Expanding of karst rocky desertification (RD) area in southwestern China is strangling the sustainable development of local agricultural economy. It is important to evaluate the soil fertility at RD regions for the sustainable management of karst lands. The changes in 19 different soil fertility-related variables along a gradient of karst rocky desertification were investigated in five different counties belonging to the central Hunan province in China. We used principal component analysis method to calculate the soil data matrix and obtained a standardized integrate soil fertility (ISF) indicator to reflect RD grades. The results showed that the succession of RD had different impacts on soil fertility indicators. The changing trend of total organic carbon (TOC), total nitrogen (TN), available phosphorus, microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) was potential RD (PRD) > light RD (LRD) > moderate RD (MRD) > intensive RD (IRD), whereas the changing trend of other indicators was not entirely consistent with the succession of RD. The degradation trend of ISF was basically parallel to the aggravation of RD, and the strength of ISF mean values were in the order of PRD > LRD > MRD > IRD. The TOC, MBC, and MBN could be regarded as the key indicators to evaluate the soil fertility.
Karst rocky desertification (RD) is a process of karst land degradation involving serious soil erosion, extensive exposure of bedrock, and the appearance of a desert-like landscape, leading to drastic decrease in soil productivity (Wang et al., 2004b). Some mountain areas of central Hunan province, China, which are included in the largest karst geomorphologic distributing areas in southwestern China, were covered with evergreen broad-leaved forest historically but now are under deforestation and over-reclamation (Huang and Cai, 2007; Xiong et al., 2009). Climate changes and anthropogenic driving forces (land overuse) are responsible for the development of aeolian/sandy desertification (Wang et al., 2013a; Wang et al., 2013b) which can cause dust storms (Wang and Jia, 2013) and soil and water losses (Cerdà and Lavée, 1999) and also play important roles in the aggravation of karst rocky desertification (Li et al., 2009b; Yan and Cai, 2015). This has gradually attracted nation-wide attention in China, so that the government and researchers are taking active measures to meliorate rocky desertification land through sustainable management (Bai et al., 2013; Huang et al., 2008). For example, during the investigation of stands, we found that some karst regions with higher grades (moderate RD (MRD) or intensive RD (IRD)) had been enclosed for afforestation. This measure is beneficial to rehabilitation and sustainable management of karst lands (Jaiyeoba, 2001).
In the process of sustainable management it is important to determine the status of soil fertility on karst regions (Deng and Jiang, 2011; Li et al., 2013), because the soil fertility is of fundamental importance for agricultural production and soil fertility management (irrigation, fertilization, and cultivation) (Fallahzade and Hajabbasi, 2012), and it is also a central issue in the decisions on food security, poverty reduction, and environment management (Tilman et al., 2002). Soil fertility is a major component of soil quality, so investigation of soil fertility could be regarded as an essential prerequisite for the rational management and utilization of karst lands. However, soil fertility changes associated with the succession of RD in the karst lands have been poorly understood (Wang and Li, 2007) due to the lack of methods for evaluating the soil on areas affected by succession of RD. The changing process of karst land from one grade to another is called “succession of RD” here (Xie and Wang, 2006), which refers to an observable process of changes of karst ecosystem such as vegetation type, vegetation coverage, bedrock exposure, and soil depth from potential RD (PRD) to IRD or vice versa. Moreover, using a minimum data set to reduce the cost for determining a broad range of indicators is vital to assess soil fertility (Yao et al., 2013) of karst lands during succession of RD.
The soil fertility depends on local climate, soil-forming conditions, environment, and anthropogenic influence in different regions (Liu et al., 2006). Choosing appropriate indicators is vital to evaluate soil fertility. Generally, evaluating indicators are chosen empirically based on the researching fruits of predecessors. However, the adaptability of soil fertility indicators to karst area should be paid close attention due to its fragile ecosystem (Fu et al., 2010). Based on the analyses of literatures (Li et al., 2007; Yao et al., 2013) and suggestions from experts on the stands investigation, we evaluated soil fertility of karst lands using 19 selected indicators.
The objectives of this work were (i) to clarify how 19 selected soil fertility indicators are affected by the succession of rocky desertification, (ii) to identify some reasonable and sensitive indicators to evaluate soil fertility of karst lands with different RD grades, and iii) to find an integrate indicator to evaluate fertility in lands with different RD grades.
The sampling sites are in a karst region in five counties, namely
Lianyuan (LY), Longhui (LH), Shaodong (SD), Xinhua (XH), and Xinshao (XS),
approximately ranging from 26
We used a core cutter (5 cm diameter) to take the soil samples before covering the holes carefully in the field. There were no endangered or protected species involved in this study. The permissions for sampling locations were approved by the forestry bureaus of Lianyuan, Longhui, Shaodong, Xinhua, and Xinshao counties.
To enforce the sustainable management of karst lands, in 2011 the report
Classification of rocky desertification and basic information of plots.
PRD, LRD, MRD, and IRD are potential, light, moderate, and intensive rocky desertification, respectively.
Soil pH was determined using a combined glass electrode with 1 : 2.5 (
Total organic carbon (TOC) content was measured by dichromate oxidation method (Yeomans and Bremner, 1988). Total nitrogen (TN) was measured by Kjeldahl determination method after digestion (Brookes et al., 1985a). Total phosphorus (TP) and total potassium (TK) contents were measured after fusion pretreatment with sodium hydroxide (Smith and Bain, 1982). Available phosphorus (AP) and available potassium (AK) were tested using Mehlich 3 extracting method (Sims, 1989).
Measurements of MBC, MBN, and MBP were tested by chloroform-fumigation method (Brookes et al., 1985b; Wu et al., 1990). The density of soil microorganisms including BAC, FUN, and ACT were measured by dilution plating method (Bulluck Iii et al., 2002).
All statistical analyses were performed using SPSS Statistics (v. 20, IBM, USA). Differences in soil fertility indicators among different RD grades were analyzed using one-way analysis of variance (ANOVA). If there was significant difference, post hoc tests (Bonferroni correction) (García, 2004; Rice, 1989) were used. Assumptions of ANOVA were checked. If equal variance could not be assumed between four RD grades, a further Brown–Forsythe test was done instead of ANOVA.
Data should be standardized to avoid unexpected influence appearing
(Liu et al., 2003) because some of the 19 selected indicators were
on very different scales. Data standardization can be done facilely in SPSS using the equation
In order to avoid information overlapping from high-dimensional data sets, dimension reduction is usually performed to get a minimum data set. PCA is regarded as a statistical procedure using dimension reduction to convert a set of observations with possibly correlated variables into a set of linearly uncorrelated variables called principal components (Liu et al., 2003). A principal component is a linear combination of all original indicators; their loading coefficients are named characteristic vectors. Although the number of principal components is equal to that of indicators, which are likely not the original indicators, all principal components are not correlated to each other. Generally, the first several principal components can represent major information of the samples. The selecting rule for principal components is that (a) the eigenvalue of each principal component is bigger than 1 and (b) the cumulative variance proportion of all principal components is more than 85 %.
Principal component scores of all samples were obtained using the equation
Integrate soil fertility (ISF) scores were calculated using the equation
The results indicated that the succession of RD affected 19 selected soil
fertility indicators to a different extent (Table 2). The content of total
organic carbon, TN, MBC, MBN, and AP decreased with the aggravation of RD (
Effects of succession of rocky desertification on soil fertility indicators.
TOC, total organic carbon; TN, total nitrogen; TP, total phosphorus; AP,
available phosphorus; TK, total potassium; AK, available potassium; CEC,
cation exchange capacity; MBC, microbial biomass carbon; MBN, microbial
biomass nitrogen; MBP, microbial mass phosphorus; BAC, bacteria; FUN,
fungi; ACT, actinomycetes; BD, bulk density; CMC, capillary moisture
capacity; FMC, field moisture capacity; CAP, capillary porosity; TOP, total
porosity.
Mean
As shown in Table 3, total organic carbon, TN, and TP showed significant
and positive correlation with each other, and TOC was highly correlated to TN
with
Correlation matrix of soil evaluating indicators for rocky
desertification
PCA was performed using the data matrix of standardized means of 17
indicators, and 17 original indicators (excluding TN and TOP) were grouped
into 17 independent principal components. Each eigenvalue of the first six
principal components (PC
Principal components analysis.
The order in which the principal components were interpreted depended on the
magnitude of their eigenvalues. The PC
The PC
The PC
After computing principal component scores, ISF scores of 20 plots were calculated (Fig. 1). The fertility levels of sampling sites LY1 and SD1 for PRD were higher than those of other sites as expected, but ISF scores of LH1, XH1, and XS1 for PRD were lower than ISF scores of LH2 and LY2 for LRD and ISF score of XH3 for MRD. ISF scores of LY4 and SD4 for IRD were lower than those of other sites. In summary, ISF scores fluctuated with different sampling sites for different RD grades.
Integrate soil fertility scores of 20 studied plots. LH, LY, SD, XH, and XS are the sampling plots standing for Longhui, Lianyuan, Shaodong, Xinhua, and Xinshao counties at central Hunan province, China, respectively. The green, blue, orange, and red bars refer to potential, light, moderate, and intensive rocky desertification, respectively.
To facilitate comparison, the means of ISF scores were calculated (Fig. 2). The sequencing of the mean ISF scores was PRD > LRD
> MRD > IRD. Only the difference between ISF scores of
MRD and those of IRD was significant (
The results in Table 5 demonstrated that the integrate fertility scores were
strongly and significantly correlated to CEC, MBC, MBN, CMC, FMC, and FMC (
Correlation analysis of integrate fertility scores (
Table 2 clearly showed that the average values of TOC, TN, AP, MBC, and MBN
perfectly matched the pre-defined succession of RD grades and those of TP
and MBP showed a similar tendency. Thus, soil fertility decreased along with
the aggravation of RD (Fig. 2). Soil fertility, as the basis of soil
quality, directly affects the productivity of land. In return, land use type
and frequency influence the soil quality (Ozgoz et al.,
2013). The aggravation of RD is not only caused by anthropogenic factor
(land overuse) but also by climate change (Li et al., 2009a).
Degradation of phytocommunity (tree
Although the sequencing of the mean ISF scores was PRD > LRD > MRD > IRD (Fig. 2), soil fertility fluctuated remarkably with different sampling sites and with different RD grades (Fig. 1). Soil fertility levels were not always consistent with RD grades; for instance, the fertilities of MRD in LH3, XH3, and XS3 sites were greater than those of LRD in SD2, XH2, and XS2 sites (Fig. 1). This might be ascribed to (i) the classification method of RD not being so correct. The actual soil fertility could not be only explored from soil depth, vegetation coverage, bedrock exposure, and vegetation type. Maybe the variables used were not enough to explain the level of RD. For some karst areas (MRD or IRD), although their vegetation covers are less than those of LRD, their surface fertile soil might accumulate in a low-lying zone when eroded by rainfall chronically; hence some soil with higher RD grade would have greater fertility. (ii) Maybe climate variables, land use, topography of the studied area and soil type should be used in PCA and should also be considered in a future work to explain RD. The difference of soil fertility is also caused by regional variation. Local climate, soil-forming conditions, and the way and extent of anthropogenic intervention are different from one region to another (Clemens et al., 2010). Thus, soil fertility in one region for MRD might be greater than that in another region for LRD. When we investigated on stands, we found that the majority of PRD regions had better vegetation because they had been enclosed for afforestation to avoid anthropogenic interference. Most of the IRD regions became abandoned land without any agricultural production due to seriously degrading soil fertility. In contrast, both LRD and MRD regions with moderate fertility were not strictly protected. Perhaps residue burning had caused degradation of tree/shrub to shrub/grass or animal grazing had led to residue mineralization, recycling of faeces, and incrementing soil nutrients (McCarty et al., 2009; Shariff, 1994). They were usually utilized to cultivate timber forests or non-wood forests. Thus, this variable should be used in the model. As a result, the anthropogenic interference to LRD or MRD certainly reached the highest level. Human activity is one of key driving factors of RD (Li et al., 2009b; Xiong et al., 2009), and RD grade varies among land use types (Li et al., 2006). Therefore, reducing human activities and taking measurements such as mountain closure, forest reservation, and plantation might be some important measures to control expanding of RD area, which could be learned from natural vegetation rehabilitation to control soil erosion on the Loess Plateau (Zhao et al., 2013, 2015). (iii) Self-organization of soil environment improves soil fertility. With gradual deterioration of soil fertility, soil animals and microorganism at some stage (MRD) increase the speed of litter fall breakdown by disintegrating tissue and fixing the nutrients to acclimate the degrading environment (Barot et al., 2007). Thus, the fertility of MRD soil is likely greater than that of LRD soil.
Selecting appropriate indicators will guarantee the accuracy of evaluating results. Generally, evaluating indicators are chosen empirically based on the researching fruits of predecessors. Some physicochemical (Ozgoz et al., 2013), microbial biomass (Paz-Ferreiro and Fu, 2013), and enzymatic activity properties (Pajares et al., 2011) had been chosen to assess the soil fertility. On the basis of scientifically reliability, defining a minimum data set for evaluating soil fertility can cut down the number of indicators and reduce evaluating cost.
Average scores of integrate soil fertility of 20 studied
plots. PRD, LRD, MRD, and IRD refer to potential, light, moderate, and
intensive rocky desertification, respectively. Paired difference were
analyzed as
Soil organic matter (used interchangeably with TOC), as the major source of several nutrients, exerts numerous positive effects on soil physicochemical properties as well as soil's capacity to provide regulatory ecosystem services. N, P, and K are often referred to the primary macronutrients in soil for plants' growth. CEC is used as a measure of fertility and nutrient retention capacity. BD, as an indicator of soil compaction, reflects the extent of loosening and permeability of soil. MBC, MBN, and MBP reflect the number and activity of soil microorganism and the status of soil environment, although they only have a little content in soil with the mean ratios of MBC to TOC (0.61 %), MBN to TN (2.16 %), and MBP to TP (0.95 %) in this study (extracting from Table 2). It was reported that the microbial activity directly influences soil ecosystem stability and fertility (Pascual et al., 1997). Soil biochemical, microbiological, and biological properties are more suitable than physical and/or chemical properties to estimate soil quality and soil degradation (Paz-Ferreiro and Fu, 2013). And it is widely recognized that a good level of microbiological activity is crucial for maintaining soil quality (de la Paz Jimenez et al., 2002; Pascual et al., 2000; Visser and Parkinson, 1992), because microbial turnover is a driving force for transformation and cycling of organic matter to plant nutrients in soils (Chen and He, 2002; Fontaine et al., 2003). For instance, the change in MBC is a sensitive index of changes in the content of soil organic matter (García-Orenes et al., 2010; Powlson et al., 1987), and it is useful for determining microbial population size to evaluate natural and degraded systems (Soulas et al., 1984). The strong and positive correlation between MBC and TOC (Table 3) indicated that MBC was a sensitive index to indicate the dynamics of soil organic carbon (Liu et al., 2012). Inorganic N and P needed by vegetation are mainly obtained from mineralization of organic matter in soil microbial degradation system (Hopkins et al., 2011; Ros et al., 2011). The changes in MBN and MBP can also indicate the fluctuation of soil fertility (Powlson et al., 1987). Thus, these indicators deserve prior researching before getting a minimum data set.
Furthermore, TOC, CEC, MBC, MBN, BD, CMC, and FMC were significantly
correlated to the ISF (
The succession of RD affected evaluating indicators of soil fertility to different extent, but the degradation trend of soil fertility was almost parallel to the aggravation of RD. Soil chemical indicators TOC and microbial indicators MBC and MBN might be the key indicators to evaluate soil fertility in RD regions according to their paired correlations and significant correlation to the ISF and changing tendency across the RD grades. Perhaps the method of classifying RD only according to soil depth and the landscape indicators (vegetation coverage, bedrock exposure, and vegetation type) could be improved after taking the regional difference of soil fertility into account in the future research.
This research was supported by National Department Public Benefit Research Foundation of State Forestry Administration of China (201104016) and the Planned Science and Technology Project of Hunan Province, China (2013RS4035), and was partially funded by the China Postdoctoral Science Foundation (2013M531787). We thank Dr. Veronika for her critical revision of the manuscript. We are grateful to the Forestry Bureau of Lianyuan, Longhui, Shaodong, Xinhua, and Xinshao counties of Hunan for providing the sampling sites. We also acknowledge the anonymous reviewers for the valuable comments.Edited by: A. Jordán