REDUCED SOIL QUALITY WITH CULTIVATION OF CALCAREOUS SOILS IN SUBTROPICAL CHINA KARST REGION ZMANJŠANA KAKOVOST TAL ZARADI OBDELAVE APNENČASTIH TAL NA SUBTROPSKEM KITAJSKEM KRAŠKEM OBMOČJU Mengxia ZHOU 1 , Hui YANG 2* , Cheng ZHANG 1,3,4 , Jianhua CAO 1,3,4 & Degen ZHU 1, 3, 4 Abstract UDC 504.12:631.61:551.435.8(510) Mengxia Zhou, Hui Yang, Cheng Zhang, Jianhua Cao & De- gen Zhu: Reduced soil quality with cultivation of calcareous soils in subtropical China karst region Cultivation practices significantly impact soil functionality and quality, however, the effects of different cultivation durations remain inadequately quantified in the fragile karst ecosystems of southwestern China. This study employed a chronosequence approach to assess agricultural soil quality changes and iden- tify their key drivers. Five land-use stages were selected and sampled: a natural reserve forest (NR) as a reference, recently burned land (0 a), and cultivated lands with durations of 1, 5, 15, and 30 years (1 a, 5 a, 15 a, 30 a, respectively) in the sub- tropical karst region of southwestern China. The soil quality in- dex (SQI) was constructed using both the Total Data Set (TDS) and Minimum Data Set (MDS) methods. Principal component analysis (PCA) identified calcium (Ca), silt content and silicon (Si) as the key indicators within the MDS. Both TDS and MDS assessments revealed a progressive decline in SQI with increas- ing cultivation duration following slash-and-burn practices (R²=0.67 between TDS- and MDS-derived SQIs), indicating that agricultural activities significantly alter soil physicochemi- cal properties and lead to soil degradation. The observed de- crease in soil quality may be explained by two main mecha- nisms: (1) depletion of the soil-forming matrix in shallow karst environments; and (2) accelerated loss of acid-insoluble residues due to enhanced weathering under cultivation. These findings highlight the high vulnerability of karst soils to long- term farming, where inherently limited pedogenic materials Izvleček UDK 504.12:631.61:551.435.8(510) Mengxia Zhou, Hui Y ang, Cheng Zhang, Jianhua Cao & Degen Zhu: Zmanjšana kakovost tal zaradi obdelave apnenčastih tal na subtropskem kitajskem kraškem območju Obdelovalne prakse pomembno vplivajo na funkcionalnost in kakovost tal, ob tem pa vplivi različno dolgega trajanja ob- delovanja v občutljivih kraških ekosistemih na jugozahodu Kitajske še vedno niso ustrezno opredeljeni. Avtorji so v tej ra- ziskavi uporabili pristop kronološkega zaporedja ter na njegovi podlagi proučili spremembe v kakovosti kmetijskih tal in opre- delili njihove ključne dejavnike. Izbrali in vzorčili so pet stopenj rabe zemljišč: naravni gozdni rezervat kot referenco, nedavno požgano zemljišče (0 a) in obdelovalna zemljišča, ki so se ob- delovala 1, 5, 15 in 30 let (1 a, 5 a, 15 a, 30 a) na subtropskem kraškem območju na jugozahodu Kitajske. Z uporabo metod ce- lotnega podatkovnega niza in minimalnega podatkovnega niza so oblikovali indeks kakovosti tal. Iz analize glavnih komponent je razvidno, da so kalcij, vsebnost mulja in silicij ključni kazalniki v minimalnem podatkovnem nizu. Na podlagi ocen celotnega podatkovnega niza in minimalnega podatkovnega niza je opazno postopno upadanje indeksa kakovosti tal, ob tem pa se podaljšuje trajanje obdelovanja po praksah sekanja in požiganja (R² = 0,67 – indeks kakovosti tal, izpeljan iz celotnega in minimalnega po- datkovnega niza), kar kaže, da kmetijske dejavnosti pomembno spreminjajo fizikalno-kemijske lastnosti tal in povzročajo njiho- vo degradacijo. Ugotovljeno zmanjšanje kakovosti tal je mogoče razložiti z dvema glavnima razlogoma: (1) izčrpavanje matrice, ki tvori tla, na plitvih kraških območjih in (2) pospešena izguba v kislini netopnih ostankov zaradi okrepljenega vpliva vremenskih ACTA CARSOLOGICA 54/2-3, 189-204, POSTOJNA 2025 1 Key Laboratory of Karst Dynamics, MNR and GZAR, Institute of Karst Geology, CAGS, Guilin 541004, China 2 School of Earth Sciences, Yunnan University, Kunming, 650500, China 3 International Research Center on Karst under the Auspices of UNESCO, National Center for International Research on Karst Dynamic System and Global Change, Guilin 541004, China 4 Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo 531406, China * Corresponding author, address: School of Earth Sciences, Yunnan University, Kunming, 650500, China; e-mail: karstyanghui@ ynu.edu.cn Received/Prejeto: 22. 5. 2025 DOI: https://doi.org/10.3986/ac.v54i2.14469 1. INTRODUCTION Karst landscapes cover 12% of the global terrestrial surface, with China containing approximately 3.44 mil- lion km 2 of karst formations (Wei et al., 2018). Notably, 83% of China’s karst is located in the southwest, where ecosystems face severe challenges including intense human-land conflicts, declining land productivity, and significant soil degradation (Jiang et al., 2014; Zhang et al., 2019; Yuan, 1991). Traditional practices such as de- forestation and hillslope cultivation of cash crops have exacerbated ecological degradation under sustained hu- man pressure. This unsustainable land use pattern has fundamentally hindered rational resource utilization and agricultural sustainability (Hu et al., 2019; Lei et al., 2019; Wu et al., 2003; Wiesmeier et al., 2015; Xiao et al., 2019). Agricultural intensification is a major driver of soil nutri- ent depletion, with a study showing that conversion of natural ecosystems to cropland can result in up to 75% soil organic carbon (SOC) loss (Lal, 2004; Lal, 2010; Liao et al., 2018). The inherent fragility of karst ecosystems, due to low acid-insoluble residue content in carbonate bedrock, severely limits soil formation and recovery ca- pacity. Cultivation further accelerates erosion, leaching, and organic matter loss, leading to negative soil mass balance, progressive thinning, and rocky desertifica- tion. The interplay between geological constraints and human disturbance heightens ecosystem vulnerability, underscoring the need for land management strategies that address both degradation processes and limited soil- forming potential (Li et al., 2019; Foley et al., 2005). Soil is a vital natural resource that integrates physi- cochemical and biological properties, underpinning biological productivity, environmental quality, and eco- system stability (Shukla et al., 2006). Soil quality reflects the inherent capacity of soil to function within ecosystem boundaries and serves as a sensitive indicator for moni- toring functional changes, predicting ecological recovery, succession dynamics, and system resilience (Doran and Parkin, 1994). SQI has become a widely used integrative tool for assessing ecosystem health, especially in fragile karst environments (Li et al., 2012). Effective ecological restoration in this region depends on indicators that are both sensitive to change and scalable across landscapes. However, standardized SQI development remains chal- lenging due to soil heterogeneity and the need for con- text-specific assessment protocols aligned with land-use goals (Zhang et al., 2011; Karlen and Stott, 1994; Mukher- jee and Lal, 2014; Qi et al., 2009). For instance, indicator selection for agricultural systems focused on productivity differs from that for conservation-oriented systems em- phasizing erosion control (Karaca et al., 2021; Griffiths et al., 2010). Current frameworks classify soil quality indicators into two types: (1) inherent properties (e.g., texture, mineral composition), which define baseline soil potential, and (2) dynamic properties (e.g., organic matter content, microbial biomass), which reflect short- term management effects (Qi et al., 2009; Andrews et al., 2002). Effective evaluation requires synergistic consider- ation of both categories, yet persistent challenges remain in establishing universally applicable indicators across diverse ecological and anthropogenic gradients (Doran and Parkin, 1994; Rahmanipour et al., 2014; Schnitzer et al., 2006). The MDS approach addresses this need by identifying a concise set of key parameters, enabling effi- cient monitoring without redundancy. This is particularly relevant in shallow karst soils, where limited depth and low formation potential constrain recovery and require precise assessment. By incorporating key geochemical factors (e.g., Ca, Si) and indicators of human impact (e.g., soil organic carbon, silt loss), a karst-specific MDS can capture major drivers of soil degradation (W ei et al., 2018; MENGXIA ZHOU, HUI YANG, CHENG ZHANG, JIANHUA CAO & DEGEN ZHU and intense chemical weathering collectively exacerbate quality decline. This study provides critical insights for developing sus- tainable land management strategies in karst regions, empha- sizing the importance of practices that conserve soil fertility, reduce residue loss, and maintain essential ecosystem services. With the MDS model explaining 67% of the variance in the TDS-based SQI, it represents an efficient and practical tool for monitoring soil quality in fragile karst environments, thereby supporting evidence-based sustainable land use planning. Keywords: soil quality index (SQI), minimum data set (MDS), total data set (TDS), cultivation, karst region. razmer ob obdelavi tal. Te ugotovitve kažejo na veliko ranljivost kraških tal ob dolgotrajnem kmetovanju, pri čemer omejena količina pedogenih materialov in intenzivno kemično prepere- vanje skupaj kakovost še poslabšujeta. Ta raziskava zagotavlja ključna spoznanja za pripravo trajnostnih strategij upravljanja zemljišč na kraških območjih ter poudarja pomen praks, s kat- erimi se ohranja rodovitnost tal, zmanjšuje izguba ostankov in se ohranjajo bistvene ekosistemske storitve. Glede na to, da model minimalnega podatkovnega niza pojasnjuje 67 % variance indeksa kakovosti tal, izpeljanega iz celotnega podatkovnega niza, je to učinkovito in praktično orodje za spremljanje kako- vosti tal na občutljivih kraških območjih ter podpira trajnostno načrtovanje rabe zemljišč, ki temelji na dokazih. Ključne besede: indeks kakovosti tal, minimalni podatkovni niz, celotni podatkovni niz, obdelava tal, kraško območje. 190 ACTA CARSOLOGICA 54/2-3 – 2025 Zhang et al., 2021). When used to compute an SQI, this framework facilitates targeted evaluation and early detec- tion of soil decline, supporting science-based land resto- ration and sustainable management in these vulnerable ecosystems (Bünemann et al., 2018). Site-specific soil quality assessment provides a criti- cal scientific basis for understanding pedogenic processes and designing sustainable land management strategies in ecologically sensitive zones, especially karst regions with vegetation vulnerability (Zhang et al., 2021). In this study, conducted under the distinctive geological and agricul- tural conditions of the karst areas, soil indicators from citrus orchards of different cultivation ages are examined. The aims are: (1) to establish a standardized soil quality evaluation framework identifying diagnostic indicators that reflect systemic soil quality patterns in karst planta- tion ecosystems; (2) to assess the influence of cultivation duration on soil quality through comprehensive scoring; and (3) to compare soil indicators across different culti- vation years to identify key factors constraining soil sus- tainability in karst ecosystem. The results will provide a valuable reference for sustainable land management and the enhancement of ecosystem service functions in karst regions. 2. MATERIALS AND METHODS 2.1 STUDY SITES This study was conducted at the Field Scientific Obser - vation and Research Station of Southwest Karst Rocky Desertification, under the Ministry of Science and Tech- nology (110°32´27" E, 25°12´33" N), located in Deng- ming village, approximately 30 km southeast of Guilin city, Guangxi Zhuang Autonomous Region (Figure 1a). The site features a typical peak-cluster depression land- scape, and a subtropical monsoon climate, with a mean annual temperature of 18.6 °C and mean annual precipi- tation ranging from 1800 to 2000 mm (Xie et al., 2018). Between 60% and 70% of the precipitation occurs dur- ing the monsoon season from April to September. The dominant soil type is classified as Leptosols (Rendzic Leptosols according to WRB 2014 classification), which is a typical calcareous soil formation of karst bedrock en- vironments (IUSS Working Group, 2014). The enclosed karst depression was originally covered by a nature re- serve forest consisting of shrubs and trees in the absence of anthropogenic disturbance. The area was cleared using fire, starting from the base of the depression and moving upslope, to establish citrus orchards for economic pur- poses. 2.2 SOIL SAMPLING A space-for-time substitution approach was employed to select sampling sites (Yang et al., 2019a). Soil sam- pling was conducted in July 2018. The selected plots represented different durations of citrus trees (0 years, 0 a; 1 year, 1 a; 5 years, 5 a; 15 years, 15 a; 30 years, 30 a) (Figure 1b), along with a nature reserve forest plot (NR) serving as a reference control. To minimize con- founding factors, all plots were chosen based on simi- lar environmental conditions, lithology, soil type, and historical land use prior to cultivation. Three replicate plots were established for each of the 0 a, 1 a, 5 a, 30 REDUCED SOIL QUALITY WITH CULTIV ATION OF CALCAREOUS SOILS IN SUBTROPICAL CHINA KARST REGION ^ ± (a) (b) Legend Rivers ^ Study area Guangxi Province Karst area Figure 1: Location of sampling sites in Guilin County, Guangxi Zhuang Autonomous Region. ACTA CARSOLOGICA 54/2-3 – 2025 191 a, and NR sites. Given that the 15 a represents a criti- cal transitional period within the 5 a to 30 a timeline, five sampling points were selected for this age group. At each sampling point, the 1 m × 1 m quadrats were randomly placed, maintaining a minimum distance of 5 m between quadrats. Soil depth across the study area ranged from 20 to 50 cm, with an organic layer thick- ness of 1 - 3 cm (Wang et al., 2022). Soil samples were collected from the 0-10 cm layer within each quadrat. After removing surface litter, visible gravel, roots, and organic debris, three subsamples were combined to form one composite sample per quadrat. A total of 20 soil samples were collected across six land-use types (5 citrus orchard ages and one natural forest), with 3 - 5 replicate plots per type. All samples were transported to the Key Laboratory of Karst Dynamics, Ministry of Natural Resources. The soils were air-dried and sieved for subsequent physical and chemical analyses (Yang et al., 2019a). 2.3 ANALYSIS OF SOIL PROPERTIES Soil pH was determined in a 1:2.5 (w/v) soil-water sus- pension using a Digital Millivolt pH Meter-2 detector (DMP-2 mV/pH, Quark Ltd., Nanjing, China). SOC and total nitrogen (TN) were analyzed using a Sercon SL C/N elemental analyzer after pretreatment with 1 mol/L HCl to remove inorganic carbon. The contents of recalcitrant organic carbon (ROC) and recalcitrant organic nitrogen (RON) were determined based on the residual C and N after NaClO oxidation. Dissolved organic carbon (DOC) was determined by shaking soil with deionized water at a 1:5 (w/v) ratio and analyzed with a Total Organic Carbon analyzer (Shimadzu Corporation) (Yang et al., 2019b). The contents of Ca, magnesium (Mg), phospho- rus (P), iron (Fe), aluminum (Al) and Si were quantified using total X-ray fluorescence spectroscopy (SPECTRO Analytical Instruments GmbH) (Yang et al., 2021). Soil texture was analyzed with a laser diffraction particle size analyzer (Beckman Coulter LS-230, Brea, CA, USA) after removing organic matter with H 2 O 2 and carbonates with dilute HCl (Yang et al., 2021). Soil bulk density (SBD), soil water content (SWC), and field water holding capac- ity (FWHC) were determined using the core method (Bao et al., 2000). 2.4 MDS ESTABLISHMENT AND SOIL QUALITY EV ALUATION The MDS was constructed to evaluate soil quality by sys- tematically selecting and integrating key soil indicators from a full set of physical and chemical properties. The procedure consisted of four steps: (1) identifying the rep- resentative indicators of soil quality; (2) scoring these in- dicators using a standard scoring function (SSF); (3) as- signing weights to each indicator through PCA method; (4) integrating the weighted scores to derive a compara- tive SQI value for comparison. 2.4.1 Indicator selection Representative indicators were selected based on their direct relevance to soil quality (Qi et al., 2009). These included soil fertility parameters (C, N, P), trace el- ements (Fe, Al, Si),base cations (Ca, Mg) which are particularly relevant in calcareous soils (Muneer et al., 1989; Rowley et al., 2018). Additionally, physical prop- erties such as SBD and FWHC, which influence root development and plant growth (Plante et al., 2018). Soil texture was also included due to its role in organic matter stabilization. The TDS initially contained 19 indicators reflecting soil properties and management effects. The coefficient of variation (C.V) and value ranges of these properties were calculated (Table S1) to assess spatial variability and sensitivity. PCA was applied to identify key indicators, retaining only principal com- ponents (PCs) with eigenvalues greater than 1 and variables with factor loadings more than 0.65. Where multiple indicators loaded highly on the same PC, cor- relation analysis was used to eliminate redundancy (P < 0.05), retaining only those that were either strongly correlated or represented unique soil functions. This process ensured the MDS was both representative and non-redundant. 2.4.2 Indicator scoring A standard scoring function (SSF) was used to normalize all indicators to a uniform scale (0-1) to mitigate differ- ences in units and magnitudes (Karlen and Stott, 1994; Li et al., 2013). Indicators were classified into three cat- egories based on their functional relationship with soil quality, and scored using the following equation: MENGXIA ZHOU, HUI YANG, CHENG ZHANG, JIANHUA CAO & DEGEN ZHU 192 ACTA CARSOLOGICA 54/2-3 – 2025 Here, f(x) is the normalized score (0.1 to 1); x is the mon- itored value; and L and U represent the lower and upper threshold values, respectively. 2.4.3 Weight assignment Weights for each indicator in both TDS and MDS were determined based on communality derived from factor analysis (FA) (Shukla et al., 2006). The weight of an indi- cator was calculated as the ratio of its communality to the sum of the communalities of all indicators. 2.4.4 Integration of soil quality index The SOI was computed for both TDS and MDS using the following formula: Where SQI is the soil quality index ranges from 0 to 1, W i is the weight of each indicator, S i is the score, and n is the number of indicators. 2.5 STATISTICAL ANALYSES One-way analysis of variance (ANOV A) was used to test all the parameters and soil quality indexes. When sig- nificant differences were detected, the Duncan’s multiple range test (P ＜ 0.05) was applied for mean separation. Two-tailed Pearson product-moment correlation analy- sis was conducted to explore relationships between soil indicators. All statistical analyses were performed using SPSS 19.0 software (SPSS Inc., Chicago, USA), while data visualization was carried out with Origin 2021 software (OriginLab Corporation, Northampton, MA, USA). Three replicates were prepared for independent labora- tory testing and statistical analysis. 3. RESULTS 3.1 SOIL PHYSICAL AND CHEMICAL PROPERTIES Soil texture analysis (Table 1) revealed no significant differences (P > 0.05) in clay and silt contents across the various cultivation durations. In contrast, sand content at 5 a, 15 a and 30 a of cultivation differed significantly from other stages (P < 0.05). Overall, pro- longed cultivation was associated with a decrease in sand content and a concurrent increase in clay and silt fractions. This trend suggests the progressive accumu- lation of fine particles, likely attributable to processes such as erosion, deposition, or selective loss of coarser materials. Table 1: Response of variation of the soil texture to cultivation. Clay (%) Silt (%) Sand (%) NR 25.53±2.93a 47±1.99a 27.40±4.68a 0 a 30.13±2.01a 46.3±2.40a 23.54±4.18ab 1 a 33.87±2.53a 50.67±2.47a 15.48±4.05bc 5 a 27.97±9.04a 64.6±8.94a 7.45±0.63c 15 a 39.08±7.54a 54.24±5.28a 6.66±2.56c 30 a 31.4±9.35a 63.1±7.79a 5.50±2.16c Note: Different lowercase letters indicate significant differences at P < 0.05. The physicochemical properties of soil indicators across different cultivation durations were shown in Figure 2. The pH values ranged from 5.34 to 7.40, with a mean value of 6.46, showing a gradual shift from neu- tral-alkaline to acidic conditions over time. Through- out the 30 years cultivation period, SOC varied from 16.74 to 84.73 g·kg -1 (the mean value is: 49.85 g·kg -1 ) , while TN ranged from 2.04 to 8.04 g·kg -1 (mean value: 4.81 g·kg -1 ). Both SOC and TN exhibited significant reductions, declining by 80.24% and 74.67%, respec- tively. Similarly, DOC decreased by 75% over the same period. Despite the sharp declines in soil carbon and nitrogen with increasing cultivation time, the C/N ra- tio showed no significant difference (P > 0.05) during the early stage of cultivation. However, after 15 years of cultivation, the C/N ratio differed significantly (P < 0.05) from the initial values. Calcium content was the highest in NR, followed by the initial stage (0 a), and gradually decreased with longer cultivation duration, reaching its lowest level at 30 a. Significant differences were observed across culti- vation stages. Soil Mg content followed a trend similar to that of Ca, with peak values observed at 0 a. Phos- phorus content peaked at 15 a and 30 a, differing sig- nificantly from other stages (P < 0.05). The Si content (derived from SiO 2 ) exhibited a fluctuating trend, de- creasing initially and then increasing, with the lowest value recorded at 5 a. In contrast, the concentrations of Fe and Al, representing soil Fe and Al oxides, accu- mulated over time, reaching a maximum at 15 a, before slightly decreasing at 30 a. REDUCED SOIL QUALITY WITH CULTIV ATION OF CALCAREOUS SOILS IN SUBTROPICAL CHINA KARST REGION ACTA CARSOLOGICA 54/2-3 – 2025 193 3.2 ESTABLISHMENT OF MDS UNDER PCA The PCA results (Table S2) showed that the first four PCs with eigenvalues >1 collectively explained 81.593% of the total variance in soil indicators. Based on the MDS selection criteria, Ca exhibited high fac- tor loadings in PC1 and was strongly correlated with SOC, TN, C/N, DOC, RON, P, FWHC, Mg, P, Al, Fe, and SBD (Figure S1). Consequently, Ca was selected as a representative indicator for MDS. Similarly, Silt was chosen from PC2, and Si from PC3. The pH value in PC4 was excluded because it had a low factor load- ing (< 0.6). Ultimately, Ca, Silt and Si were selected to constitute the MDS. MENGXIA ZHOU, HUI YANG, CHENG ZHANG, JIANHUA CAO & DEGEN ZHU Note: Error bars corresponded to the standard deviation (SD). different letters indicate signifi- cant differences among different stages of cultivation (one-way ANOVA followed by Duncan's test, P ＜0.05). Figure 2: Soil physical and chemical indicators under different stages of cultivation. 194 ACTA CARSOLOGICA 54/2-3 – 2025 3.3 EV ALUATION OF SQI WITH THE TDS AND MDS METHODS The SQI values calculated using the TDS method (Figure 3) ranged from 0.26 to 0.67 and showed highly significant variation (P < 0.05) across cultivation durations. The no- tillage site exhibited the highest SQI, followed by 0 a. The SQI decreased with increasing cultivation time, with the lowest value observed at 30 a. Using the MDS method, SQI values ranged from 0.27 to 0.70 and showed no sig- nificant differences among NR, 0 a and 1 a (P > 0.05), but these indicators were statistically distinct from those at 5 a, 15 a and 30 a (P < 0.05). The highest SQI value un- der the MDS method was also observed in NR, followed by 1 a, differing slightly from the TDS method. The SQI value for 0 a fell between NR and 1 a. In summary, soil quality in the study area declined progressively from NR to 30 a of cultivation. The SQI values derived from both the TDS and MDS methods generally followed the same ranking from the highest to lowest across different culti- vation stages. Linear regression analysis was employed to evalu- ate the relationship between SQI values derived from the MDS and TDS methods. The results (Figure S2) indicat- ed a significant positive correlation between the two sets of SQI values, with a coefficient of determination (R 2 ) of 0.67 (P < 0.01). 3.4 LIMITING SOIL INDICATORS FOR SOIL QUALITY A radar diagram was used to visualize the score of all soil parameters, helping to identify key indicators that limit soil quality (Figure 4). The plotted lines corresponding to REDUCED SOIL QUALITY WITH CULTIV ATION OF CALCAREOUS SOILS IN SUBTROPICAL CHINA KARST REGION Figure 3: Mean values of SQI based on TDS and MDS methods. Figure 4: The limiting factors of soil quality index for SQI. different cultivation durations intersect the axes, project- ing the scores of each indicator onto the diagram. Inter- sections near the outer edges of the radar indicate higher soil quality, while those closer to the center reflect poorer soil quality. The results revealed that NR had the high- est soil quality under the TDS method. As cultivation duration increased, the radar profile converged toward the center, indicating a progressive decline in soil qual- ity. The lowest soil quality was observed after 30 years of cultivation, with major limiting factors including Ca, Mg, C and N contents, FWHC, and SBD. At 15 a, soil quality was primarily constrained by Fe and Al oxides. For 5 a, soil texture, particularly clay and silt content, was ACTA CARSOLOGICA 54/2-3 – 2025 195 the main limiting factor. After 1 year of cultivation, pH was the dominant constraint, while SWC and P were the significant limiting factors at the initial stage (0a). With increasing cultivation duration, parameters such as SOC, TN, C/N, DOC, RON, Ca, and Mg showed consistent re- duction toward the center of the diagram. The greatest declines were observed in Ca and Mg, followed by soil carbon and nitrogen components (including SOC, DOC, TN and RON), indicating that long-term cultivation in karst areas accelerates the depletion of organic matter and base cations, leading to progressive degradation of soil quality. 4. DISCUSSION 4.1 SOIL QUALITY INDEX METHODS AND SELECTED SOIL INDICATORS Cultivation significantly alters soil physicochemical properties, highlighting the importance of a compre- hensive understanding of soil quality for sustainable land management. The selection of appropriate soil pa- rameters is critical for accurately assessing soil quality. Soil quality indices have gained widespread recognition due to their reliability and precision (Doran and Parkin, 1994; Zhang et al., 2011; Griffiths et al., 2010; Andrews et al., 2002). However, identifying the most representa- tive soil indicators remains a challenge. While the TDS method provides more comprehensive evaluation, the Minimum Data Set (MDS) method offers practical ad- vantages by minimizing data redundancy, reducing time, and lowering costs (Zhang et al., 2019; Raiesi, 2017). In this study, 17 soil variables were analyzed using TDS, while three key parameters, Ca, Silt and Si were se- lected for MDS through PCA to minimize redundancy. The MDS served as a robust composite index, effectively reflecting soil nutrients, soil structure, and pedogenic processes. The strong positive correlation (R 2 =0.67) be- tween MDS and TDS methods highlights their compa- rable accuracy in assessing soil quality. Notably, the MDS method, despite relying on fewer indicators, achieved performance similar to the more exhaustive TDS ap- proach. This suggests that Ca, Silt, and Si in the MDS are robust indicators for quantifying the impacts of cultiva- tion on soil quality in karst areas, offering an efficient al- ternative to the TDS method. 4.2 EFFECT OF GEOLOGICAL BACKGROUND ON SOIL QUALITY The geochemical characteristics of calcareous soils in karst regions are fundamentally influenced by their ori- gin in carbonate bedrock, which leads to naturally high levels of Ca and Mg. Elevated CO 2 levels in water, atmo- sphere and soil promote the dissolution of carbonate minerals, releasing Ca 2+ and enhancing its bioavailability in these soils (Y ang et al., 2021). Over time, however, pro- longed rainfall leads to intense leaching and decalcifica- tion, gradually depleting base cations. Soil organic carbon is a well-established indicator of soil quality (Schnitzer et al., 2006; Griffiths et al., 2010; Davari et al., 2020; Huang et al., 2021), and it was highly weighted in the PCA results of our findings (Table S2). A significant correlation between Ca and SOC was ob- served, likely due to their symbiotic relationship (Yang et al., 2018; Rowley et al., 2018). Calcium forms organo- mineral complexes such as calcium humate with soil organic matter, improving soil structural stability and promoting organic matter accumulation (Muneer and Oades, 1989a, b, c). Additionally, Ca 2+ form multivalent cationic bridges between organic ligands and soil min- eral surfaces, effectively protecting SOC from microbial decomposition (Rowley et al., 2018). Beyond its struc- tural role, Ca is also an essential plant nutrient, positively impacting on net primary productivity (NPP) and thus increasing above- and belowground biomass inputs to soil organic matter (Carmeis Filho et al., 2017; Haynes and Naidu, 1998). Moreover, changes in Ca 2+ can modu- late bacterial community composition and function, in- fluencing soil carbon and nitrogen cycling (Tang et al., 2019). In the calcareous soils of karst area, Ca emerged as a dominant indicator of soil quality due to its high contri- bution in PCA, strong association with SOC, and regula- tory role in microbial processes (Y ang et al., 2019b). Soil formation in karst regions involves two main processes: carbonate dissolution and subsequent bedrock weathering (Wang et al., 1999). In these environments, Si is primarily found in quartz and silicate minerals, which have low solubility under subaerial conditions. Conse- quently, Si accumulates in residual mineral fractions, contributing to the stability of weathered rock residues (Lasaga, 1984; White et al., 1996). The Si:(Fe+Al) ratio (Figure S3) serves as an indicator of weathering intensity in carbonate rocks (Sugitani et al., 1996). This ratio ini- tially decreased with cultivation age but increased after 30 years, suggesting that carbonate dissolution domi- nated early soil formation stages, accompanied by al- MENGXIA ZHOU, HUI YANG, CHENG ZHANG, JIANHUA CAO & DEGEN ZHU 196 ACTA CARSOLOGICA 54/2-3 – 2025 litization. In later stages, however, the decreasing MgO and CaO contents-typical of carbonate rocks, indicated extensive leaching due to high CO 2 and water levels. This process also caused weathering and decomposition acid- insoluble residues containing Ca and Mg (host minerals include amphibole, plagioclase, montmorillonite, and chlorite, etc.). The depletion of base cations such as Ca 2+ and Mg 2+ may reduce soil buffering capacity, thereby en- hancing susceptibility to acidification driven by agricul- tural inputs or root exudates, contributing to an increase in SiO 2 (Si being a component of quartz and silicate) and decreased Fe 2 O 3 and Al 2 O 3 (Al from silicate and alumi- num oxides and Fe from hornblende, chlorite, and iron oxides). These trends reflect the weathering of acid-in- soluble residues due to cultivation. Carbonate weather- ing occurs more slowly than dissolution. In this study, acid-insoluble residues weathered only in the advanced stages of cultivation, with human activities accelerat- ing both soil-forming processes. Consequently, Si was included in the MDS as mineralogical evidence of soil formation through carbonate weathering, indicating that soil derived from weathered carbonate rocks, widespread in southwest China, are an important source of soil in karst areas. 4.3 EFFECT OF CULTIV ATION ON SOIL QUALITY Agricultural practices are likely to reduce the content of soil organic matter, alter soil structure, decrease porosity and increase soil bulk density (Jones, 1971; Jonas et al., 2016). In this study, most soil physicochemical proper- ties exhibited statistically significant differences, indicat- ing that cultivation has disturbed the soil, contributing to property changes and substantial impacts on soil func- tion (Davari et al., 2020; Jaiyeoba, 2003; Khormali et al., 2009). The highest levels of SOC and TN were observed in NR, but reclamation and agricultural operations caused severe losses of SOC and TN. These losses were influ- enced by the degree of human disturbance and the annu- al harvesting of citrus, which removed C and N from the nutrient cycle (Yang et al., 2019a; Jaiyeoba, 2003; Wang et al., 2021). Additionally, fire-based reclamation can de- grade soil aggregate structure and microbial communi- ties in a short time scales, further contributing to SOM loss (Jones et al., 2016; Jaiyeoba, 2003). The imbalance between limited organic inputs and increased mineral- ization rates under tillage resulted in a net decline in SOC over time (Jones, 1971). Soil pH decreased by nearly two units with pro- longed cultivation, indicating progressive acidification of the initially alkaline calcareous soils. This shift is at- tributed to several factors: (1) root exudation of organic acids by citrus plants, (2) accumulation of phenolic com- pounds from litter decomposition, and (3) nitrification of ammonium-based fertilizers (Zhang et al., 2000; Chat- terjee et al., 2017). Acidification reduces base saturation and cation exchange capacity, further compromising soil buffering and fertility. Soil texture is a reliable indicator of soil properties, including erosion potential, pore structure and permea- bility (Plante et al., 2006). Silt and clay particles have larg- er surface areas than sand, giving them strong sorption properties and facilitating the formation of soil aggre- gates. In organically rich soils, these particulars combine with organic material to form stable aggregates, resulting in high water retention, permeability, and homogeneity. Conversely, dispersed clay particles can clog soil pores during droughts, leading to soil slumping and reduced agricultural productivity (Ferreras et al., 2000; Six et al., 2000). Thus, silt content serves as a critical physical in- dicator of soil structure, and its decline has significant implications for soil chemical and biology. Khormali et al. (2009) have reported differences in soil texture, attrib- uting increased silt content in the surface layer follow- ing deforestation to erosion and the loss of clay. In this study, the gradual rise in soil silt content with extended cultivation reflects accelerated physical fragmentation of soil particles, contributing to a finer-grained soil texture. An increase in silt content may indicate enhanced physi- cal disaggregation due to tillage, which could elevate the susceptibility of soil to water erosion, particularly in sloping landscapes characteristic of karst regions. The observed increases in clay and silt content contrast with typical agricultural systems, possibly reflecting ongoing weathering of carbonate bedrock or localized deposition in this karst landscape, justifying the inclusion of silt in the MDS analysis. The SQI of NR was significantly higher than that of all cultivated treatments, confirming that prolonged agriculture has degraded soil quality. This finding aligns with numerous studies highlighting that frequent tillage and harvesting over time may contribute to soil struc- ture degradation and a loss of soil organic matter inputs (Schnitzer et al., 2006). In contrast, revegetation pro- cesses have been shown to improve soil structure and soil properties (Zhang et al., 2019; Huang et al., 2021; Xiao et al., 2017; Rojas et al., 2016). Soil quality is influenced by multiple factors, including lithology, land use, vegetation type and human activity (Zhang et al., 2019), with vary- ing constraints at different cultivation stages. Compared to NR, there was a significant loss of SOM in the 0a treat- ment following burning. Continuous cultivation led to the leaching of Ca and Mg from karst soils, undermining the stability of SOM (Yang et al.2019b). This process is accompanied by an increase in the clay and silt content of REDUCED SOIL QUALITY WITH CULTIV ATION OF CALCAREOUS SOILS IN SUBTROPICAL CHINA KARST REGION ACTA CARSOLOGICA 54/2-3 – 2025 197 the soil, leading to greater soil compaction and reduced water and nutrient retention capacity, which all adversely affect agricultural production. Based on the soil factors selected by MDS, it is reasonable to conclude that the sharp decline in soil quality of artificial forest land in karst areas is primarily due to two factors: (1) Geological constraints: the low abundance of acid-insoluble residu- um in carbonate bedrock limits the potential for new soil formation; (2) Anthropogenic acceleration: cultivation practices intensify erosion, SOM loss, and weathering, pushing soils toward irreversible degradation. 5. CONCLUSION This study demonstrates that agricultural cultivation exerts a profound and time-dependent degradation ef- fect on soil quality in karst ecosystems. Key findings include: (1) Soil quality exhibits a progressive decline with increasing duration of cultivation, highlighting the substantial adverse impact of farming on the soil physi- cochemical properties and overall quality of the soil. (2) The MDS method identified Ca, silt and Si as pivotal in- dicators for quantifying tillage's effect on soil quality. (3) The severe soil degradation in the karst area is attributed primarily to the inherently low abundance of acid-in- soluble residues in the carbonate bedrock which limits soil formation coupled with human activities that disrupt pedogenic processes. In summary, integrating cultivation effects into SQI evaluations fosters a robust linkage be- tween soil quality metrics and agricultural management practices. This study provides a scientific basis for devel- oping targeted strategies aimed at promoting sustainable soil management, restoring soil fertility, and enhancing ecosystem services in fragile karst environments. ACKNOWLEDGEMENTS The study is supported by the National Natural Science Foundation of China under Grant No. 42361144885 and No. 42271094. The Guangxi Key Research and De- velopment Program, China under Grant No. Guike- AB22035004, Natural Resources Science and Technol- ogy Strategic Research Project (2023-ZL-23). The author would like to thank Zihong Zhu for the generous help with the experiment. REFERENCES Andrews, S., Karlen, D., Mitchell, J., 2002. A compari- son of soil quality indexing methods for vegetable production systems in Northern California. Ag- riculture, Ecosystems Environment, 90(1): 25-45. https://doi.org/10.1016/S0167-8809(01)00174-8 Bao, S., Qin, H., Lao, J., 2000. Soil and agricultural chem- istry analysis, Third ed. China Agricultural Press, Beijing, 495. Bünemann, E., Bongiorno, G., Bai, Z., Creamer, R., de Deyn, G., de Goede, R., Fleskens, L., Geissen, V., Kuyper, T., Mäder, P., Pulleman, M., Sukkel, W., van Groenigen, J., Brussaard, L., 2018. Soil qual- ity–A critical review. Soil Biology and Biochem- istry, 120: 105-125. https://doi.org/10.1016/j.soil- bio.2018.01.030 Carmeis Filho, A., Penn, C., Crusciol, C., Calonego, J., 2017. Lime and phosphogypsum impacts on soil or- ganic matter pools in a tropical Oxisol under long- term no-till conditions. Agriculture, Ecosystems Environment, 241: 11-23. https://doi.org/10.1016/j. agee.2017.02.027 Chatterjee, A., Teboh, J., Nelson, S., Aberle, E., Schatz, B., Zilahi-Sebess, S., 2017. Long-term effect of nitrogen and tillage management on soil carbon pools in the semiarid northern great plains. Communications in Soil Science and Plant Analysis, 48(7): 730-740. https://doi.org/10.1080/00103624.2017.1298786 Davari, M., Gholami, L., Nabiollahi, K., Homaee, M., Jafari, H., 2020. Deforestation and cultivation of sparse forest impacts on soil quality (case study: MENGXIA ZHOU, HUI YANG, CHENG ZHANG, JIANHUA CAO & DEGEN ZHU 198 ACTA CARSOLOGICA 54/2-3 – 2025 West Iran, Baneh). Soil and Tillage Research, 198: 104504. https://doi.org/10.1016/j.still.2019.104504 Doran, J., Parkin, T., 1994. Defining and assessing soil quality. Defining Soil Quality for A Sustainable Environment, 35: 1-21. https://doi.org/10.2136/ss- saspecpub35.c1 Ferreras, L., Costa, J., Garcia, F., Pecorari, C., 2000. Ef- fect of no-tillage on some soil physical properties of a structural degraded Petrocalcic Paleudoll of the southern “Pampa” of Argentina. Soil and Tillage Research, 54(1-2): 31-39. https://doi.org/10.1016/ S0167-1987(99)00102-6 Foley, J., DeFries, R., Asner, G., Barford, C., Bonan, G., Carpenter, S., Chapin, F., Coe, M., Daily, G., Gibbs, H., Helkowski, J., Holloway, T., Howard, E., Kucha- rik, C., Monfreda, C., Patz, J., Prentice, C., Raman- kutty, N., Snyder, P., 2005. Global consequences of land use. Science, 309(5734): 570-574. http://doi. org/10.1126/science.1111772 Griffiths, B., Ball, B., Daniell, T. , Hallett, P., Neilson, R., Wheatley, R., Osler, G., Bohanec, M, 2010. Integrat- ing soil quality changes to arable agricultural sys- tems following organic matter addition, or adoption of a ley-arable rotation. Applied Soil Ecology, 46(1): 43-53. https://doi.org/10.1016/j.apsoil.2010.06.012 Haynes, R., Naidu, R., 1998. Influence of lime, fertilizer and manure applications on soil organic matter con- tent and soil physical conditions: a review. Nutrient Cycling in Agroecosystems, 51(2): 123-137. http:// doi.org/10.1023/A:1009738307837 Hu, C., Wright, A., Lian, G., 2019. Estimating the spatial distribution of soil properties using environmen- tal variables at a catchment scale in the loess hilly area, China. International Journal of Environmental Research and Public Health, 16(3): 491. https://doi. org/10.3390/ijerph16030491 Huang, W ., Zong, M., Fan, Z., Feng, Y., Li, S., Duan, C., Li, H., 2021. Determining the impacts of deforesta- tion and corn cultivation on soil quality in tropical acidic red soils using a soil quality index. Ecological Indicators, 125: 107580. https://doi.org/10.1016/j. ecolind.2021.107580 IUSS Working Group, 2014. World reference base for soil resources 2014. International soil classification sys- tem for naming soils and creating legends for soil maps; World Soil Resources Reports No. 106; FAO: Rome, Italy. Jaiyeoba, I., 2003. Changes in soil properties due to con- tinuous cultivation in Nigerian semiarid Savannah. Soil and Tillage Research, 70(1): 91-98. https://doi. org/10.1016/S0167-1987(02)00138-1 Jiang, Z., Lian, Y., Qin, X., 2014. Rocky desertification in Southwest China: impacts, causes, and restora- tion. Earth-Science Reviews, 132: 1-12. https://doi. org/10.1016/j.earscirev.2014.01.005 Jones, A., Orton, T., Dalal, R., 2016. The legacy of crop- ping history reduces the recovery of soil carbon and nitrogen after conversion from continuous cropping to permanent pasture. Agriculture, Ecosystems En- vironment, 216: 166-176. https://doi.org/10.1016/j. agee.2015.09.029 Jones, M., 1971. The maintenance of soil organic matter under continuous cultivation at Samaru, Nigeria. The Journal of Agricultural Science, 77(3): 473-482. https://doi.org/10.1017/S0021859600064558 Karaca, S., Dengiz, O., Turan, İ.D., Özkan, B., Dedeoğlu, M., Gülser, F., Sargin, B., Demirkaya, S. and Ay, A., 2021. An assessment of pasture soils quality based on multi-indicator weighting approaches in semi- arid ecosystem. Ecological Indicators, 121: 107001. https://doi.org/10.1016/j.ecolind.2020.107001 Karlen, D., Stott, D., 1994. A framework for evaluating physical and chemical indicators of soil quality. De- fining Soil Quality for A Sustainable Environment, 35: 53-72. https://doi.org/10.2136/sssaspecpub35.c4 Khormali, F., Ajami, M., Ayoubi, S., Srinivasarao, C., Wani, S., 2009. Role of deforestation and hillslope position on soil quality attributes of loess-derived soils in Golestan province, Iran. Agriculture, Eco- systems Environment, 134(3-4): 178-189. https:// doi.org/10.1016/j.agee.2009.06.017 Lal, R., 2004. Soil carbon sequestration impacts on global climate change and food security. Science, 304(5677): 1623-1627. https://doi.org/10.1126/sci- ence.1097396 Lal, R., 2010. Managing soils and ecosystems for mitigat- ing anthropogenic carbon emissions and advancing global food security. Bioscience, 60(9): 708-721. https://doi.org/10.1525/bio.2010.60.9.8. Lasaga, A., 1984. Chemical kinetics of water‐rock in- teractions. Journal of Geophysical Research: Solid Earth, 89(B6): 4009-4025. http://doi.org/10.1029/ JB089iB06p04009 Lei, Z., Yu, D., Zhou, F., Zhang, Y., Yu, D., Zhou, Y., Han, Y., 2019. Changes in soil organic carbon and its in- fluencing factors in the growth of Pinus sylvestris var. mongolica plantation in Horqin Sandy Land, Northeast China. Scientific Reports, 9(1): 1-12. http://doi.org/10.1038/s41598-019-52945-5 Li, P ., Zhang, T., Wang, X., Yu, D., 2013. Development of biological soil quality indicator system for subtropi- cal China. Soil and Tillage Research, 126: 112-118. https://doi.org/10.1016/j.still.2012.07.011 Li, Q., Xu, M., Zhao, Y., Gao, L., Zhang, J., Zhang, X., 2012. Gully Erosion Soil Quality Assessment on the Cultivated Slope Land in the Loess Plateau Region, REDUCED SOIL QUALITY WITH CULTIV ATION OF CALCAREOUS SOILS IN SUBTROPICAL CHINA KARST REGION ACTA CARSOLOGICA 54/2-3 – 2025 199 China. Journal of Natural Resources, (06): 1001- 1012. http://doi.org/10.11849/zrzyxb.2012.09.001 (in Chinese with English Abstract) Li, Z., Xu, X., Zhu, J., Xu, C., Wang, K., 2019. Sediment yield is closely related to lithology and landscape properties in heterogeneous karst watersheds. Journal of Hydrology, 568: 437-446. https://doi. org/10.1016/j.jhydrol.2018.10.076 Liao, H., Zheng, C., Li, J., Long, J., 2018. Dynamics of soil microbial recovery from cropland to orchard along a 20-year chronosequence in a degraded karst ecosystem. Science of the Total Environment, 639: 1051-1059. https://doi.org/10.1016/j.scito- tenv.2018.05.246 Mukherjee, A., Lal, R., 2014. Comparison of soil quality index using three methods. PloS One, 9(8): e105981. http://doi.org/10.1371/journal.pone.0105981 Muneer, M., Oades, J., 1989. The role of Ca-organic inter- actions in soil aggregate stability. I. Laboratory stud- ies with glucose 14 C, CaCO 3 and CaSO 4 . 2H 2 O. Soil Research, 27(2): 389-399. https://doi.org/10.1071/ SR9890389 Muneer, M., Oades, J. M., 1989. The role of Ca-organ- ic interactions in soil aggregate stability. II. Field studies with 14 C-labeled straw, CaCO 3 and CaSO 4 . 2H 2 O. Soil Research, 27(2): 401-409. https://doi. org/10.1071/SR9890401 Muneer, M., Oades, J., 1989. The role of Ca-organic inter- actions in soil aggregate stability. III. Mechanisms and models. Soil Research, 27(2): 411-423. https:// doi.org/10.1071/SR9890411 Plante, A., Conant, R., Stewart, C., Paustian, K., Six, J., 2006. Impact of soil texture on the distribution of soil organic matter in physical and chemical frac- tions. Soil Science Society of America Journal, 70(1), 287-296. https://doi.org/10.2136/sssaj2004.0363 Qi, Y., Darilek, J. L., Huang, B., Zhao, Y., Sun, W ., Gu, Z., 2009. Evaluating soil quality indices in an agricul- tural region of Jiangsu Province, China. Geoderma, 149(3-4): 325-334. https://doi.org/10.1016/j.geo- derma.2008.12.015 Rahmanipour, F ., Marzaioli, R., Bahrami, H., Fereidouni, Z., Bandarabadi, S., 2014. Assessment of soil qual- ity indices in agricultural lands of Qazvin Province, Iran. Ecological Indicators, 40: 19-26. https://doi. org/10.1016/j.ecolind.2013.12.003 Raiesi, F., 2017. A minimum data set and soil quality in- dex to quantify the effect of land use conversion on soil quality and degradation in native rangelands of upland arid and semiarid regions. Ecological Indicators, 75: 307-320. https://doi.org/10.1016/j. ecolind.2016.12.049 Rojas, J., Prause, J., Sanzano, G., Arce, O., Sanchez, M., 2016. Soil quality indicators selection by mixed models and multivariate techniques in deforested areas for agricultural use in NW of Chaco, Argenti- na. Soil and Tillage Research, 155: 250-262. https:// doi.org/10.1016/j.still.2015.08.010 Rowley, M., Grand, S., V errecchia, É., 2018. Calcium-me- diated stabilisation of soil organic carbon. Biogeo- chemistry, 137(1): 27-49. http://doi.org/10.1007/ s10533-017-0410-1 Schnitzer, M., McArthur, D., Schulten, H., Kozak, L., Huang, P., 2006. Long-term cultivation effects on the quantity and quality of organic matter in se- lected Canadian prairie soils. Geoderma, 130(1- 2): 141-156. https://doi.org/10.1016/j.geoder- ma.2005.01.021 Shukla, M., Lal, R., Ebinger, M., 2006. Determining soil quality indicators by factor analysis. Soil and Tillage Research, 87(2): 194-204. https://doi.org/10.1016/j. still.2005.03.011 Six, J., Paustian, K., Elliott, E., Combrink, C., 2000. Soil structure and organic matter I. Distribution of ag- gregate‐size classes and aggregate‐associated car- bon. Soil Science Society of America Journal, 64(2): 681-689. https://doi.org/10.2136/sssaj2000.642681x Sugitani, K., Horiuchi, Y., Adachi, M., Sugisaki, R., 1996. Anomalously low Al 2 O 3 /TiO 2 values for Archean cherts from the Pilbara Block, Western Australia— possible evidence for extensive chemical weath- ering on the early earth. Precambrian Research, 80(1-2): 49-76. https://doi.org/10.1016/S0301- 9268(96)00005-8 Tang, J., Tang, X., Qin, Y ., He, Q., Yi, Y ., Ji, Z., 2019. Karst rocky desertification progress: soil calcium as a pos- sible driving force. Science of the Total Environ- ment, 649: 1250-1259. https://doi.org/10.1016/j. scitotenv.2018.08.242 Wang, G., Zhu, T., Zhou, J., Yu, Y., Petropoulos, E., Mül- ler, C., 2022. Slash-and-burn in karst regions low- ers soil gross nitrogen (N) transformation rates and N-turnover. Geoderma, 425: 116084. https://doi. org/10.1016/j.geoderma.2022.116084 Wang, M., Chen, H., Zhang, W., Wang, K., 2021. Soil organic carbon stock and its changes in a typical karst area from 1983 to 2015. Journal of Soils and Sediments, 21(1): 42-51. https://doi.org/10.1007/ s11368-020-02745-6 Wang, S., Ji, H., Ouyang, Z., Zhou, D., Zhen, L., Li, T., 1999. Preliminary study on weathering and pedo- genesis of carbonate rock. Science in China Se- ries D: Earth Sciences, 42(6): 572-581. http://doi. org/10.1007/BF02877784 Wei, X., Deng, X., Xiang, W ., Lei, P ., Ouyang, S., Wen, H., Chen, L., 2018. Calcium content and high calcium MENGXIA ZHOU, HUI YANG, CHENG ZHANG, JIANHUA CAO & DEGEN ZHU 200 ACTA CARSOLOGICA 54/2-3 – 2025 adaptation of plants in karst areas of southwestern Hunan, China. Biogeosciences, 15(9): 2991-3002. https://doi.org/10.5194/bg-15-2991-2018 White, A., Blum, A., Schulz, M., Bullen, T ., Harden, J., Pe- terson, M., 1996. Chemical weathering rates of a soil chronosequence on granitic alluvium: I. Quantifica- tion of mineralogical and surface area changes and calculation of primary silicate reaction rates. Geo- chimica et Cosmochimica Acta, 60(14): 2533-2550. https://doi.org/10.1016/0016-7037(96)00106-8 Wiesmeier, M., von Lützow, M., Spörlein, P., Geuß, U., Hangen, E., Reischl, A., Schilling, B., Kögel-Knab- ner, I., 2015. Land use effects on organic carbon stor- age in soils of Bavaria: the importance of soil types. Soil and Tillage Research, 146: 296-302. https://doi. org/10.1016/j.still.2014.10.003 Wu, H., Guo, Z., Peng, C., 2003. Land use induced changes of organic carbon storage in soils of China. Global Change Biology, 9(3): 305-315. http://doi/ org/10.1046/j.1365-2486.2003.00590.x Xiao, S., Ye, Y., Xiao, D., Chen, W ., Zhang, W ., Wang, K., 2019. Effects of tillage on soil N availability, aggre- gate size, and microbial biomass in a subtropical karst region. Soil and Tillage Research, 192: 187- 195. https://doi.org/10.1016/j.still.2019.05.006 Xie, Y ., Y ang, L., Zhu, T ., Y ang, H., Zhang, J., Y ang, J., Cao, J., Bai, B., Jiang, Z., Liang, Y ., Lan, F ., Meng, L., Mül- ler, C., 2018. Rapid recovery of nitrogen retention capacity in a subtropical acidic soil following affor- estation. Soil Biology and Biochemistry, 120: 171- 180. https://doi.org/10.1016/j.soilbio.2018.02.008 Yang, H., Liang, Y., Xu, J., Cao, J., 2018. Research prog- ress of the relationship between soil calcium and soil organic carbon in karst area. Guangxi Scienc- es, 25(5): 505-514. http://doi.org/10.13656/j.cnki. gxkx.20181106.001 (in Chinese with English Ab- stract) Yang, H., Mo, B., Zhou, M., Zhu, T., Cao, J., 2019. Effects of plum plantation ages on soil organic carbon min- eralization in the karst rocky desertification eco- system of southwest China. Forests, 10(12): 1107. https://doi.org/10.3390/f10121107 Yang H., Prelovšek M., Huang F., Zhang C., Cao J., Ravbar N., 2019. Quantification and evaluation of soil organic carbon and its fractions: case study from the Classical Karst, SW Slovenia. Acta Car- sologica, 48(3): 295-311. https://doi.org/10.3986/ ac.v48i3.7305 Yang, H., Xie, Y., Zhu, T., Zhou, M., 2021. Reduced Or- ganic Carbon Content during the Evolvement of Calcareous Soils in Karst Region. Forests, 12(2): 221. https://doi.org/10.3390/f12020221 Yuan, D., Zhu, D., Weng, J., Zhu, X., Han, X., Wang, X., Cai, G., Zhu, Y., Cui, G., Deng, Z., 1991. Karst of China, Geological Publishing House, Beijing. 224. Zhang, C., Xue, S., Liu, G., Song, Z., 2011. A compari- son of soil qualities of different revegetation types in the Loess Plateau, China. Plant and Soil, 347(1): 163-178. http://doi.org/10.1007/s11104-011-0836-5 Zhang, S., Gao, Z., Liu, H., 2000. Continuous cropping obstacle and rhizospherric microecology Ⅲ. Soil phenolic acids and their biological effect. Chinese journal of applied ecology, 11(5): 741-744. http:// doi.org/10.13287/j .1001-9332.2000.0179 (in Chi- nese with English Abstract) Zhang, Y ., Xu, X., Li, Z., Liu, M., Xu, C., Zhang, R., Luo, W ., 2019. Effects of vegetation restoration on soil qual- ity in degraded karst landscapes of southwest China. Science of the Total Environment, 650: 2657-2665. https://doi.org/10.1016/j.scitotenv.2018.09.372 Zhang, Y., Xu, X., Li, Z., Xu, C., Luo, W ., 2021. Improve- ments in soil quality with vegetation succession in subtropical China karst. Science of The Total En- vironment, 775: 145876. https://doi.org/10.1016/j. scitotenv.2021.145876 REDUCED SOIL QUALITY WITH CULTIV ATION OF CALCAREOUS SOILS IN SUBTROPICAL CHINA KARST REGION ACTA CARSOLOGICA 54/2-3 – 2025 201 MENGXIA ZHOU, HUI YANG, CHENG ZHANG, JIANHUA CAO & DEGEN ZHU SUPPLEMENT Table S1: The C.V and range of sensitive soil indicators. Min Max Mean S.E Coefficient of variation Range ROC 0.1 1.37 0.549 0.34 0.619 1.27 Sand 1.55 36.66 13.571 10.066 0.742 35.11 Table S2: Principal component analysis of soil quality indicators. Soil quality indexes 1st principal component 2nd principal component 3rd principal component 4th principal component Capacity Weight Capacity Weight Capacity Weight Capacity Weight pH 0.08 0.007 -0.512 0.01 -0.02 0.005 0.564 0.136 SOC 0.942 0.087 0.177 0.035 0.028 0.007 -0.199 0.048 TN 0.913 0.084 0.214 0.042 0.013 0.003 -0.227 0.055 C/N 0.82 0.076 -0.149 0.029 0.288 0.068 0.11 0.027 DOC 0.795 0.073 0.228 0.044 -0.232 0.055 0.206 0.05 RON 0.889 0.082 0.193 0.038 0.184 0.043 -0.265 0.064 SWC 0.215 0.02 -0.414 0.081 0.622 0.146 -0.238 0.058 FWHC 0.645 0.059 -0.384 0.075 0.309 0.073 0.395 0.095 Mg 0.79 0.073 0.233 0.045 -0.121 0.028 0.23 0.056 Ca 0.955 0.088 0.108 0.021 -0.022 0.005 -0.141 0.034 P -0.823 0.076 0.267 0.052 0.298 0.07 -0.003 0.001 Si -0.007 0.001 -0.108 0.021 0.779 0.183 -0.277 0.067 Al 0.815 0.075 -0.196 0.038 -0.382 0.09 -0.125 0.03 Fe 0.8 0.074 -0.24 0.047 -0.173 0.041 -0.145 0.035 Clay -0.121 0.011 0.633 0.124 0.332 0.078 0.508 0.123 Silt 0.442 0.041 0.803 0.157 0.229 0.054 0.123 0.03 SBD 0.792 0.073 -0.265 0.052 0.215 0.051 0.381 0.092 Variance contribution /% 50.822 12.527 10.295 7.95 Cumulative variance contribution /% 50.822 63.349 73.644 81.593 202 ACTA CARSOLOGICA 54/2-3 – 2025 REDUCED SOIL QUALITY WITH CULTIV ATION OF CALCAREOUS SOILS IN SUBTROPICAL CHINA KARST REGION Figure S1: Correlogram of the dataset. *** correlation is significant at P ≤0.01 level; ** correlation is significant at P ＜0.01 level; * correla tion is significant at P ＜0.05 level. Figure S2: Linear regression of SQI based on TDS and MDS methods. ACTA CARSOLOGICA 54/2-3 – 2025 203 MENGXIA ZHOU, HUI YANG, CHENG ZHANG, JIANHUA CAO & DEGEN ZHU Figure S3: Weathering indices. 204 ACTA CARSOLOGICA 54/2-3 – 2025