Next Article in Journal
A Comparison of the Physiological Traits and Gene Expression of Brassinosteroids Signaling under Drought Conditions in Two Chickpea Cultivars
Previous Article in Journal
Effects of Two Sowing Methods on the Growth, Yield, and Quality of Hybrid Rice under Mechanical Transplantation
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 13
Issue 12
10.3390/agronomy13122962
agronomy-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

Planting Year- and Climate-Controlled Soil Aggregate Stability and Soil Fertility in the Karst Region of Southwest China

by
Wei-Chun Zhang
1,
Wei Wu
2 and
Hong-Bin Liu
1,*
1
College of Resources and Environment, Southwest University, Chongqing 400716, China
2
College of Computer and Information Science, Southwest University, Beibei, Chongqing 400716, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(12), 2962; https://doi.org/10.3390/agronomy13122962
Submission received: 10 October 2023 / Revised: 16 November 2023 / Accepted: 28 November 2023 / Published: 30 November 2023
(This article belongs to the Section Farming Sustainability)
Browse Figures
Versions Notes

Abstract

:
The effects of long-term monocropping systems combined with climate on soil water aggregate stability (WSA) and soil fertility in the karst region of Southwest China (KRSWC) are unclear. Our research was conducted in the KRSWC, wherein tobacco (Nicotiana tabacum) production is characterized by heavy fertilization and continuous monocropping. The tobacco fields in the study area have similar soil types and fertilization and tillage practices and are spread over an area of 11,500 km2. A total of 568 topsoil samples were collected in 2021. Soil fertility was reflected using the soil fertility index (SFI), which was calculated using the minimum data set method with six soil fertility-related factors, namely, soil pH, soil organic matter, cation exchange capacity, available nitrogen, available phosphorus, and available potassium. Results showed that long-term planting generally promoted soil fertility levels and WSA content. WSA and SFI had inconsistent spatial distribution patterns likely due to different climate-driven effects. WSA variability was greatly controlled by precipitation (Spearman correlation coefficient [r] = −0.49, p < 0.01), whereas SFI variability was mostly dominated by temperature (r = −0.36, p < 0.01). The levels of SFI and WSA were optimal under conditions of low temperature and precipitation and poor under conditions of high temperature and precipitation. Moreover, long-term planting could alleviate the negative effects of climate on SFI and WSA in the KRSWC. The results of this study could provide valuable information on fertilization and climate-adapted strategies for tobacco fields in the KRSWC.

1. Introduction

Soil water aggregate stability (WSA) is an important indicator of soil erosion risk and soil sustainability [1,2,3,4]. Soil fertility, which is defined as the ability of soil to sustain plant growth, is generally assessed using a scheme proposed in the 1990s [5,6,7]. The level of soil fertility determines the performance of sustainable agriculture [7,8,9]. According to Jenny’s factorial model of soil formation, soil properties are influenced by numerous factors, including climate and human activities [10]. Climate determines the moisture and temperature of soil formation [10]. Planting years, an important human factor for arable soils, are related to the duration of material input and anthropogenic disturbances [11]. A clear understanding of the effect of planting years and climate on soil fertility and structure is important for the sustainable management of regional land use and early warning of unfavorable trends.
Climatic conditions, including temperature and precipitation, are regarded as the key factors affecting soil properties on a regional scale [2,12,13,14]. WSA is governed by four well-known mechanisms (i.e., raindrop impact, slaking, differential swelling, and physicochemical dispersion) [15,16], all of which are closely related to precipitation. Our previous studies confirmed that precipitation in the karst region of Southwest China (KRSWC) exerts a strong negative control on WSA, whereas temperature has a weaker effect than precipitation on WSA [2]. The relative importance of temperature and precipitation for soil fertility indicators may depend on regional biological constraints [14]. For example, compared with other factors, temperature has a greater influence on soil fertility indicators in China [13] and Europe [12], whereas precipitation contributes more highly in eastern Australia, which is under strong water limitation [17]. Past studies revealed positive correlations between WSA and soil fertility with field experiments [18,19,20]. However, given the different effects of climate on soil fertility and structure, their positive correlation may break down at regional scales. Therefore, exploring how climate affects the spatial distribution of WSA and soil fertility may help the proposal of climate-adapted agricultural strategies and is thus necessary.
Continuous soil management might alter soil properties, and such change varies across planting years [11,21,22,23,24,25]. For example, the storage of soil carbon, nitrogen, and phosphorus in tea gardens [11] and ginseng fields [25] increases with an increase in planting years. Under conventional farming practices, the soil structure in Brazilian tobacco (Nicotiana tabacum) fields continuously declines with increasing planting years [21]. By contrast, long-term no-tillage was found to be an effective strategy for improving soil fertility in a Brazilian oxisol [24]. Overall, planting years affect soil fertility and aggregate stability differently under various land use types and soil management strategies. The KRSWC is one of the least underdeveloped regions in China, with a per capita rural income of 86% of the national average in 2016 [26]. Tobacco cultivation, as an important income source for farmers in the KRSWC [27], is characterized by high fertilization and continuous monocropping [28,29]. Previous long-term field experiments revealed the effects of different tobacco rotation systems on soil fertility and aggregate stability [29,30] but paid little attention to the effects of cropping systems that are widely used in actual tobacco production. Moreover, climate and planting year might interactively influence soil fertility and WSA, which has received scant attention.
We assumed that long-term tobacco monocropping with heavy fertilization affects soil fertility and structure and that climate might break the positive correlation between soil fertility and structure at regional scales. This study was implemented on KRSWC tobacco fields with similar soil types, fertilization, and tillage. Specifically, after quantifying soil fertility (as indicated by the soil fertility index (SFI)) using a classical process [5,6,7], we measured the single and joint controls of planting years and climate on SFI and WSA.

2. Materials and Methods

2.1. Study Area

The study site is located in southwestern China, which is part of the largest contiguous karst zone in the world [26] (Figure 1). The spatial location of the study area is 107°80′–109°31′ E and 28°33′–28°99′ N. The region is predominantly mountainous and hilly with a humid subtropical monsoon climate. In accordance with the Chinese Soil Genetic Classification System, the vast majority of the soils in the tobacco fields in the study area are classified as yellow soil that developed from Permian or Triassic limestones. In the World Reference Base for Soil Resources system, yellow soil is equivalent to haplic alisols or haplic luvisols. Additional information on the climate, topography, geology, and soil types of the region can be found in our previous publication [2]. Tobacco was first planted in the study area in the 1980s. Tobacco, an important cash crop that helps local farmers escape poverty, is under the unified, professional, and careful guidance of the local agricultural department. Tobacco nurtured in the greenhouse is transplanted to the field in early May each year and harvested approximately 120 days later. Tobacco fields are fallow during the rest of the year to maintain soil quality. Conventional tillage processes are used to manage tobacco fields, and agricultural machinery is uniformly equipped by the local agricultural department. The tobacco farms in the study area rely solely on rainwater as their source of moisture and lack supplementary irrigation systems. Field surveys in 2017 showed that fertilization practices in the region are similar, with an inorganic nitrogen fertilizer application rate of 112.5 kg per hectare, which is roughly four times the application rate of organic nitrogen fertilizers. Organic fertilizers include oil cake fertilizer, farmyard manure, and amino acid fertilizer. The amount of fertilizer applied has remained almost unchanged in recent years.

2.2. Data and Pre-Processing

Topsoil (0–20 cm) samples were collected using random sampling prior to tobacco planting and fertilization (March 2021). For ensuring representativeness, 3–5 subsamples were collected within a 5 m radius from the center of a field-determined sampling point. Each sample was geotagged with GPS. Approximately 1 kg of soil was collected from each subsample. Approximately 2 kg of well-mixed soils was obtained using the quartering method and placed in ziplock bags for subsequent chemical analysis. Meanwhile, undisturbed soils were collected at the same location, and each subsample was carefully stripped off along the natural profile. Ultimately, a total of 1 kg of soil was stored in aluminum containers. Information about planting years was recorded at the site through field observations and farmer interviews (Table 1). Samples with missing information were deleted. Finally, 568 samples collected from yellow soil were used in the current study.
Aggregates with different diameters were measured on the basis of the wet sieving method [31] using undisturbed soil, and WSA was defined as the sum of aggregates with particle sizes larger than 0.25 mm. A high WSA generally indicates high soil aggregate stability. Refer to Zhang et al. for additional details [2]. Various soil chemical characteristics, including soil pH, soil organic matter (SOM), available nitrogen (AVN), phosphorus (AVP), and potassium (AVK), as well as cation exchange capacity (CEC), were measured. The following analytical methods were utilized to measure the above characteristics: SOM, potassium dichromate oxidation; AVN, NaOH hydrolysis; AVP, HCl–H2SO4 extraction and colorimetry; AVK, ammonium acetate extraction and colorimetry; soil pH, glass electrode method (soil/water 1:2.5); and CEC, titration [32].
Two climate raster maps of mean annual temperature (MAT) and mean annual total precipitation (MAP) with a spatial resolution of 1 km were sourced from our previous study [2]. The raster maps of five commonly used topographic factors, including aspect (Asp), slope (Slp), topographical wetness index (TWI), plan curvature (PLC), and profile curvature (PRC), derived from the 30 m digital elevation model were used for data analysis [2]. Altitude was not considered because the strong co-linearity between altitude and MAT may disturb analyses.

2.3. Data Analytical Tools

Descriptive statistics were used to examine the degree of concentration and variability across the original data set. Spearman correlation coefficients were calculated between response variables (WSA and SFI) and environmental factors because not all data were normally distributed. Given that the assumptions of homogeneity of variances and normality always were violated, the statistical comparisons of WSA and SFI were performed using the Kruskal–Wallis test with Games–Howell post-hoc analysis. These statistical analyses were completed with SPSS v22.
Principal component analysis (PCA) is a popular technique for data dimensionality reduction that aims to replace a large number of original variables with a few variables (i.e., principal component (PC)) and retain the maximum amount of information [33]. The PCs with eigenvalues greater than 1 were used to determine how many PCs should be retained [6,34]. Variance partitioning analysis (VPA) was applied to quantify the interpretation ratio of different environmental factors to WSA and SFI. If covariance existed among the factors in the same group, the data were down-dimensioned using PCA before VPA was performed. PCA and VPA were executed with the vegan package of R 4.0.5.
Local indicators of spatial association (LISA) analysis, which was developed by Anselin [35], was used to analyze the local spatial autocorrelation and existence of clusters in the spatial arrangement of a given variable. LISA analysis provided the ability to detect the local spatial associations and clustering of WSA or SFI [36]. Five clustering patterns were identified: (i) no significant spatial dependence; (ii) high–high, high value in a high-value neighborhood; (iii) high–low, high value in a low-value neighborhood; (iv) low–high, low value in a high-value neighborhood; and (v) low–low, low value in a low-value neighborhood. LISA analysis was conducted with ArcGIS v.10.3. Furthermore, we obtained the spatial distributions of SFI and WSA with ordinary kriging in ArcGIS v.10.3.

2.4. Soil Fertility Evaluation

The total data set for soil fertility evaluation consists of 6 soil fertility-related factors, including pH, SOM, CEC, AVN, AVP, and AVK. A widely used scheme (minimum data set (MDS) method) for soil fertility evaluation was adopted [5,6,34] and it consists of three steps. (1) MDS construction: PCA and Spearman correlation analysis were used to select the most appropriate factors indicating the fertility characteristics. As described by Doran and Parkin [5] and Qi et al. [6], only PCs with eigenvalues greater than 1 were used to represent the local soil fertility characteristics. Factors with weighted loading values within 10% of the highest weighted loading among each PC were selected. Redundant factors with strong correlations were eliminated. (2) Factor scoring and weighting: based on MDS, the scores of soil fertility-related factors were assigned using two standard scoring functions (upper limit function [Equation (1)] and peak limit function [Equation (2)]. The weights of each factor were determined according to the communality [6]. Commonality is the sum of the squared loading for each variable. It spans from 0 to 1, and a value close to 1 denotes greater variance. (3) Calculation of SFI: SFI was assessed for each sample site according to Equation (3). The higher the SFI, the better the soil fertility. The SFI was divided into five grades using the isometric division method [34]:
f ( x ) = 0.1 x < L 0.9 × x − L U − L + 0.1 L < x ≤ U 1 x > U
f ( x ) = 0.1 x ≤ L 1   o r   x > U 2 0.9 × x − L L 2 − L 1 + 0.1 L 2 < x ≤ L 1 1 L 2 < x ≤ U 1 0.9 × x − U 1 U 2 − U 1 + 0.1 U 1 < x ≤ U 2
where f(x) is the score, x is the factor value, and L and U are the lower and upper threshold values, respectively. The upper and lower limits used in this work referred to Qi et al. [6] and Qu et al. [7] (Table S1):
S F I = ∑ i = 1 n W i × N i
where Wi is the assigned weight, which was determined in accordance with communality [6], Ni is the factor score, and n is the number of factors in MDS.

3. Results

3.1. Descriptive Statistics

The descriptive statistics for soil fertility-related factors and the WSA of topsoil (0–20 cm) are shown in Table 2. WSA accounted for 51.67 ± 4.06% of the soil bulk. Soil fertility-related factors were graded (Table 3 and Table 4) in accordance with the MLR [37], CNSSO [38], and tobacco growing experience. The samples had average AVN, AVP, and AVK contents of 155.73, 64.63, and 441.37 mg/kg, respectively (Table 2), with more than half of them assigned to Grade I (Table 3). Most of the samples had Grade II CEC contents with a mean ± standard deviation of 17.44 ± 3.13 cmol/kg. The SOM content was low, and 54% of the samples had Grade III SOM contents. Approximately 63.2% of the samples had soil pH values at sub-suitable and unsuitable levels for tobacco growth (Table 4). The study area has a complex topography but little climatic variability (Table 2). WSA, pH, MAT, and MAP exhibited low variability (CV < 15%) [39]. SOM, CEC, and AVN showed moderate variability (16–35%). By contrast, AVK, AVP, Asp, Slp, and TWI presented high variability (CV > 35%).

3.2. Soil Fertility Assessment

The first three PCs had eigenvalues >1 and explained 82.18% of the variation in all soil fertility-related factors (Table 5). For each PC, the variables within 10% of the highest factor loading were reserved as key indicators for SFI calculation. AVN was removed due to its strong correlation with SOM (r > 0.9, p < 0.01). The other five factors were all chosen. Their weights were quantified using their communality values. SOM was assigned the highest weight (0.24), followed by pH (0.20), CEC (0.20), AVP (0.18), and AVK (0.17). Approximately 64.85% of the soil samples were in Grade I (Table S2). This result implies that local tobacco fields have excellent soil fertility.

3.3. WSA Content and Soil Fertility in Different Planting Years

The differences in WSA, SFI, and key soil fertility-related factors in different groups of planting years (p < 0.05) were investigated (Figure 2). The numbers of soil samples in the short, middle, and long planting year groups were 46, 327, and 195, respectively. No significant differences in AVP and AVK were found between different planting years. WSA, SFI, pH, SOM, and CEC significantly differed between the long and short planting year groups but were not always significantly different between the middle and short planting year groups or middle and long planting year groups. The general trend of WSA, SFI, and key soil fertility-related factors showed that the contents of WSA, SFI, SOM, CEC, AVP, and AVK increased and pH declined with increasing planting years.

3.4. Climate Affects SFI and WSA Differently

The spatial distribution of WSA differed from that of SFI (Figure 3). WSA values were low and high in the east and west, respectively, whereas low SFI values were concentrated in the central area. The spatial structure derived from LISA analysis further confirmed the spatial differences between WSA and SFI (Figure 3). For example, in the central area, SFI exhibited the significant spatial distribution of high–high relationships, whereas WSA generally displayed the spatial distribution of low–low relationships. This result implies that the soils of the tobacco fields in the study region are mainly characterized by low WSA values and high SFI values. In general, the spatial distributions of WSA and SFI were inconsistent at the regional scale, likely due to the different influences exerted by climate on SFI and WSA. The VPA method was used to verify the pure and joint effects of precipitation, temperature, and topography on WSA and SFI. MAP, MAT, and terrain factors accounted for 18.26%, 0.05%, and 1.15% of the total variation in WSA, respectively, and for 0.76%, 11.18%, and 0.55% of the total variation in SFI, respectively (Figure 4a,b). Precipitation dominantly controlled WSA variability, whereas temperature governed SFI variability. Moreover, linear and nonlinear fits revealed monotonically negative correlations (p < 0.01) between MAP and WSA and between MAT and SFI with Spearman correlation coefficients of −0.49 and −0.36 (p < 0.01), respectively (Figure 4c,d).
Raw data were categorized into four groups, namely, HT–HP, HT–LP, LT–HP, and LT–LP, in accordance with the median values of MAP and MAT. HT and LT denote high and low temperature groups, respectively, and HP and LP indicate high and low precipitation groups, respectively. WSA differed significantly between the HP and LP groups, and SFI differed significantly between the HT and LT groups (Figure 5). This result is consistent with the finding that WSA and SFI were dominated by precipitation and temperature, respectively (Figure 4). The soil in the LT–LP group exhibited high WSA and SFI contents.
The differences in WSA and SFI across various climates and planting years were further explored (Figure 6). In each planting year group, WSA significantly differed between different precipitation groups, whereas SFI significantly differed between different temperature groups (similar to the result in Figure 5). In the HT–HP group, the levels of WSA and SFI were significantly higher in the long planting year group than in the middle and short planting year groups. Planting year had no significant effect on WSA and SFI in the other climate groups.

4. Discussion

As indicated by SFI values, the tobacco fields in the study area exhibited excellent soil fertility, with 64.79% and 32.92% of the soil samples classified as Grades I and II, respectively (Table S2). By contrast, the tobacco fields in the study area showed low soil aggregate stability with a mean WSA content of 51.8%, and almost all samples (98.98%) were in an unstable state [2], likely due to their low SOM content. Previous studies have discovered that the effect of SOM on aggregates always exceeds that of other soil chemical properties [4,42,43]. Therefore, for the protection of soil aggregates, fertilization strategies should be modified to increase SOM content. For example, the application of organic fertilizer should be increased. The benefits of organic fertilizers for SOM promotion and aggregate protection have been widely confirmed [30,44,45]. To illustrate, Zou et al. [29] revealed that manure amendment increases soil macroaggregates and soil organic carbon. Tobacco straws, as valuable organic fertilizer materials, are also effective in enhancing SOM and physical structure [46,47].
WSA, SFI, SOM, and CEC increased significantly with the increase in planting years (Figure 2). Overall, the current cropping patterns are beneficial to tobacco soil. Consistent with previous results [29,48,49], this above finding suggests that the long-term application of organic fertilizers (e.g., manure and oil cake fertilizers) might neutralize the negative effects of continuous monocropping on soil fertility and aggregate stability. For example, soil fertility in tobacco fields significantly improved after 11 years of consecutive tobacco cropping with three different organic fertilizers [48]. However, in line with a reported finding [28], soil pH declined with increasing planting years (Figure 3). This phenomenon is an early warning that needs to be taken seriously because declining soil pH potentially threatens soil aggregate stability [3] and fertility [50]. Generally, although the current cropping patterns could improve soil fertility and structure in the long term, soil pH decline should be controlled.
Previous works at the field scale usually considered fertility and aggregate stability to be positively correlated [18,19,20]. However, our study found that the spatial distribution of soil fertility and WSA was inconsistent at the regional scale (Figure 3), presumably due to the different effects of climate on SFI and WSA. Temperature controlled the vast majority of variability in SFI, whereas precipitation mainly governed WSA. Our previous work demonstrated the critical negative control of WSA by precipitation [2]. The relative contribution of temperature and precipitation to soil fertility varied across environmental conditions [12,13,17,51], which depend on the main limiting factor of biological activity [14]. For example, Ballabio et al. [12] and Song et al. [13], respectively, revealed that temperature has great effects on soil fertility factors in Europe and China. The role of precipitation is greater in areas under strong water limitation (e.g., eastern Australia) [17]. Given that our study area is not under water limitation (annual precipitation of 1000–1500 mm), temperature dominated SFI variability. The negative correlation between temperature and soil fertility found in our research is consistent with that discovered with studies in southwestern China [50,52,53] and may be closely related to soil microbial activity [14]. Increased planting years significantly and positively affected WSA and SFI in environments with high temperature and rainfall (Figure 6). This result shows that long-term tobacco planting could alleviate the negative effects of climate on soil fertility and structure.

5. Conclusions

Our regional-scale study was implemented on tobacco fields in the KRSWC, wherein tobacco production is characterized by high fertilization and continuous monocropping. SFI values showed that in the study area, the soils under tobacco production are of good fertility but have low SOM levels, which may result in unstable aggregates. Long-term tobacco planting promotes soil fertility and WSA. The spatial variabilities of WSA and SFI are largely negatively controlled by precipitation and temperature, respectively. Climate controls WSA and SFI differently, leading to inconsistencies in the spatial distribution of these factors. WSA and SFI are highest under the LT–LP condition and lowest under the HT–HP condition. Interestingly, considering the combination of climate and planting years, long-term tobacco planting exerts a significant positive effect only under the HT–HP condition. In general, the above results reveal that long-term tobacco planting could promote soil fertility and structure and alleviate the negative effects of climate on soil fertility and structure. However, fertilization strategies should be modified, for example, by increasing the application of organic fertilizers to improve SOM content, and thus protect aggregates. Evidently, long-term planting caused temporal variability of tobacco soil fertility and structure. Therefore, continuous investigations are necessary in the future.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13122962/s1, Table S1. Standard scoring functions and the corresponding threshold for soil fertility indicators; Table S2. Soil fertility grading criteria based soil fertility index (SFI) values, and total number and proportion of soil samples in each grade.

Author Contributions

Conceptualization, W.-C.Z.; methodology, W.-C.Z. and W.W.; software, W.-C.Z.; validation, W.-C.Z., W.W. and H.-B.L.; writing—original draft, W.-C.Z.; writing—review and editing, W.-C.Z. and W.W.; visualization, W.-C.Z.; supervision, H.-B.L.; funding acquisition, H.-B.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Chongqing Key Laboratory of Digital Agriculture (China) and Chongqing Tobacco Science Research Institute (China).

Data Availability Statement

Data are contained within the article.

Acknowledgments

We thank the local farmers for their help during the sample collection.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Six, J.; Bossuyt, H.; Degryze, S.; Denef, K. A History of Research on the Link between (Micro)Aggregates, Soil Biota, and Soil Organic Matter Dynamics. Soil Tillage Res. 2004, 79, 7–31. [Google Scholar] [CrossRef]
  2. Zhang, W.; Wu, W.; Li, J.; Liu, H. Climate and Topography Controls on Soil Water-Stable Aggregates at Regional Scale: Independent and Interactive Effects. Catena 2023, 228, 107170. [Google Scholar] [CrossRef]
  3. Bronick, C.J.; Lal, R. Soil Structure and Management: A Review. Geoderma 2005, 124, 3–22. [Google Scholar] [CrossRef]
  4. Ye, L.; Tan, W.; Fang, L.; Ji, L. Spatial Analysis of Soil Aggregate Stability in a Small Catchment of the Loess Plateau, China: II. Spatial Prediction. Soil Tillage Res. 2019, 192, 1–11. [Google Scholar] [CrossRef]
  5. Doran, J.W.; Parkin, T.B. Defining and Assessing Soil Quality. Defin. Soil Qual. Sustain. Environ. 1994, 35, 1–21. [Google Scholar]
  6. Qi, Y.; Darilek, J.L.; Huang, B.; Zhao, Y.; Sun, W.; Gu, Z. Evaluating Soil Quality Indices in an Agricultural Region of Jiangsu Province, China. Geoderma 2009, 149, 325–334. [Google Scholar] [CrossRef]
  7. Qu, M.; Li, W.; Zhang, C.; Zhao, Y.; Huang, B.; Sun, W.; Hu, W. Comparison of Three Methods for Soil Fertility Quality Spatial Simulation with Uncertainty Assessment. Soil Sci. Soc. Am. J. 2013, 77, 2182–2191. [Google Scholar] [CrossRef]
  8. Singh, S.; Kasana, S.S. Estimation of Soil Properties from the EU Spectral Library Using Long Short-Term Memory Networks. Geoderma Reg. 2019, 18, e00233. [Google Scholar] [CrossRef]
  9. Singh, S.; Kasana, S.S. Quantitative Estimation of Soil Properties Using Hybrid Features and RNN Variants. Chemosphere 2022, 287, 131889. [Google Scholar] [CrossRef]
  10. Jenny, H. Factors of Soil Formation: A System of Quantitative Pedology; Courier Corporation: North Chelmsford, MA, USA, 1994; ISBN 0486681289. [Google Scholar]
  11. Wang, S.; Zhang, Z.; Ye, S. Response of Soil Fertility Characteristics in Water-Stable Aggregates to Tea Cultivation Age in Hilly Region of Southern Guangxi, China. Catena 2020, 191, 104578. [Google Scholar] [CrossRef]
  12. Ballabio, C.; Lugato, E.; Fernández-Ugalde, O.; Orgiazzi, A.; Jones, A.; Borrelli, P.; Montanarella, L.; Panagos, P. Mapping LUCAS Topsoil Chemical Properties at European Scale Using Gaussian Process Regression. Geoderma 2019, 355, 113912. [Google Scholar] [CrossRef]
  13. Song, X.D.; Wu, H.Y.; Ju, B.; Liu, F.; Yang, F.; Li, D.C.; Zhao, Y.G.; Yang, J.L.; Zhang, G.L. Pedoclimatic Zone-Based Three-Dimensional Soil Organic Carbon Mapping in China. Geoderma 2020, 363, 114145. [Google Scholar] [CrossRef]
  14. Wiesmeier, M.; Urbanski, L.; Hobley, E.; Lang, B.; von Luetzow, M.; Marin-Spiotta, E.; van Wesemael, B.; Rabot, E.; Liess, M.; Garcia-Franco, N.; et al. Soil Organic Carbon Storage as a Key Function of Soils—A Review of Drivers and Indicators at Various Scales. Geoderma 2019, 333, 149–162. [Google Scholar] [CrossRef]
  15. Amézketa, E. Soil Aggregate Stability: A Review. J. Sustain. Agric. 1999, 14, 83–151. [Google Scholar] [CrossRef]
  16. Le Bissonnais, Y. Aggregate Stability and Assessment of Soil Crustability and Erodibility: I. Theory and Methodology. Eur. J. Soil Sci. 2016, 67, 11–21. [Google Scholar] [CrossRef]
  17. Hobley, E.; Wilson, B.; Wilkie, A.; Gray, J.; Koen, T. Drivers of Soil Organic Carbon Storage and Vertical Distribution in Eastern Australia. Plant Soil 2015, 390, 111–127. [Google Scholar] [CrossRef]
  18. Řezáčová, V.; Czakó, A.; Stehlík, M.; Mayerová, M.; Šimon, T.; Smatanová, M.; Madaras, M. Organic Fertilization Improves Soil Aggregation through Increases in Abundance of Eubacteria and Products of Arbuscular Mycorrhizal Fungi. Sci. Rep. 2021, 11, 12548. [Google Scholar] [CrossRef]
  19. Zhang, S.; Meng, L.; Hou, J.; Liu, X.; Ogundeji, A.O.; Cheng, Z.; Yin, T.; Clarke, N.; Hu, B.; Li, S. Maize/Soybean Intercropping Improves Stability of Soil Aggregates Driven by Arbuscular Mycorrhizal Fungi in a Black Soil of Northeast China. Plant Soil 2022, 481, 63–82. [Google Scholar] [CrossRef]
  20. Chen, P.; Wang, L.; Li, J.-Y.; Wan, W.; Zhang, R.-Q.; Liu, Y.; Li, Z.-G. Response of Soil Aggregate-Associated Fertility and Microbial Communities to Afforestation in the Degraded Ecosystem of the Danjiangkou Reservoir, China. Plant Soil 2023. [Google Scholar] [CrossRef]
  21. Thomaz, E.; Antoneli, V. Long-Term Soil Quality Decline Due to the Conventional Tobacco Tillage in Southern Brazil. Arch. Agron. Soil Sci. 2022, 68, 719–731. [Google Scholar] [CrossRef]
  22. Wu, R.; Liu, B.; Xue, B.; Gao, R.; Ndzana, G.M.; Liu, R.; Huang, J.; An, H.; Du, L.; Kamran, M. Changes in Soil Organic Carbon and Nutrient Pools in Aggregate-Sized Fractions along a Chronosequence of Wolfberry (Lycium barbarum L.) Plantations in Arid Areas of Northwest China. Soil Use Manag. 2023, 39, 1109–1124. [Google Scholar] [CrossRef]
  23. Ramos, M.F.; da Silva Almeida, W.R.; de Lima do Amaral, R.; Suzuki, L.E.A.S. Degree of Compactness and Soil Quality of Peach Orchards with Different Production Ages. Soil Tillage Res. 2022, 219, 105324. [Google Scholar] [CrossRef]
  24. de Moraes Sá, J.C.; Cerri, C.C.; Lal, R.; Dick, W.A.; de Cassia Piccolo, M.; Feigl, B.E. Soil Organic Carbon and Fertility Interactions Affected by a Tillage Chronosequence in a Brazilian Oxisol. Soil Tillage Res. 2009, 104, 56–64. [Google Scholar] [CrossRef]
  25. He, C.; Wang, R.; Ding, W.; Li, Y. Effects of Cultivation Soils and Ages on Microbiome in the Rhizosphere Soil of Panax Ginseng. Appl. Soil Ecol. 2022, 174, 104397. [Google Scholar] [CrossRef]
  26. Wang, K.; Zhang, C.; Chen, H.; Yue, Y.; Zhang, W.; Zhang, M.; Qi, X.; Fu, Z. Karst Landscapes of China: Patterns, Ecosystem Processes and Services. Landsc. Ecol. 2019, 34, 2743–2763. [Google Scholar] [CrossRef]
  27. Hu, T.; Mao, Z.; Shi, J.; Chen, W. The Role of Taxation in Tobacco Control and Its Potential Economic Impact in China. Tob. Control 2010, 19, 58–64. [Google Scholar] [CrossRef] [PubMed]
  28. Zhang, Y.; He, X.; Liang, H.; Zhao, J.; Zhang, Y.; Xu, C.; Shi, X. Long-Term Tobacco Plantation Induces Soil Acidification and Soil Base Cation Loss. Environ. Sci. Pollut. Res. 2016, 23, 5442–5450. [Google Scholar] [CrossRef] [PubMed]
  29. Zou, C.; Li, Y.; Huang, W.; Zhao, G.; Pu, G.; Su, J.; Coyne, M.S.; Chen, Y.; Wang, L.; Hu, X.; et al. Rotation and Manure Amendment Increase Soil Macro-Aggregates and Associated Carbon and Nitrogen Stocks in Flue-Cured Tobacco Production. Geoderma 2018, 325, 49–58. [Google Scholar] [CrossRef]
  30. Chen, Y.; Ren, K.; Su, J.; He, X.; Zhao, G.; Hu, B.; Chen, Y.; Xu, Z.; Jin, Y.; Zou, C. Rotation and Organic Fertilizers Stabilize Soil Water-Stable Aggregates and Their Associated Carbon and Nitrogen in Flue-Cured Tobacco Production. J. Soil Sci. Plant Nutr. 2020, 20, 192–205. [Google Scholar] [CrossRef]
  31. Kemper, W.D.; Chepil, W.S. Size Distribution of Aggregates. In Methods of Soil Analysis: Part 1 Physical and Mineralogical Properties, Including Statistics of Measurement and Sampling; American Society of Agronomy, Inc.: Madison, WI, USA, 1965; Volume 9, pp. 499–510. [Google Scholar]
  32. Institute of Soil Sciences, Chinese Academy of Sciences (ISSCAS). Physical and Chemical Analysis Methods of Soils; Shanghai Science Technology Press: Shanghai, China, 1978; pp. 7–59. (In Chinese) [Google Scholar]
  33. Abdi, H.; Williams, L.J. Principal Component Analysis. Wiley Interdiscip. Rev. Comput. Stat. 2010, 2, 433–459. [Google Scholar] [CrossRef]
  34. Jin, H.; Zhong, Y.; Shi, D.; Li, J.; Lou, Y.; Li, Y.; Li, J. Quantifying the Impact of Tillage Measures on the Cultivated-Layer Soil Quality in the Red Soil Hilly Region: Establishing the Thresholds of the Minimum Data Set. Ecol. Indic. 2021, 130, 108013. [Google Scholar] [CrossRef]
  35. Anselin, L. Local Indicators of Spatial Association—LISA. Geogr. Anal. 1995, 27, 93–115. [Google Scholar] [CrossRef]
  36. Ye, L.; Tan, W.; Fang, L.; Ji, L.; Deng, H. Spatial Analysis of Soil Aggregate Stability in a Small Catchment of the Loess Plateau, China: I. Spatial Variability. Soil Tillage Res. 2018, 179, 71–81. [Google Scholar] [CrossRef]
  37. DZ/T 0295-2016; Specification of Land Quality Geochemical Assessment. Geological Publishing House: Beijing, China, 2016; pp. 1–59. (In Chinese)
  38. Chinese National Soil Survey Office (CNSSO). China Soil Data from Census; China Agricultural Press: Beijing, China, 1997. (In Chinese) [Google Scholar]
  39. Wilding, L.P. Spatial Variability: Its Documentation, Accomodation and Implication to Soil Surveys. In Proceedings of the Soil Spatial Variability, Las Vegas, NV, USA, 30 November–1 December 1984; pp. 166–194. [Google Scholar]
  40. Dai, P.; Cong, P.; Wang, P.; Dong, J.; Dong, Z.; Song, W. Alleviating Soil Acidification and Increasing the Organic Carbon Pool by Long-term Organic Fertilizer on Tobacco Planting Soil. Agronomy 2021, 11, 2135. [Google Scholar] [CrossRef]
  41. Chen, J.; Deng, H.; Li, Y.; Yang, L.; Deng, Y.; Deng, X.; Zhou, Z.; Peng, S.; Liu, Y.; Chen, M. Strong Acidic Conditions Impaired Photosynthesis, Root System and Seedling Growth of Flue-Cured Tobacco. Int. J. Agric. Biol. 2020, 24, 645–650. [Google Scholar]
  42. Ma, R.; Zhu, X.; Tian, Z.; Qu, L.; He, Y.; Liang, Y. Spatial Distribution and Scale-Specific Controls of Soil Water-Stable Aggregates in Southeastern China. J. Clean. Prod. 2022, 369, 133305. [Google Scholar] [CrossRef]
  43. Zeraatpisheh, M.; Ayoubi, S.; Mirbagheri, Z.; Mosaddeghi, M.R.; Xu, M. Spatial Prediction of Soil Aggregate Stability and Soil Organic Carbon in Aggregate Fractions Using Machine Learning Algorithms and Environmental Variables. Geoderma Reg. 2021, 27, e00440. [Google Scholar] [CrossRef]
  44. Plaza, C.; Giannetta, B.; Fernández, J.M.; López-de-Sá, E.G.; Polo, A.; Gascó, G.; Méndez, A.; Zaccone, C. Response of Different Soil Organic Matter Pools to Biochar and Organic Fertilizers. Agric. Ecosyst. Environ. 2016, 225, 150–159. [Google Scholar] [CrossRef]
  45. Li, P.; Li, Y.; Xu, L.; Zhang, H.; Shen, X.; Xu, H.; Jiao, J.; Li, H.; Hu, F. Crop Yield-Soil Quality Balance in Double Cropping in China’s Upland by Organic Amendments: A Meta-Analysis. Geoderma 2021, 403, 115197. [Google Scholar] [CrossRef]
  46. Zheng, X.; Song, W.; Guan, E.; Wang, Y.; Hu, X.; Liang, H.; Dong, J. Response in Physicochemical Properties of Tobacco-Growing Soils and N/P/K Accumulation in Tobacco Plant to Tobacco Straw Biochar. J. Soil Sci. Plant Nutr. 2020, 20, 293–305. [Google Scholar] [CrossRef]
  47. Bashir, M.A.; Zhai, L.; Wang, H.; Jian, L.I.U.; Qurat-Ul-Ain, R.; Geng, Y.; Rehim, A.; Liu, H. Apparent Variations in Nitrogen Runoff and Its Uptake in Paddy Rice under Straw Incorporation. J. Integr. Agric. 2022, 21, 3356–3367. [Google Scholar] [CrossRef]
  48. Su, Y.; Zi, H.; Wei, X.; Hu, B.; Deng, X.; Chen, Y.; Jiang, Y. Application of Manure Rather than Plant-Origin Organic Fertilizers Alters the Fungal Community in Continuous Cropping Tobacco Soil. Front. Microbiol. 2022, 13, 818956. [Google Scholar] [CrossRef]
  49. Wu, K.; Yuan, S.; Wang, L.; Shi, J.; Zhao, J.; Shen, B.; Shen, Q. Effects of Bio-Organic Fertilizer plus Soil Amendment on the Control of Tobacco Bacterial Wilt and Composition of Soil Bacterial Communities. Biol. Fertil. Soils 2014, 50, 961–971. [Google Scholar] [CrossRef]
  50. Liu, F.; Wang, X.; Chi, Q.; Tian, M. Spatial Variations in Soil Organic Carbon, Nitrogen, Phosphorus Contents and Controlling Factors across the “Three Rivers” Regions of Southwest China. Sci. Total Environ. 2021, 794, 148795. [Google Scholar] [CrossRef]
  51. Jobbágy, E.G.; Jackson, R.B. The Vertical Distribution of Soil Organic Carbon and Its Relation to Climate and Vegetation. Ecol. Appl. 2000, 10, 423–436. [Google Scholar] [CrossRef]
  52. Wang, T.; Zhou, W.; Xiao, J.; Li, H.; Yao, L.; Xie, L.; Wang, K. Soil Organic Carbon Prediction Using Sentinel-2 Data and Environmental Variables in a Karst Trough Valley Area of Southwest China. Remote Sens. 2023, 15, 2118. [Google Scholar] [CrossRef]
  53. Li, Y.; Ma, J.; Li, Y.; Jia, Q.; Shen, X.; Xia, X. Spatiotemporal Variations in the Soil Quality of Agricultural Land and Its Drivers in China from 1980 to 2018. Sci. Total Environ. 2023, 892, 164649. [Google Scholar] [CrossRef]
Agronomy 13 02962 g001
Figure 1. (a) Soil sample points, digital elevation model, and spatial distribution of tobacco fields on Permian and Triassic limestone; (b) geographic location of the study area.
Figure 1. (a) Soil sample points, digital elevation model, and spatial distribution of tobacco fields on Permian and Triassic limestone; (b) geographic location of the study area.
Agronomy 13 02962 g001
Agronomy 13 02962 g002
Figure 2. Boxplots of WSA, SFI, and key soil fertility-related factors in different planting years. Different letters indicate significant differences at p < 0.05 between different planting years. Planting year grouping: short: <10 years; middle: 10–20 years; long: ≥20 years. SFI: soil fertility index. The abbreviations and descriptions of soil factors are listed in Table 1.
Figure 2. Boxplots of WSA, SFI, and key soil fertility-related factors in different planting years. Different letters indicate significant differences at p < 0.05 between different planting years. Planting year grouping: short: <10 years; middle: 10–20 years; long: ≥20 years. SFI: soil fertility index. The abbreviations and descriptions of soil factors are listed in Table 1.
Agronomy 13 02962 g002
Agronomy 13 02962 g003
Figure 3. Ordinary kriging interpolation maps and LISA cluster maps of WSA (a) and SFI (b).
Figure 3. Ordinary kriging interpolation maps and LISA cluster maps of WSA (a) and SFI (b).
Agronomy 13 02962 g003
Agronomy 13 02962 g004
Figure 4. Variation partitioning analysis for WSA (a) and SFI (b). X1–X3: pure effect of each explanatory variable; X4–X7: joint effects of two or three explanatory variables. Linear and non-linear regressions between MAP and WSA (c), and between MAT and SFI (d). Linear fits are indicated by red solid. Nonlinear relationships are represented by the generalized additive model (GAM), shown by the black dashed line. The shaded area represents 95% confidence interval. **: significant at 0.01 level; r: Spearman correlation coefficient; AIC: Akaike information criterion.
Figure 4. Variation partitioning analysis for WSA (a) and SFI (b). X1–X3: pure effect of each explanatory variable; X4–X7: joint effects of two or three explanatory variables. Linear and non-linear regressions between MAP and WSA (c), and between MAT and SFI (d). Linear fits are indicated by red solid. Nonlinear relationships are represented by the generalized additive model (GAM), shown by the black dashed line. The shaded area represents 95% confidence interval. **: significant at 0.01 level; r: Spearman correlation coefficient; AIC: Akaike information criterion.
Agronomy 13 02962 g004
Agronomy 13 02962 g005
Figure 5. Boxplots of (a) WSA and (b) SFI in different climate groups (different letters indicate significant differences at p < 0.05 in different climate groups. Red bolded numbers indicate the number of soil samples in different climatic groups. WSA is water-stable aggregates, SFI is the soil fertility index, HT–HP is the high temperature and high precipitation group, HT–LP is the high temperature and low precipitation group, LT–HP is the low temperature and high precipitation group, and LT–LP is the low temperature and low precipitation group).
Figure 5. Boxplots of (a) WSA and (b) SFI in different climate groups (different letters indicate significant differences at p < 0.05 in different climate groups. Red bolded numbers indicate the number of soil samples in different climatic groups. WSA is water-stable aggregates, SFI is the soil fertility index, HT–HP is the high temperature and high precipitation group, HT–LP is the high temperature and low precipitation group, LT–HP is the low temperature and high precipitation group, and LT–LP is the low temperature and low precipitation group).
Agronomy 13 02962 g005
Agronomy 13 02962 g006
Figure 6. Boxplots of WSA and SFI in different climate and plant year groups (different uppercase and lowercase letters indicate significant differences at p < 0.05 across planting years and climate groups, respectively. Red bolded numbers indicate the number of soil samples in different groups, WSA is water-stable aggregates, SFI is the soil fertility index, HT–HP is the high temperature and high precipitation group, HT–LP is the high temperature and low precipitation group, LT–HP is the low temperature and high precipitation group, and LT–LP is the low temperature and low precipitation group).
Figure 6. Boxplots of WSA and SFI in different climate and plant year groups (different uppercase and lowercase letters indicate significant differences at p < 0.05 across planting years and climate groups, respectively. Red bolded numbers indicate the number of soil samples in different groups, WSA is water-stable aggregates, SFI is the soil fertility index, HT–HP is the high temperature and high precipitation group, HT–LP is the high temperature and low precipitation group, LT–HP is the low temperature and high precipitation group, and LT–LP is the low temperature and low precipitation group).
Agronomy 13 02962 g006
Table 1. Environmental factors.
Table 1. Environmental factors.
GroupParametersAbbreviationUnit
Soil propertiesSoil pHpHunitless
Soil organic matterSOMg/kg
Cation exchange capacityCECcmol/kg
Available phosphorusAVPmg/kg
Available nitrogenAVNmg/kg
Available potassiumAVKmg/kg
TopographyAspectAsp°
SlopeSlp°
Plan curvaturePLC100× rad/m
Profile curvaturePRC100× rad/m
Topographical wetness indexTWIunitless
ClimateMean annual temperatureMAT°C
Mean annual total precipitationMAPmm
AnthropogenicPlant years (a)PYcategorical
Note: (a) Plant years grouping: short: <10 years; middle: 10–20 years; long: ≥20 years.
Table 2. Descriptive statistics (number of samples = 568).
Table 2. Descriptive statistics (number of samples = 568).
UnitMinMaxMeanSDCV (%)
WSA%40.8464.0551.654.077.87
pHunitless4.088.295.890.8814.96
SOMg/kg11.3043.2025.835.3120.55
CECcmol/kg9.2327.3117.443.1317.95
AVPmg/kg3.40208.8064.6336.5356.51
AVNmg/kg57.90286155.7330.9719.89
AVKmg/kg90.301458441.37195.4444.28
Asp°0.00360173.8795.3375.99
Slp°0.0034.359.206.9954.83
PLC100× rad/m−0.640.63−0.020.13/
PRC100× rad/m−0.820.60−0.080.16/
TWIunitless3.9918.678.222.9335.64
MAT°C11.7117.6513.821.118.03
MAPmm1156.011263.301217.7922.881.88
Note: SD: standard deviation; CV: coefficient of variation; WSA: water-stable aggregates; /: The mean values for PLC and PRC are negative, which typically means that the CVs are misleading. The abbreviations and descriptions of soil factors, topographic factors, and climatic factors are listed in Table 1.
Table 3. The grading criteria for SOM, CEC, AVN, AVP, and AVK, and percentage of different grades.
Table 3. The grading criteria for SOM, CEC, AVN, AVP, and AVK, and percentage of different grades.
ItemGradeGrade IGrade IIGrade IIIGrade IVGrade V
SOM a
(g/kg)		Criteria>40(30–40)(20–30)(10–20)≤10
Percentage (%)0.421.163.714.80
CEC b
(mg/kg)		Criteria>20(15.4–20)(10.5–15.4)(6.2–10.5)≤6.2
Percentage (%)20.25424.80.90
AVN a
(mg/kg)		Criteria>150(120–150)(90–120)(60–90)≤60
Percentage (%)55.832.6101.40.2
AVP a
(mg/kg)		Criteria>40(20–40)(10–20)(5–10)≤5
Percentage (%)69.221.37.71.60.2
AVK a
(mg/kg)		Criteria>200(150–200)(100–150)(50–100)≤50
Percentage (%)90.345.10.50
a: The grading criteria are derived from the Specification of Land quality geochemical assessment. b: The grading criteria are derived from the Second National soil census of China.
Table 4. The grading criteria for pH and percentage of different grades.
Table 4. The grading criteria for pH and percentage of different grades.
ItemGradeUnsuitable
(Very
Alkaline)		Sub-Suitable
(Slightly
Alkaline)		SuitableSub-Suitable
(Slightly Acidic)		Unsuitable
(Very Acidic)
pH a
(unitless)		Criteria≥7.5(6.5–7.5)(5.5–6.5)(5–5.5)<5
Percentage (%)6.3417.9636.823.0615.85
a: The grading criteria are derived from tobacco growing experience [40,41].
Table 5. Principal component analysis for soil fertility-related factors.
Table 5. Principal component analysis for soil fertility-related factors.
UnitPC1PC2PC3CommunalityWeight
Eigenvalue unitless2.451.471.01--
PTV%40.8224.5016.86--
CPTV%40.8265.3282.18--
pHunitless0.04−0.89−0.080.800.20
SOMg/kg0.960.050.200.970.24
AVPcmol/kg0.110.100.840.720.18
AVNmg/kg0.970.060.14- (a)-
AVKmg/kg0.190.060.810.690.17
CECmg/kg0.140.870.080.780.20
Note: PTV: percentage of the total variation; CPTV: cumulative PTV; (a): the Spearman’s correlation coefficient between AVN and SOM was greater than 0.9 (p < 0.01), so AVN was removed. Variables in bold type have a high factor loading within 10% of the highest loading in a principle component. The abbreviations and descriptions of soil factors are listed in Table 1.
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

Zhang, W.-C.; Wu, W.; Liu, H.-B. Planting Year- and Climate-Controlled Soil Aggregate Stability and Soil Fertility in the Karst Region of Southwest China. Agronomy 2023, 13, 2962. https://doi.org/10.3390/agronomy13122962

AMA Style

Zhang W-C, Wu W, Liu H-B. Planting Year- and Climate-Controlled Soil Aggregate Stability and Soil Fertility in the Karst Region of Southwest China. Agronomy. 2023; 13(12):2962. https://doi.org/10.3390/agronomy13122962

Chicago/Turabian Style

Zhang, Wei-Chun, Wei Wu, and Hong-Bin Liu. 2023. "Planting Year- and Climate-Controlled Soil Aggregate Stability and Soil Fertility in the Karst Region of Southwest China" Agronomy 13, no. 12: 2962. https://doi.org/10.3390/agronomy13122962

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