmland sustainability and making it impossible to sustain the agricultural mechanization of production in this Iranian province [44]. These findings demonstrate that as large-scale mechanized production spreads globally in rural areas, it is necessary to strengthen the monitoring of the SOM content. Once the SOM is rapidly reduced, measures should be taken to restore the production capacity of farmland to increase conservation support.

4.3. The Suitability of Intensive Cultivation

In the second round of the step-by-step elimination between the maximum and the distance corresponding to the maximum for all levels of residential areas, the eighth level was removed. Meanwhile, in the first round of the step-by-step elimination between the minimum and the distance corresponding to the minimum for all levels of the residential area, the eighth level was also removed. These findings revealed that the eighth level did not follow the rule of an increasing or decreasing SOM content as the distance to residential areas increased. The mean (2.56%) of SOM content of the eighth level was the highest, and the difference (0.19%) between the maximum (2.66%) and minimum (2.47%) was the smallest in all residential area levels. When rural urbanization advanced to this stage (town level) [45], the contrast between improvement and interference was very small, the extrapolation in the region tended to be consistent, and the spatial heterogeneity was the smallest in all levels of residential areas, indicating that cultivation methods for farmland were the most favorable for SOM restoration at this stage. We found that the process of rural urbanization was at the township level, and that the agricultural production occurred through intensive cultivation. At this stage, farmers had relatively high incomes and a strong dependence on their farmland, and they were willing to make the investments necessary for land restoration. Mechanization was mainly focused on miniaturization, which was convenient and flexible and caused little interference with the farmland. At this stage, farmers were likely to invest more resources into the land to ensure higher returns in the future, and the soil fertility was well maintained thanks to their higher investments. Intensive cultivation was the most effective means of farmland restoration in China, as well as globally, as intensive cultivation plays an important role in maintaining the SOM content in farmland [46]. For instance, in a case from the Czech Republic, traditional agriculture led to soil degradation, whereas organic or bio-based agricultural management practices based on intensive cultivation restored soil health and productivity. After more than 50 years of intensive cultivation, for the high SOM input generated by organic agriculture, the mean of SOM content increased by 5.51%, and soil fertility was restored [47]. Elsewhere, in the eastern region of Iran, due to the adoption of intensive farming, the SOM content increased by 0.3% from 2004 to 2018 [48]. Similarly, in southeastern Brazil, intensive management increased the soil carbon storage in farmland at a rate of 0.28 Mg/ha from 2010 to 2016, promoting an SOM increase while also playing a positive role in carbon sequestration [49]. Therefore, from the perspective of soil protection, if the process of rural urbanization in China is not advanced to a higher level and is instead maintained at the township level, this is beneficial for soil restoration. Currently, China’s rural areas are undergoing urbanization through a process of merging villages and towns, with a drop in the rural population and an intensive cultivation trend of agricultural production. In Nancun township and Qingdao, Shandong Province, merging villages and towns activated the rural economy while maintaining effective land conservation, and intensive cultivation was a beneficial approach in maintaining the sustainable development of soil fertility [50]. In sum, based on our research results, intensive cultivation seems to be beneficial for increasing the SOM content. Furthermore, based on the literature, it seems that promoting the merging of villages and towns is an effective measure for soil protection in China.

5. Conclusions

With the continuous advancement of rural urbanization, the SOM content undergoes different changes in different stages of the process, which we determined according to the changes in linear functions or the maxima and minima of the best functions. With the continuous improvement of urbanization from low to high levels, the restoration and disturbance of soil expanded outward from residential areas, and their intensity increased during this expansion. Our specific key findings were as follows: (1) As the level of residential areas increased, the SOM had an increasing trend (k = 0.0112), and the maximum of SOM continuously increased with the distance away from residential areas (k = 1554). As such, the advancement of urbanization was beneficial for soil recovery. (2) At the first level of residential areas, the SOM mean was only 2.51%. This showed that once the land was cultivated, soil degradation immediately occurred. Meanwhile, at the 13th level of residential areas, the SOM mean was the lowest (2.48%). This demonstrated that when rural areas began to transition to larger cities, the popularization of large-scale mechanized production had the greatest impact on land disturbance and caused the most obvious degradation. (3) At the eighth level, the SOM mean was the highest (2.56%), and the difference between the maximum and minimum was the smallest (0.19%). This highlighted that when rural urbanization advanced to the town level, the improvement was great, and interference was low. At this time, intensive cultivation was conducive to soil restoration and protection.

Author Contributions

Conceptualization, X.W.; methodology, M.Z.; writing—original draft preparation, L.F.; investigation, Y.A.; writing—review and editing, X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Joint Funds of the National Natural Science Foundation of China (No. U2243230); the National Natural Science Foundation of China (No. 42230516); Technology Development Program of Jilin Province (No. YDZJ202301ZYTS524); and Natural Science Foundation of Changchun Normal University (No. CSJJ2022009ZK).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available from the author upon reasonable request.

Conflicts of Interest

The authors declares no conflicts of interest.

References

  1. Zhang, Z.P.; Ding, J.L.; Zhu, C.M.; Chen, X.Y.; Wang, J.Z.; Han, L.J.; Ma, X.; Xu, D. Bivariate empirical mode decomposition of the spatial variation in the soil organic matter content: A case study from NW China. Catena 2021, 206, 105572. [Google Scholar] [CrossRef]
  2. Liu, S.W.; Zhang, P.Y.; Li, J. Analyzing the urbanization driving force in Heilongjiang Reclamation Areas. Geogr. Res. 2013, 32, 2066–2078, (In Chinese with English Abstract). [Google Scholar]
  3. Li, W.B.; Wang, D.Y.; Liu, S.H.; Zhu, Y.L. Measuring urbanization-occupation and internal conversion of peri-urban cultivated land to determine changes in the peri-urban agriculture of the black soil region. Ecol. Indic. 2019, 102, 328–337. [Google Scholar] [CrossRef]
  4. Chai, L.W.; Huang, M.K.; Fan, H.; Wang, J.; Jiang, D.L.; Zhang, M.J.; Huang, Y. Urbanization altered regional soil organic matter quantity and quality: Insight from excitation emission matrix (EEM) and parallel factor analysis (PARAFAC). Chemosphere 2019, 220, 249–258. [Google Scholar] [CrossRef] [PubMed]
  5. Ye, Y.J.; Qi, Q.W.; Jiang, L.L.; Zhang, A. Impact factors of grain output from farms in Heilongjiang reclamation area based on geographical detector. Geogr. Res. 2018, 37, 171–182, (In Chinese with English Abstract). [Google Scholar]
  6. Wang, W.J.; Wang, Q.; Zhou, W.; Xiao, L.; Wang, H.M.; He, X.W. Glomalin changes in urban-rural gradients and their possible associations with forest characteristics and soil properties in Harbin City, Northeastern China. J. Environ. Manag. 2018, 224, 225–234. [Google Scholar] [CrossRef] [PubMed]
  7. Abd-Elmabod, S.K.; Fitch, A.C.; Zhang, Z.H.; Ali, R.R.; Jones, L. Rapid urbanisation threatens fertile agricultural land and soil carbon in the Nile delta. J. Environ. Manag. 2019, 252, 109668. [Google Scholar] [CrossRef] [PubMed]
  8. Nguemezi, C.; Tematio, P.; TSilatsa, F.B.; Yemefack, M. Spatial variation and temporal decline (1985–2017) of soil organic carbon stocks (SOCS) in relation to land use types in Tombel area, South-West Cameroon. Soil. Till Res. 2021, 213, 105114. [Google Scholar] [CrossRef]
  9. Wu, K.N.; Han, C.J.; Sun, Z.Y.; Lv, Q.L.; Feng, X.W.; Fu, Q.L.; Li, L. Spatial and temporal distribution of soil fertility as affected by urbanization. Chin. J. Soil. Sci. 2007, 38, 242–246, (In Chinese with English Abstract). [Google Scholar]
  10. Parenti, A.; Zegada-Lizarazu, W.; Pagani, E.; Monti, A. Soil organic carbon dynamics in multipurpose cropping systems. Ind. Crop Prod. 2022, 187, 115315. [Google Scholar] [CrossRef]
  11. Zhang, Y.N.; Long, H.L. Agricultural production transition and its environmental effects: Research progress and prospect. J. Nat. Resour. 2022, 7, 1691–1706. [Google Scholar] [CrossRef]
  12. Yang, C.L.; Li, C.F.; Tian, Y.F.; Zhou, H.; He, J.N.; Liu, J.P.; Ma, Y.Q. Situation of non-grain cropland and its eco-environmental effect in Yunnan Province. Miner. Explor. 2020, 11, 2573–2591. [Google Scholar]
  13. Zhao, M.; Hu, J.; Tang, Q.H. Evaluation of soil carbon sinks in Shanghai. Admin Tech. Environ. Monit. 2012, 24, 17–22, (In Chinese with English Abstract). [Google Scholar]
  14. Moeskops, B.; Sukristiyonubowo; Buchan, D.; Sleutel, S.; Herawaty, L.; Husen, E.; Wati, R.S.; Setyorini, D.; Neve, S.D. Soil microbial communities and activities under intensive organic and conventional vegetable farming in West Java, Indonesia. Appl. Soil. Ecol. 2010, 45, 112–120. [Google Scholar] [CrossRef]
  15. Cillis, D.; Maestrini, B.; Pezzuolo, A.; Marinello, F.; Sartori, L. Modeling soil organic carbon and carbon dioxide emissions in different tillage systems supported by precision agriculture technologies under current climatic conditions. Soil. Till Res. 2018, 183, 51–59. [Google Scholar] [CrossRef]
  16. Scharer, M.L.; Dietrich, L.; Kundel, D.; Mader, P.; Kahmen, A. Reduced plant water use can explain higher soil moisture in organic compared to conventional farming systems. Agr. Ecosyst. Environ. 2022, 332, 107915. [Google Scholar] [CrossRef]
  17. Abebe, M.G. Impacts of urbanization on food security in Ethiopia. A review with empirical evidence. J. Agr. Food Res. 2024, 15, 100997. [Google Scholar] [CrossRef]
  18. Zhang, M.; Chen, T.; Latifmanesh, H.; Feng, X.M.; Cao, T.H.; Qian, C.R.; Deng, A.X.; Song, Z.W.; Zhang, W.J. How plant density affects maize spike differentiation, kernel set, and grain yield formation in Northeast China? J. Integr. Agr. 2018, 17, 1745–1757. [Google Scholar] [CrossRef]
  19. Li, H.Q.; Yao, Y.F.; Zhang, X.J.; Zhu, H.S.; Wei, X.R. Changes in soil physical and hydraulic properties following the conversion of forest to cropland in the black soil region of Northeast China. Catena 2021, 198, 104986. [Google Scholar] [CrossRef]
  20. Yang, Q.F.; Zhang, P.Y.; Li, J.; Liu, W.X.; He, X.L. Development Level and Spatio-temporal Evolution of Agricultural Modernization in Northeast China. Sci. Geogr. Sin. 2022, 9, 1588–1599, (In Chinese with English Abstract). [Google Scholar]
  21. Fang, Y.G.; Shi, K.J.; Niu, C.C. A comparison of the means and ends of rural construction land consolidation: Case studies of villagers’ attitudes and behaviours in Changchun City, Jilin province, China. J. Rural. Stud. 2016, 47, 459–473. [Google Scholar] [CrossRef]
  22. Yu, R.; Wang, Y.; Wang, C.X.; Wang, Q.C.; Cui, Z.W. Survey of heavy metal pollution and source identification of black soil in Zea mays L. cultivated region of Yushu city. China 2017, 26, 1788–1794, (In Chinese with English Abstract). [Google Scholar]
  23. Wang, D.D.; Shi, X.Z.; Lu, X.X.; Wang, H.J.; Yu, D.S.; Sun, W.X.; Zhao, Y.C. Response of soil organic carbon spatial variability to the expansion of scale in the uplands of Northeast China. Geoderma 2010, 154, 302–310. [Google Scholar] [CrossRef]
  24. Zhao, H.B.; Ma, Y.J.; Miao, C.H. Integrated assessment and obstacle factors of environmental carrying capacity of national strategic economic zones based on entropy and catastrophe progression methods: A case study in Changchun-Jilin-Tumenjiang development and opening-up pilot area. Sci. Geogr. Sin. 2015, 35, 1525–1532, (In Chinese with English Abstract). [Google Scholar]
  25. Institute of Soil Science, Chinese Academy of Sciences. Soil Physicochemical Analysis; Routledge: Beijing, China, 1977. [Google Scholar]
  26. Imbusch, G.F.; Yen, W.M. The mcCumber and sturge formula. J. Lumin. 2000, 85, 177–179. [Google Scholar] [CrossRef]
  27. Zhang, L.J.; Wu, Y.; Hu, M.H.; Guo, W.Y. Automatic image analysis of episcleral hemangioma applied to the prognosis prediction of trabeculotomy in Sturge–Weber syndrome. Displays 2022, 71, 102118. [Google Scholar] [CrossRef]
  28. Du, M.M.; Li, H.Y.; Jin, X.; Roshnianfard, A.; Zhou, H. Extracting micro-topographical features of farmland by using UAV remote sensing image. Agri Eng. 2023, 13, 77–81, (In Chinese with English Abstract). [Google Scholar]
  29. Li, Q.Y.; Li, Y.F.; Gong, H.Y.; Wang, D.Y. Prediction of urban and rural construction lands supply and demand indicators under human-land Linking in Changchun and Jilin. J. Northeast Normal Univ. (Nat. Sci. Ed.) 2017, 49, 120–127, (In Chinese with English Abstract). [Google Scholar]
  30. Liu, Z.M.; Xiu, C.L.; Wei, Y.; Fang, Z.X. Spatial Pattern of Population Change and Its Influencing Factors in Northeast China from 1990 to 2010. Northwest Popul. 2017, 5, 19–26, (In Chinese with English Abstract). [Google Scholar]
  31. Yang, Y. Study on the Coupling Relationship between the Land Input-Output Efficiency and Urbanization Quality in Jilin Province. Ph.D. Thesis, Jilin University, Changchun, China, 2018. [Google Scholar]
  32. Liu, C.; Liu, X.Y.; Liu, P. Analysis on the changes of organic matters and their influencing factors of typical black soil areas in Songliao plain. Geol. Resour. 2020, 29, 550–555, (In Chinese with English Abstract). [Google Scholar]
  33. Wick, A.F.; Ingram, L.G.; Stahl, P.D. Aggregate and organic matter dynamics in reclaimed soils as indicated by stable carbon isotopes. Soil. Biol. Biochem. 2009, 41, 201–209. [Google Scholar] [CrossRef]
  34. Xu, W.Q.; Chen, X.; Luo, G.P.; Lin, Q. Using the CENTURY model to assess the impact of land reclamation and management practices in oasis agriculture on the dynamics of soil organic carbon in the arid region of North-western China. Ecol. Complex. 2011, 8, 30–37. [Google Scholar] [CrossRef]
  35. Okolo, C.C.; Gebresamuel, G.; Zenebe, A.; Haile, M.; Eze, P.N. Accumulation of organic carbon in various soil aggregate sizes under different land use systems in a semi-arid environment. Agr. Ecosyst. Environ. 2020, 297, 106924. [Google Scholar] [CrossRef]
  36. Stevens, A.; Wesemael, B. Soil organic carbon stock in the Belgian Ardennes as affected by afforestation and deforestation from 1868 to 2005. For. Ecol Manag. 2008, 256, 1527–1539. [Google Scholar] [CrossRef]
  37. Gu, S.Z.; Wu, Y.B.; Wang, J.R. Thinking and strategic countermeasures of cultivating the fifth-tier cities. Chin. J. Pop. Sci. 2016, 6, 2–10, (In Chinese with English Abstract). [Google Scholar]
  38. Yang, S.D.; Yang, X.; Zhang, J.X.; Gao, X.; Zhou, J.; Wu, X.L. Assessing the impacts of rural depopulation and urbanization on vegetation cover: Based on land use and nighttime light data in China, 2000–2020. Ecol. Indic. 2024, 159, 111639. [Google Scholar] [CrossRef]
  39. Liao, L.W.; Long, H.L.; Ma, E.P. Rural labor change and farmland use transition. Econ. Geogr. 2021, 41, 148–155, (In Chinese with English Abstract). [Google Scholar]
  40. Cambou, A.; Chevallier, T.; Barthès, B.G.; Derrien, D.; Cannavo, P.; Bouchard, A.; Allory, V.; Schwartz, C.; Vidal, B.L. The impact of urbanization on soil organic carbon stocks and particle size and density fractions. J. Soil. Sediment. 2022, 23, 792–803. [Google Scholar] [CrossRef]
  41. Loon, J.V.; Woltering, L.; Krupnik, T.J.; Baudron, F.; Boa, M.; Govaerts, B. Scaling agricultural mechanization services in smallholder farming systems: Case studies from sub-Saharan Africa, South Asia, and Latin America. Agr. Syst. 2020, 180, 102792. [Google Scholar] [CrossRef]
  42. Cai, J.; Li, X.P.; Liu, L.J.; Chen, Y.Z.; Wang, X.W.; Lu, S.H. Coupling and coordinated development of new urbanization and agro-ecological environment in China. Sci. Total Environ. 2021, 776, 145837. [Google Scholar] [CrossRef]
  43. Virginia, A.; Zamora, M.; Barbera, A.; Castro-Franco, M.; Domenech, M.; Geronimo, E.D.; Costa, J.L. Industrial agriculture and agroecological transition systems: A comparative analysis of productivity results, organic matter and glyphosate in soil. Agr. Syst. 2018, 167, 103–112. [Google Scholar] [CrossRef]
  44. Amiri, Z.; Asgharipour, M.R.; Campbell, D.E.; Sabaghi, M.A. Comparison of the sustainability of mechanized and traditional rapeseed production systems using an emergy-based production function: A case study in Lorestan Province, Iran. J. Clean. Prod. 2020, 258, 120891. [Google Scholar] [CrossRef]
  45. Fang, C.L.; Cui, X.G.; Liang, L.W. Theoretical analysis of urbanization and eco-environment coupling coil and coupler control. Acta Geogr. Sin. 2019, 74, 2529–2546, (In Chinese with English Abstract). [Google Scholar]
  46. Pudelko, K.; Kolodziel, J.; Mankowski, J. Restoration of minesoil organic matter by cultivation of fiber hemp (Cannabis sativa L) on lignite post-mining areas. Ind. Crop Prod. 2021, 171, 113921. [Google Scholar] [CrossRef]
  47. Juricova, A.; Chuman, T.; Zizala, D. Soil organic carbon content and stock change after half a century of intensive cultivation in a chernozem area. Catena 2022, 211, 105950. [Google Scholar] [CrossRef]
  48. Maleki, S.; Karimi, A.; Zeraatpisheh, M.; Poozeshi, R.; Feizi, H. Long-term cultivation effects on soil properties variations in different landforms in an arid region of eastern Iran. Catena 2021, 206, 105465. [Google Scholar] [CrossRef]
  49. Bieluczyk, W.; Piccolo, M.C.; Pereira, M.G.; Moraes, M.T.; Soltangheisi, A.; Bernardi, A.C.C.; Pezzopane, J.R.M.; Oliveira, P.P.A.; Moreira, M.Z.; Camargo, P.B.; et al. Integrated farming systems influence soil organic matter dynamics in southeastern Brazil. Geoderma 2020, 371, 114368. [Google Scholar] [CrossRef]
  50. Gu, C.L.; Li, Y.; Han, S.S. Development and transition of small towns in rural China. Habitat. Int. 2015, 50, 110–119. [Google Scholar] [CrossRef]
Sustainability 16 04683 g001
Figure 1. The distribution of residential areas and croplands in the study area.
Figure 1. The distribution of residential areas and croplands in the study area.
Sustainability 16 04683 g001
Sustainability 16 04683 g002
Figure 2. The sixth function with the best fitting effect between residential area level (independent variable) and the mean of SOM content in all circled areas (dependent variable).
Figure 2. The sixth function with the best fitting effect between residential area level (independent variable) and the mean of SOM content in all circled areas (dependent variable).
Sustainability 16 04683 g002
Sustainability 16 04683 g003
Figure 3. The 4th function with the best fitting effect between residential area level (independent variable) and the slope (dependent variable) of the function; the function referred to the distance (circled area and residential area) as the independent variable and the SOM mean in the circled area as the dependent variable. The dotted line was 0 for the slope of the function; the dashed line was the linear function (k = 0.007) for the slope of the function.
Figure 3. The 4th function with the best fitting effect between residential area level (independent variable) and the slope (dependent variable) of the function; the function referred to the distance (circled area and residential area) as the independent variable and the SOM mean in the circled area as the dependent variable. The dotted line was 0 for the slope of the function; the dashed line was the linear function (k = 0.007) for the slope of the function.
Sustainability 16 04683 g003
Sustainability 16 04683 g004
Figure 4. The linear function between residential area level (independent variable) and the SOM maximum of the function. The linear function between residential area level (independent variable) and the distance corresponding to the SOM maximum of the function (dependent variable). The function referred to the distance (circled area and residential area) as the independent variable and the SOM mean in the circled area as the dependent variable with the best fitting effect. The dotted line is the linear function (k = 0.0076) for the SOM maximum; the dashed line is the linear function (k = 1554) for the distance corresponding to the SOM maximum.
Figure 4. The linear function between residential area level (independent variable) and the SOM maximum of the function. The linear function between residential area level (independent variable) and the distance corresponding to the SOM maximum of the function (dependent variable). The function referred to the distance (circled area and residential area) as the independent variable and the SOM mean in the circled area as the dependent variable with the best fitting effect. The dotted line is the linear function (k = 0.0076) for the SOM maximum; the dashed line is the linear function (k = 1554) for the distance corresponding to the SOM maximum.
Sustainability 16 04683 g004
Sustainability 16 04683 g005
Figure 5. The linear function between the residential area level (independent variable) and the SOM minimum of the function. The linear function between the residential area level (independent variable) and the distance corresponding to the SOM minimum of the function (dependent variable), where the function referred to the distance (circled area and residential area) as the independent variable and the SOM mean in the circled area as the dependent variable with the best fitting effect. The dotted line is the linear function between the residential area level and the SOM minimum of the function; the dashed line is the linear function between the residential area level and the distance corresponding to the SOM minimum of the function.
Figure 5. The linear function between the residential area level (independent variable) and the SOM minimum of the function. The linear function between the residential area level (independent variable) and the distance corresponding to the SOM minimum of the function (dependent variable), where the function referred to the distance (circled area and residential area) as the independent variable and the SOM mean in the circled area as the dependent variable with the best fitting effect. The dotted line is the linear function between the residential area level and the SOM minimum of the function; the dashed line is the linear function between the residential area level and the distance corresponding to the SOM minimum of the function.
Sustainability 16 04683 g005
Table 1. Classification of residential areas and function selection of each level.
Table 1. Classification of residential areas and function selection of each level.
LevelLevel Range
(Ten Thousand m2)		X1X2 (%)X3 (m)X4 (%)X5 (m)
10–14.5−0.03082.559002.372700
214.5–290.00062.5696602.485340
329–43.4−0.00072.61602.4512,900
443.4–57.9−0.00722.5876202.2120,880
557.9–72.4−0.00212.57602.4726,160
672.4–86.9−0.00162.5912,8402.4726,400
786.9–101.4−0.00122.6741,4602.4428,320
8101.4–115.90.00342.6621,2402.475220
9115.9–130.3−0.00182.6651002.4542,780
10130.3–144.8−0.00012.6668,6402.444620
11144.8–159.30.00142.6148,2402.1460
12159.3–173.8−0.00152.5987602.4633,060
13173.8–188.3−0.00392.5815,8402.1746,380
15202.8–217.20.00012.6628,8602.4047,460
16859.30.00042.5938,5202.3460
172612.8−0.00282.82602.4728,740
Note: X1, the slope of the linear function between the distance (circled area and residential area) (independent variable) and the SOM mean (dependent variable) in the circled area; X2, the maximum of function with the best fitting effect between the distance (circled area and residential area) (independent variable) and the SOM mean (dependent variable) in the circled area. X3, the distance corresponding to the maximum of functions with the best fitting effect between the distance (circled area and residential area) (independent variable) and the SOM mean (dependent variable) in the circled area. X4, the maximum of functions with the best fitting effect between the distance (circled area and residential area) (independent variable) and the SOM mean (dependent variable) in the circled area. X5, the distance corresponding to the maximum of functions with the best fitting effect between the distance (circled area and residential area) (independent variable) and the SOM mean (dependent variable) in the circled area.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, X.; Fei, L.; An, Y.; Liu, X.; Zhang, M. The Influence of Rural Urbanization on the Change in Soil Organic Matter of Farmland in Northeast China. Sustainability 2024, 16, 4683. https://doi.org/10.3390/su16114683

AMA Style

Wang X, Fei L, An Y, Liu X, Zhang M. The Influence of Rural Urbanization on the Change in Soil Organic Matter of Farmland in Northeast China. Sustainability. 2024; 16(11):4683. https://doi.org/10.3390/su16114683

Chicago/Turabian Style

Wang, Xiaodong, Long Fei, Yu An, Xiaohui Liu, and Mei Zhang. 2024. "The Influence of Rural Urbanization on the Change in Soil Organic Matter of Farmland in Northeast China" Sustainability 16, no. 11: 4683. https://doi.org/10.3390/su16114683

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop