Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | light RD > IRD. The TOC, > LRD > 1 MRD > erent RD grades in the central ff , and L. C. Wu 1 3334 3333 , J. J. Li erent impacts on soil fertility indicators. The changing 2 intensive RD (IRD), whereas the changing trend of ff > erent RD regions was: potential RD (PRD) ff , F. X. Cao 1 ects of the succession of RD on soil fertility were studied by investigating ff , J. Zhong 1 moderate RD (MRD) > This discussion paper is/has beenPlease under refer review to for the the corresponding journal final Solid paper Earth in (SE). SE if available. Key Laboratory of Cultivation and Protection for Non-Wood Forest Trees, Ministry of College of Life Science and Technology, Central South University of Forestry and Technology, Abstract Expanding of karst rockydestructed desertification ecosystem and (RD) local area economicto in development understand lagging southwestern the behind. It soil haslands. is fertility The important led at e to RD regions for the sustainable management of karst total phosphorus (TP), cationphosphorous exchange (MBP), capacity and (CEC), bulk density MBC,evaluate (BD) the MBN, could soil microbial be fertility mass regarded due as to their the close key indicators correlations to to the integrated1 fertility. Introduction Karst rocky desertificationsoil is erosion, a extensive processlandscape, exposure of leading of karst to bedrocks, drastic landis and decrease degradation recognized in the involving as soil appearance seriousSome productivity an of mountain (Wang areas obstacle a et of al.,with to desert-like central evergreen 2004b), local broad-leaved Hunan and forest province, sustainable historicallyreclamation, China, development but being are now (Wu karst included under et region deforestation in covered and al., the over- 2011). largest karst geomorphologic distributing areas in the stands and analyzing the soil samples with di (LRD) other indicators was not entirely consistenttrend with of the succession soil of fertility RD.of The was degradation integrated basically parallel soil to fertility the aggravation was of in RD, and the the order strength of PRD trend of total organicmicrobial carbon (TOC), biomass total carbon nitrogen19 (TN), selected (MBC), available phosphorous indicators and (AP), in microbial di biomass nitrogen (MBN) out of province, China, using thethat principal the component succession analysis of RD method. had The di results showed Evaluation of soil fertility insuccession the of karst rocky desertification using principal component analysis L. W. Xie Solid Earth Discuss., 6, 3333–3359,www.solid-earth-discuss.net/6/3333/2014/ 2014 doi:10.5194/sed-6-3333-2014 © Author(s) 2014. CC Attribution 3.0 License. 1 Education, Central South University ofChina Forestry and2 Technology, 410004, Hunan, Changsha 410004, China Received: 10 November 2014 –– Accepted: Published: 16 18 November December 2014 2014 Correspondence to: L. Wu ([email protected]) Published by Copernicus Publications on behalf of the European Geosciences Union. 5 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent ff orestation. ff had been issued by 3336 3335 erent regions (Liu et al., 2006). Choosing appropriate ff ected by RD. Especially, using a minimum dataset to reduce the need ff ected by the succession of rocky desertification, and (ii) to identify some ff The objectives of this work were: (i) to clarify how 19 selected soil fertility indicators The soil fertility depends on local climate, soil-forming conditions, eco-environment, In order to avoid information overlapping from high-dimensional datasets, dimension In the process of sustainable management, it is important to determine the status For example, to enforce the sustainable management of karst lands, in 2011, Monitoring Rules of Rocky Desertification in Hunan Province RD grades. are a reasonable and sensitive indicators to evaluate soil fertility of karst lands with di variables called principal components (Liu et al., 2003). and anthropogenic influence in di reduction is usually performed to get(PCA) a is minimum regarded dataset. as Principal a component statisticalof analysis procedure observations using dimension with reduction to possibly convert correlated a set variables into a set of linearly uncorrelated of soil quality,prerequisite so to investigation rationallyfertility on management changes soil and associated fertility with utilizationpoorly the understood could of (Wang succession be and karst ofsoil Li, regarded RD lands. on 2007) areas in due as However, a to the soil anfor lacking karst determining of essential lands a method have broad howkarst been to range lands evaluate of during the indicators succession to of assess RD have soil not fertility been (Yao achieved et at al., present. 2013) of indicators is vital toshould evaluate be soil included fertility. into Those evaluatingempirically system. indicators Generally, based evaluating that indicators influence on are plant chosen soil the growth fertility researching indicators fruitsecosystem should of (Fu be predecessors. et paid Butfrom al., close the experts 2010). attention adaptability on the Based to of stands19 karst on investigation, selected we area the indicators. evaluated soil due analyses fertility to of of karst its literatures lands fragile and using suggestions soil quality ismanagement of (irrigation, fertilization, fundamental and cultivation) importance (Fallahzadeand and for Hajabbasi, it 2012), agricultural is production alsoenvironment and a management central soil issue (Tilman fertility in et the al., decisions 2002). on food Soil security, fertility poverty reduction is and a major component of soil quality on karst regions (Deng and Jiang, 2011; Li et al., 2013), because the a These measures are beneficial tolands. rehabilitation and sustainable management of karst southwestern China (Huang andanthropogenic Cai, 2007; driving Xiong forces etdesertification al., are (T. 2009). Wang et responsible Climate al., changes 2013; for X.(Wang and Wang the and et al., Jia, development 2013) 2013), which ofplaying can soil important cause aeolian/sandy dust and roles storms water in2009b; losses the Yan (Cerdà aggravation and of and Cai, karst 2013). Lavée,China, rocky This 1999), desertification and has and the (Y. gradually B. are government attracted Lirocky also the and et desertification national-wide researchers al., land attention are in by taking2008). sustainable active management measures (Bai to et meliorate al., 2013; Huang et al., Hunan Provincial Bureau of Forestry, ininto which 4 rocky grades, desertification namely (RD)intensive potential was RD classified RD (IRD) (PRD), based light onbedrock RD the (LRD), exposure soil moderate according depth, RD vegetation2004a; to (MRD), coverage, Xiong vegetation and some et type al., reported 2009) and withfrom classification minor one modifications. methods grade The changing to (Wang process anotherWang, of et 2006), was karst which called land al., means succession an observable ofas process RD of vegetation changes here type, of and vegetation karst elsewhere ecosystem coverage,to such (Xie bedrock IRD and orderly exposure, or and vice soilkarst versa. depth Furthermore, regions from on with PRD stands higher investigation, we grades found that (MRD some or IRD) had been enclosed for a 5 5 25 20 10 15 25 10 15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | C, and with ◦ cials at local Forestry ffi C until culturing microbe to ◦ 20 − erent RD grades every county ff E in the central Hunan province, China. in area. At each sampling plot, six points 0 2 05 ◦ 3338 3337 –112 0 40 ◦ N and 110 0 18 KCl in distilled water. Bulk density (BD), capillary moisture capacity ◦ 1 − –28 0 55 ◦ Rocky desertification (RD) regions are divided into 4 grades, namely potential RD Total organic carbon (TOC) content was measured by dichromate oxidation method Topographic features oflandforms, characterized by this hills, syncline regiontropical valleys and warm-moist include mountains. The climate region karst with is a mean landforms sub- annual and air temperature fluvial of erosion 18.3 were evenly distributed by walkingpoint, on three the cores way (5 like cm letter diameter, 0–20 “S” cm over depth) the were taken area. from And three at vertices each of one mean annual precipitationsfrom of China 1425 Meteorological mm Datacn/home.do). from Sharing 2000 Service to System online 2012, (http://cdc.cma.gov. which2.2 were obtained Soil sampling and handling We used core cuttercarefully in (5 the cm field.study. i.d.) There The were to permissions no take for endangeredLianyuan the sampling or (LY), Longhui soil protected (LH), locations species samples were (SD), involved Xinhuarespectively. before approved (XH), in by and covering this Xinshao Forestry the (XS) counties, Bureau holes of (PRD), light RD (LRD), moderatedepth, RD (MRD), vegetation and intensive coverage, RD bedrock15 (IRD) based exposure to on and the 22 soil vegetationwere December type selected 2011, (Table as four 1).Bureau. the typical From The sampling plots plots, sites, designated withXS4 which LY1–LY4, (Table LH1–LH4, di 1), guided SD1–SD4, were by all XH1–XH4, approximately the and 400 m o XS1– triangle patch (0.5 m sidethese length). three After cores plant were debris,composite sample. roots mixed Thus, and totally thoroughly 120 stones in soilcomposite were samples a sample were removed, was collected clean divided in into the pail two fieldThe parts, work. without field-moist a Every samples field-moist sieving were sample kept to and in an refrigerator give air-dried under one. one enumerate bacteria, fungi and actinomycetes, and(MBC), analyzing microbial microbial biomass biomass carbon nitrogenThe (MBN) air-dried and samples microbial were biomass used phosphorus to (MBP). determinate chemical2.3 and physical parameters. Soil physicochemical properties analyses Soil pH wasof determined soil using to 1 a molL (CMC), combined field moisture glass capacity (FMC), electrode capillarywere porosity with (CAP), determined and 1 total by : porosity 2.5 core (TOP) using (w cutter digital : camera v) method. method ratios after Vegetation calculating coverageof the was ratio image of measured red recorded on tocalculating near-infrared and site brightness the processed ratio (Hu2000), of et bedrock bedrock exposure al., was area estimated 2007;a to using dimension White Nikon whole measurements DTM322 et image on site totalCation al., (Hu using exchange station et 2000). capacity surveying (CEC) al., Based instrument wasmethod 2007; on determined (Zou (Nikon-Trimble White et by Co. al., mixed et Ltd., 2009). ammonium al., Japan). acetate EDTA (Yeomans and Bremner,determination 1988). method Total(TP) after and nitrogen digestion total (TN)sodium potassium (Brookes hydroxide (TK) (Smith was et and contents Bain,available al., measured were 1982) potassium respectively. measured (AK) Available 1985a). phosphorus by were after (AP) tested and using Total fusion-pretreated Kjedahl Mehlich with phosphorus 3 extracting method (Sims, 1989). 2 Materials and methods 2.1 Study area The sampling sites(LY), Longhui are (LH), in Shaodongranging (SD), 26 karst Xinhua region (XH), and involving Xinshao five (XS), counties, approximately namely 5 5 10 15 20 25 25 10 15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , = − kj ij kj P x ( P × 19; and = k 0 ij = x (number of (number of m V AR m 1 p is the standard p = P k i S = erent scales. Data j is the original value ff F ij 1, 2, . . . , x = 1, 2, . . . , i = k test is not significant, no follow-up F 20. Then, the standardized means of = erences at 5 and 1 % level of significance n ff (number of indicators), herein m 3339 3340 1, 2, . . . , (number of samples). = n i test for paired di t is the characteristic vector based on standardized data matrix; is the mean of original value for each indicator; ki i 1, 2, . . . , A x is the standardized value for each indicator; = cient, and principal component analysis). The mean values were j 0 ij ffi (number of samples), herein x is the variance contribution rate for each principal component, i.e. the n test) of 4 RD classes. If the ANOVA k , where 0 F ij x matrix V AR × , where i cients are also named characteristic vectors. Although the number of principal ki is the standardized value of evaluating indicators; ffi 1, 2, . . . , A /S ) 1 tests should be used. All statistical analyses were performed using SPSS Statistics = 0 i ij m = P i components is equaldependent indicators to maybe exist), all that principalother. components Generally, of are first not indicators, correlated severalof to principal each unlikely the components samples. the can Selectingprincipal represent rule original component major for indicators is principal information principal bigger components (some components than was: is more (a) 1; than eigenvalue and 85 %. of (b) each cumulative2.6.3 variance proportion Calculation of of principal all component scores Principal component scores of all samples were obtained using the equation: j 19 indicators for 20 plots were used to compute2.6.2 the correlation matrix. Identification of principal components Principal component is a linearcoe combination of all original indicators, and their loading x for each indicator; x after one-way analysisvariance of ( variance (ANOVA) are conducted to test homogeneity of standardization can be done facilely in SPSS, which using the equation: deviation for each indicator; indicators); and 2.6.1 Standardization of original variablesData and should computation be2003) of standardized because correlation to some avoid of unexpected 19 influence selected appearing indicators (Liu are et on al., very di 2.6.4 Calculation of integrated fertility scores Integrated soil fertility scores were calculated using the equation: 2.4 Soil microbial biomass properties analyses Measurements of MBC, MBN, and(Brookes MBP et were al., tested 1985b; by chloroform-fumigation Wubacteria method et (BAC), al., 1990). fungi Theplating (FUN), density method (Bulluck of and Iii soil et actinomycetes microorganisms al., including (ACT) 2002). were2.5 measured by Statistical dilution analyses The studied variablessamples in were each plot, average analyzed withincorrelation the by same coe desertification descriptive level, standard-deviation, compared statistics using Student’s (i.e., average of 6 t (ver. 20, IBM, USA). 2.6 Procedure for evaluating soil fertility using PCA selected principal components according to the rule above); where percentage of the variancewhich for means each the proportion principal of component information out in of the the whole sum sample of information deriving all variances, 5 5 10 20 10 15 15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ” 6 erence ff (number water/air explained ” because n erent from 2 ff phosphorus 0.936. MBC, was called as = 4 r 1, 2, . . . , = ected 19 selected soil ” because it explained ff j of soil. The PC is the principal component kj was identified as the “ P potassium nutrient component 0.05). TOC, TN, MBC, and MBN 1 ” since it mainly covered features erence. Contrarily, the content of ff organic matter component “ p < IRD. There were significant di 2 > explained 31.1 % of the variance (Table 4). 1000 because the TOP was calculated from LRD 1 3342 3341 − > erent from those for LRD, MRD, and IRD, while water-holding capacity ff ” because it had positive loading from FUN (0.593). MRD microbial biomass component > PRD without obvious di > ) was bigger than 1, and their cumulative variance proportion erent extent (Table 2). The content of TOC, TN, MBC, MBN, TP, (number of principal components); and 6 ff p ” because it had positive loading from TP (0.572). The PC MRD –PC 1 > cient of BD vs. TOP is ffi erence between TP of PRD with that of IRD, between AP of PRD with ff explained 8.4 % of the variance and was defined as the “ was defined as the “ LRD 1, 2, . . . , 5 3 > = k erence between those values for LRD and MRD was not significant. There were ff The order by which the principal components are interpreted depends on the PCA was performed using the data matrix of standardized means for 19 indicators. After computing principal component scores, integrated soil fertility scores of 20 plots The PC The PC microbial communities component since it had positive loading from AK (0.613). were calculated (Fig. 1).higher than Fertility those level of of other sampling sites sites as LY1 expected, and but XH1 fertility for scores PRD of was LH1 and XS1 for It had highly(0.766) positive and loadings TOP (0.746). from In CMC a (0.838), rough sense, FMC the (0.821), PC TN (0.779), CEC “ nutrient component explained 5.4 % of the variance and was referred to “ magnitude of their eigenvalues. The PC related to water and air permeability was 83.8 %,components a could represent litter the total less information of than original variables. 85 % (Table 4). Taken altogether, first 6 principal permeability and water-holding capacity component all these indicators were significantly correlated to TOC (Table 3). 10.9 % of the(0.512) variance and with MBN positive (0.508). loadings Explaining from 9.0 MBP % (0.592), of ACT the (0.515), variance, CAP the PC Although there were severalgrouped highly into 19 dependent independent indicators, principalcomponents components. all Each (PC original eigenvalue of indicators first 6 were principal correlation coe BD data. Thus, we couldstudy. remove TOP and TN from dataset of measurements in future 19.0 % of theTOC variance (0.610), and with MBP highly (0.574). positive We named loadings PC from AP (0.743), MBC (0.679), from original indicators toscore; each principal component; of samples). 3 Results 3.1 Variation of soil fertility indicatorsUsing with succession one-way of ANOVA, RD statisticalperformed. comparison The among results indicated thefertility that indicators measured to the indicators di succession was of RD a and AP decreased with the aggravation of RDthe di ( significant di that of LRD orMBP, BAC, IRD, and and ACT between was: AK PRD of PRD and that of IRD. The changing trend of values for PRD were significantly di TK, CEC, pH, FUN,succession of CMC, RD. FMC, CAP, and TOP were3.2 not significantly di Evaluation of soil fertility usingThe PCA correlation matrix forof 19 20 indicators plots were using calculatedcorrelation with SPSS with the (Table 3). each standardized TOC, means other, TN, and and TOC TP highly showed correlated significant and to positive TN with between MBP for MRDwas: and those IRD for PRD, LRD, and IRD. The changing trend of BD MBN, and MBP alsoand significantly FMC were and correlated positivelyand to correlated TP, ACT AP, to TK, nearly each CEC, showed and other. no BD. Both However, correlation CMC pH, with AK, BAC, other FUN, indicators. It was notable that the 5 5 25 10 20 15 10 20 15 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | → erent ” and ff erent RD IRD. The ff > 0.416) were not 0.622), PRD vs. LRD = = > orestation to avoid p p ff MRD > ects the productivity of land. In ff erence of soil fertility also caused by ff erent sampling sites and with di grass) results in homogenized community ff 3344 3343 → 0.692), and MRD vs. IRD ( = erent from one region to another (Clemens et al., ected strongly by the aggravation of RD. Thus, the ff ff p ”, so water/air permeability, water-holding capacity, and shrub/grass erence between fertility scores of LRD and those of IRD was ects above eventually lead to integrated soil fertility decreasing ff ff → erent RD grades. causes great loss of N, P,and K nutrients (Peng and Wang, 2012). ff water/air permeability and water-holding capacity component ff 0.023). However, fertility scores of PRD vs. LRD ( erent. shrub ff = p → 0.160), LRD vs. MRD ( = ects of succession of RD on soil fertility ff p 0.008), and the di 0.01), were significantly correlated to TK, AK, FUN, FMC, and TOP, but were erence between fertility scores of PRD and those of IRD was very significant = To facilitate comparison, the means of integrated fertility scores were calculated ff organic matter component p p < structure, decrease of biomass and litter fall, and reduction of plant nutrition such as soil MRD ( return, land useThe type aggravation and of frequencybut RD influence is also the not soil bytree/shrub only quality climate (Ozgoz caused (S. et by Li al., anthropogenic 2013). et factor (land al., overuse), 2009). Degradation of phytocommunity (tree Soil fertility, as the basis of soil quality, directly a significantly di 3.3 Correlation of integrated fertility scoresWe with analyzed evaluating indicators the correlationindicators (Table of 5). integrated The results soilstrongly demonstrated and fertility that significantly scores the correlated integrated with( to fertility the TOC, scores TN, 19 were TP,insignificantly CEC, evaluating correlated MBC, to MBN, pH, MBP AP, BAC, and ACT, CMC, BD and CAP. 4 Discussions 4.1 E grades. Soil fertility levels wereaverage not fertility always consistent of with MRD RD wasto: grades, greater for (i) instance, than the the that classification ofsoil LRD method fertility (Fig. of could 2). RD not Thisexposure is might be and be not only ascribed vegetation explored so fromvegetation type. satisfactory covers soil are as For less depth, expected. some than vegetation those Thein karst of coverage, actual LRD, a bedrock areas their surface low-lying (MRD fertile zone soilRD or might when accumulate grade IRD), eroded would although by rainfall have their chronically, greater hence fertility, some (ii) soil di with higher anthropogenic interference. Most of IRDagricultural regions production became abandoned due land to without seriously any degrading soil fertility. In contrast, both LRD with the aggravation of RD. 4.2 Discordance between soil fertility levelSoil and fertility RD grade fluctuated remarkably with di regional variation. Localanthropogenic climate, intervention soil-forming were conditions, di and2010). Soil the fertility way in andfor one LRD. region extent When for of we MRD investigated mighthad on be better stands, greater we than vegetation that found that in because another the they region majority of had PRD been regions enclosed for a In one word, multiple a “ organic matter content would beaggravation of a RD leads to soilworsening, hardening, bulk and density water-holding enlarging, ability water/air of permeability strong surface surface soil runo decreasing would happen, then the organic matter, total N andpopulation so reducing on. and The microbial alteredand soil degradation P ecosystem of retentions leads litter towere in fall microorganism identified soil decreasing, as decrease “ so (Lu that et C, al., N, 2014). First two components (Table 4) sampling sites for di (Fig. 2). The sequencing of the mean scores was PRD di ( significant ( PRD were lower thanLH3, fertility XH3 scores and of XS3than LH2 for those of MRD. and other Fertility LY2 sites. In for scores summary, LRD, of integrated fertility and LY4 scores and fertility fluctuated SD4 with scores di for of IRD were far lower 5 5 15 20 25 10 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 0.01) (Table 5). But TN was p < 3346 3345 ects on soil physiochemical properties, as well as ff 0.936 (Table 3). Thus, we can put forward that TOC, = r Furthermore, TOC, TN, TP, CEC, MBC, MBN, MBP, and BD were strongly and Soil organic matter (used interchangeably with TOC), as the major source of several TP, CEC, MBC, MBN, MBP,estimate and soil BD fertility might inevaluating be RD indicators reasonable region. for RD. and They could sensitive be indicators included to in the minimum dataset of significantly correlated to the integrated soil fertility ( the loosen extent andand permeability activity of soil. of MBC, soilonly MBN, microorganism, have and and a MBP, reflects the littleto the status content number TN of in (2.16 soil soil %),was with environment, and although the reported MBP mean they that ratios toand of the TP fertility MBC microbial (0.95 to (Pascual %)properties activity TOC et in are (0.61 directly %), more al., this MBN suitable influences study 1997).quality than soil physical (extracting and Soil and/or soil from ecosystem biochemical, chemical degradation Table propertiesthat stability microbiological (Paz-Ferreiro 2). a to and and estimate good It Fu, soil level biological 2013). ofPaz And Jimenez microbiological et it activity al., is is 2002;microbial widely crucial Pascual turnover for recognized et is maintaining al., a soil 2000; drivingplant quality Visser force nutrients (de and for la in Parkinson, transformation 1992), and soilschanges because cycling (Chen in of and MBC organic He, matter is(García-Orenes 2002; to a Fontaine et sensitive et index al., al., ofmicrobial 2010; 2003). changes population Powlson For in instance, et size the the content1984). al., to The of 1987), evaluate strong soil natural and and organic positivethat and matter it correlation MBC between degraded is was MBC systems useful and aet (Soulas TOC for sensitive (Table al., 3) et determining index indicated to 2012). al., mineralization indicate Inorganic of the organic N dynamics matter of2011; and in Ros soil et soil P al., organic 2011). microbial needed carbon Theof degradation changes (Liu soil by in system MBN fertility (Hopkins and vegetation (Powlson MBP etbefore can are et getting al., also al., a indicate mainly the minimum 1987). fluctuation dataset. obtained Thus, these from indicators deserve pre-researching highly correlated to TOC with soil’s capacity to provide regulatoryto ecosystem the services. primary N, P, macronutrients andfertility K in are and soil often nutrient for referred retention plants’ capacity. growth. BD, CEC as is an used indicator as of a soil measure compaction, of reflects nutrients, exerts numerous positive e been chosen to assess thea soil minimum quality. On dataset the forand basis evaluating reduce of soil evaluating scientifically cost. fertility reliability, defining can cut down the number of indicators 4.3 Sensitive indicators to evaluate soilSelecting fertility in appropriate RD lands indicatorsGenerally, evaluating will indicators are guarantee chosen empiricallyof the based predecessors. on Some accuracy the physiochemical researching of (OzgozFerreiro fruits et and evaluating al., Fu, results. 2013), 2013), microbial biomass and (Paz- enzymatic activity properties (Pajares et al., 2011) had and MRD regions withburning moderate had fertility caused were degradationled not to of strictly residue tree/shrub mineralization, protected. recycling to Perhapswere of shrub/grass residue faeces, usually and or utilized incrementing animal soil toanthropogenic nutrients. grazing interference cultivate They to had timber LRD or forestsactivity MRD or certainly is non-wood reached one the forests. highest of Asand level. key Human RD a driving grade result, factors varies the activities of among and RD land taking (Y. use measurements B.plantation such types Li might as (Li et be mountain et definitely al., closure, al.,be important forest 2009; 2006). learned reservation to Xiong Thus, from and control et reducing expanding naturalPlateau al., of human vegetation 2009), (G. RD rehabilitation area, Zhao to whichenvironment control et improves could soil soil al., fertility. erosion With gradual 2013a; onand deterioration of X. the microorganism soil Loess at Zhao fertility, soil some animals etby stage disintegrating al., tissue (MRD) and 2013), increase fixing the(Barot the (iii) nutrients et speed to al., self-organization acclimate of 2007). the of Thus, litter degrading the soil fall environment fertility breakdown of MRD soil is likely greater than that of LRD soil. 5 5 25 10 15 20 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent extent, ff erence of soil fertility ff ect of organic matter: a question of ff ects of irrigation and cultivation on the quality of 3348 3347 ff ect of grazing on soil and water losses under arid and ff ects of fumigation time and temperature, Soil Biol. Biochem., ff ected evaluating indicators of soil fertility to di ff This research was supported by National Department Public Benefit erent agricultural management systems in a semiarid Mediterranean agroecosystem, Soil ected by land use history, relief position, and parent material under a tropical climate in ff ff Mediterranean climates. Implications for desertification, Pirineos, 153, 159–174, 1999. China, Commun. Soil Sci. Plan., 33, 2101–2117, 2002. a NW-Vietnam, Catena, 81, 87–96, 2010. based on microbiological and biochemical parameters, Biol. Fert. Soils,model 35, in 302–306, karst 2002. peak-cluster depressionSciences, in 10, Guohua, 2449–2452, Guangxi, 2011. China, Procedia Environmental desert soil in central Iran, Land Degrad. Dev., 23, 53–61,microbial 2012. competition?, Soil Biol. Biochem., 35, 837–843, 2003. network: progress and perspectives, Ecol. Complex., 7, 225–233, 2010. Zornoza, R., Bárcenas,di G., and Caravaca,Till. 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H.: B.: The Organic e and synthetic 5 Conclusions The succession of RD a Longhui, Shaodong, Xinhua, andWe Xinshao also counties acknowledge of the Hunan anonymous reviewers for for providing the the valuable sampling comments. sites. References Bai, X. Y., Wang, S. J., and Xiong, K. N.: AssessingBarot, spatial–temporal S., Rossi, evolution J. processes P., and of Lavelle, P.: Self-organization in a simpleBrookes, consumer-resource P.C., system, Kragt, J. F., Powlson, D. S., and Jenkinson, D. S.: Chloroform fumigation and the Acknowledgements. Research Foundation of State ForestryScience Administration and of Technology China Project (201104016)funded of and by Hunan the the Province, China Planned Postdoctoral Chinaher Science (2013RS4035), critical Foundation and (2013M531787). revision We was thank of partially Veronika the for manuscript. We are grateful to the Forestry Bureau of Lianyuan, into account in the future research. but the degradationRD. trend Soil chemical of indicators soilMBP, TOC, and TP fertility physical and was CEC, indicatorin almost microbial BD indicators RD parallel might MBC, to regions MBN bethe and the the according integrated key aggravation to soil indicators of soil fertility. their to depth Perhaps and paired evaluate the the soil correlations methodvegetation landscape fertility type) indicators of and could (vegetation classifying significant coverage, be bedrock RD improved correlation exposure, only after and to taking according the to regional di 5 5 25 10 15 20 30 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ect of vegetation types on chemical ff and, E.: Nitrogen mineralization: a review and ffl 3350 3349 ects in a tipic Haplustoll, Land Degrad. 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J., karst Gao, H., rocky P., Zhang, desertification and J., in and Yin, Jin, J.: W.: Determining minimum data set for soil quality 5 5 30 20 25 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 4.56c 2.86c 2.99c 3.84a 2.84a 0.11c 0.41c 0.03a 0.02a 0.03a 0.08a 0.03a 0.92a 0.17a 2.29a 0.28a 19.66b 0.14b 0.10a ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± erent in ANOVA ff 30 LH1, LY1, SD1, XH1, XS1 < 8.66a53.51b 24.72 43.74 4.05b 23.48 8.09a 11.83 1.42b 16.86 0.04a0.04a 0.45 0.48 0.09a 0.25 0.12a0.05a 1.39 0.33 0.80b1.39a 3.07 1.25b 0.46 4.99a 2.09 0.44 0.64a 6.16 0.15b 1.41 47.34ab 64.51 0.45ab 0.19 0.06ab 0.43 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 70 /% /% > 7.31a48.73b 24.02 103.45 5.28a 12.33 7.97b 19.10 0.06a0.05a 0.48 0.51 0.09a 0.27 0.14a0.09a 1.29 0.38 0.65a0.97b1.70a 4.22 1.88b 1.22 1.79 3.30 18.78b 34.03 0.90b 0.60 1.09a0.87b 6.55 1.77 31.83ab 89.25 0.21ab 0.39 40 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± /cm coverage exposure > 3354 3353 0.05. ≤ p 31.03a 160.58 9.95a 24.87 4.30a 25.32 0.09a0.07a 0.46 0.50 0.08a 0.28 0.04a 0.36 0.18a 1.33 1.57a2.03a 0.92 1.49 1.41a 3.34 27.98a 53.80 22.13a 85.98 4.52a 10.90 0.49a 1.12 0.05a 0.45 1.26a0.40a 6.18 2.31 14.64a 2.05 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± erence (LSD) at ff ) 2.61 ) 1.41 ) 7.37 1 1 1 − − − ) 27.12 1 )) 230.87 64.41 ) 6.95 − 1 1 1 ) 95.60 ) 1.37 − − ) 27.50 − CFU g CFU g CFU g 1 1 ) 0.26 ) 1.26 1 3 3 ) 2.64 3 ) 8.67 ) 0.58 − − 1 3 − 1 1 1 − − − − − 10 10 10 SD within each column, for each indicator, followed by the same letter are not significantly di ects of succession of rocky desertification on soil quality indicators. × × × ff ± E Classification of rocky desertification and basic information of plots. test, by least significant di CAP (%)TOP (%) 0.42 0.52 CMC (%)FMC (gg 0.33 BAC ( FUN ( ACT ( BD (gcm CEC (cmol kg MBC (mgkg MBN (mgkg MBP (mgkg AK (mgkg TK (gkg AP (mgkg TOC (gkg TN (gkg TP (gkg Test itemspH PRD 5.72 LRD MRD IRD TOC, total organic carbon; TN,available total potassium; nitrogen; CEC, TP, cation total exchange phosphorus;MBP, capacity; microbial AP, available MBC, mass phosphorus; microbial phosphorous; TK, biomass BAC, total bacteria; carbon;moisture potassium; MBN, FUN, capacity; AK, microbial fungi; FMC, field biomass ACT, actinomycetes; moisture nitrogen; Means BD, capacity; bulk CAP, density; capillary CMC, porosity; capillary TOP, total porosity. F LRDMRD tree, shrubIRD shrub timber stands, non-wood forests grass non-wood forest, abandoned 30–40 land abandoned land 20–29 50–70 30–49 30–39 LH2, LY2, 40–49 SD2, XH2, XS2 LH3, LY3, SD3, XH3, XS3 10–19 20–29 50–69 LH4, LY4, SD4, XH4, XS4 PRD tree forest conversation Grade Vegetation Utilization Soil depth Vegetation Bedrock Serial no. of plots PRD, LRD, MRD, and IRD are potential, light, moderate, and intensive rocky desertification respectively. Table 2. Table 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1 0.108 − c b 6 1 –A 0.681 0.466 6 0.039 0.007 0.134 0.613 0.071 0.271 0.171 0.186 c c b − − − − − − − 1 PC 0.861 0.522 c b b 5 . a –A 1.000 0.522 0.466 − 5 − − 0.369 0.422 0.572 0.312 0.388 0.066 0.265 0.291 0.044 0.6880.162 0.115 0.058 0.201 0.058 0.070 − − − − − − − − PC 0.061 0.282 0.0110.096 0.107 0.787 − − − − 4 –A 0.113 0.147 0.033 0.131 4 − − − − 0.283 0.302 0.212 0.408 0.151 0.163 0.520 0.104 0.3140.212 0.014 0.110 0.593 0.370 0.380 0.069 0.186 0.044 0.180 0.129 − − − − − − − − PC 0.240 1 0.432 0.131 0.096 1 0.059 0.380 1 − − 3 c –A 3 0.126 0.035 0.361 0.020 0.402 0.096 0.135 0.096 0.051 0.090 0.347 0.043 0.238 0.289 0.311 0.217 0.143 0.318 0.508 0.036 0.093 0.515 0.512 0.369 0.380 − − − − − − − − − − − − PC 1 0.062 0.602 0.054 0.218 0.182 0.439 1 − − − 2 c c –A 2 0.064 0.314 0.125 0.308 0.590 0.372 0.021 0.054 0.743 0.5200.055 0.033 0.261 0.322 0.181 0.074 0.679 0.309 0.574 0.592 0.012 0.298 0.369 0.3490.407 0.205 0.195 0.173 0.058 0.149 0.003 0.627 0.299 0.150 0.242 0.1920.094 1 0.379 0.099 0.442 0.314 0.182 0.126 0.433 − − − − − − − − − − − − PC c c c c b 3356 3355 0.100 0.277 0.361 0.664 1 − − − –A 1 0.655 0.610 0.779 0.628 0.264 0.766 0.523 0.500 0.3460.133 0.0620.746 0.396 0.446 0.838 0.821 0.746 0.052 0.310 0.0520.124 0.118 0.085 0.690 0.021 0.580 0.266 0.534 0.112 0.664 0.101 0.173 − − − − − − − − − PC c b b 1 0.106 0.056 0.0460.224 0.1750.029 1 0.023 0.005 0.573 0.233 0.577 0.526 0.474 − − − − − − c c b b 0.093 0.037 0.00 0.001 0.335 0.312 0.1650.113 1 0.641 0.613 0.476 0.455 − − − − − − − − − c b 1 0.0320.332 0.227 0.065 1 0.055 0.101 0.205 0.274 0.253 0.049 0.255 0.332 0.093 0.232 0.310 0.664 0.587 − − − − − b b c b b b b b 0.05 level. 0.01 level. 1 0.062 0.027 0.095 0.277 0.365 1 0.548 0.463 ≤ ≤ 0.678 0.536 0.514 0.548 − − − − p p c c c c b b 0.036 0.314 0.091 0.097 0.421 0.562 0.308 0.125 − − − − Principle components analysis. Correlation matrix of soil evaluating indicators for rocky desertification b pH TOC TN TP AP TK AK CEC MBC MBN MBP BAC FUN ACT BD CMC FMC CAP 0.049 0.555 0.158 1 0.036 0.530 0.233 0.090 0.188 0.678 0.317 0.2170.379 0.104 0.100 0.562 0.458 − − − − − − − − − ItemspH Characteristic vector of principal component 0.229 TOC TN TP AP TK 0.470 AK 0.216 CEC MBC 0.395 MBN MBP 0.106 BAC FUN ACT BD CMC FMC CAP 0.436 TOP EigenvalueVariance contribution rate/%Cumulative variance proportion/% 31.131 31.131 50.070 18.939 61.008 10.938 69.967 78.391 8.959 5.915 83.761 8.424 3.598 5.370 2.078 1.702 1.601 1.020 The standardized means of 20Significant plots (two-tailed) were at used to compute the correlation matrix. Significant (two-tailed) at BD 0.100 TP AP TN 0.097TK 0.936 0.357 0.009MBN 0.116 0.406 ACT CMC 0.263 0.302 0.448 TOC AK 0.032 CECMBC 0.285MBP 0.375BAC 0.514 FUN 0.348 0.129 0.254 0.026 FMC 0.278 0.236 0.404 0.447 CAP 0.364 TOP Table 4. a b c Table 3. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | b 0.480 b a 0.503 b 0.077 0.679 0.764 a a 0.179 ) with soil indicators. F a 0.449 0.528 b 3358 3357 0.105 0.145 0.679 − − b 0.091 0.465 − a b 0.295 0.571 0.01 level. 0.05 level. − ≤ ≤ p p b 0.424 b Integrated soil fertility scores of 20 studied plots. LH, LY, SD, XH, and XS are the Correlation analysis of integrated fertility scores ( pH TOC TN TP AP TK AK CEC MBC MBN MBP BAC FUN ACT BD CMC FMC CAP TOP 0.111 0.497 0.445 Significant (two-tailed) at Significant (two-tailed) at F F b a sampling plots standing forcentral Longhui, Hunan Lianyuan, province, Shaodong,potential, China, light, Xinhua, respectively. moderate, and and The Xinshao intensive green, rocky counties desertification, blue, at respectively. orange, and red bar refer to Figure 1. Table 5.

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 25 rocky

(LRD)=0.114, (LRD)=0.114, p intensive

0.120 compared = , and IRD (IRD) p analyzed analyzed as moderate ,

ht lig 0.347, and MRD , = (MRD) p potential difference were 3359

LRD Rocky desertification grade desertification Rocky 0.114, = Paired

(LRD) p PRD (IRD)=0.120 compared to compared (IRD)=0.120 PRD. p

5 4 3 2 1 0

-1 -2

Integrated soil fertility score ( score fertility soil Integrated ) F Average scores of integrated soil scores fertility studied plots integrated of 20 Average of Average scores of integrated soil fertility of 20 studied plots. PRD, LRD, MRD, and

2 erence were analyzed as ff (MRD)=0.347, and and (MRD)=0.347, IRD refer to potential,di light, moderate, and intensive rocky desertification, respectively. Paired Figure 2. to PRD.

p Figure PRD, LRD, MRD, and IRD refer to desertification, respectively.

7 8 9 6 2 3 4 5 1 13 14 10 11 12