J Cent, South Univ(2012)19: 2279-2285DoI:10.1007/s11771-012-1272-22 SpringerEffects of soil and water conservation and its interactions withsoll properties on soil productivityGUO Wang(郭旺)2, LI Zhong-wur(李忠武)2, SHEN Wei-ping(申衛(wèi)平), WANG Xiao-yan(王曉燕)ZENG Guang-ming(曾光明}2, Chen Xiao-lin(陳曉琳)2, ZHANG Xue(張雪)2,ZHANG Yue-nan(張越男)2, LIU Gui-ping(劉桂平)3, WANG Shu- guang(王曙光)31. College of Environmental Science and Engineering, Hunan University, Changsha 410082, China;2. Key Laboratory of Environmental Biology and Pollution Control of Ministry of Education(Hunan University),Changsha 410082. China3. Soil and Water Conservation Research Institute, Shaoyang 422002, China4. College of Civil Engineering and Architecture, Changsha University of Science and Technology,Changsha 410076, chinaC Central South University Press and Springer-Verlag Berlin Heidelberg 2012Abstract: The effects of soil and water conservation(SwC)on soil properties are well documented. However, definitive andquantitative information of SwC and its interactions with soil properties on soil productivity is lacking for hilly red soil region ofouthern China. Experiments were conducted in the hilly red soil region of southem China for seven years in three runoff plots, eachof which represented different SwC forest-grass measures. Principal component analysis and multiple regression techniques werused to relate the aboveground biomass (representing soil productivity) to soil properties. Based on the final regression equations,soil organic carbon content(Soc)is significantly correlated with soil productivity under the condition of forest-grass Y,C playwhereas pH value and cation exchange capacity(Cec)are the main factors for soil productivity without swC. Therefore, swC playsan important role in sequestering Soc and improving soil productivityKey words: hilly red soil region; soil erosion; soil organic carbon; soil productivity1 IntroductionSoil productivity is defined as the capacity of soil toproduce a certain yield of crops or plants given aHilly red soil regions, which have abundant water specificd system of management. Soil erosion has longand heat resourccs, have the potential for high been recognized as an important factor in reducing theproductivity potential and could be essential in resolving productivity of many soils. In fact, the extent to whichgrowing concerns on food sufficiency [1]. In recent years, soil productivity responds to erosion depends on severaleconomic forests have developed rapidly in southern variables, such as crop or plant types, soil propertiesChina. However, farmers tend only trees and overlook management practices, and climate characteristics [4]the soil in which these trees were planted. Shrubs and Over the last 50 years, a significant amount of researcheslitter are completely cleaned by the use of herbicides, havc bcen carried out to study the relationship betweenand the original vegetation can become seriously erosion and soil productivity [4-7], but the conclusionsdamaged. Soil and water erosion under trees have of these works are inconsistent. Some studies reportedbecome more prominent. While the forest coverage rate that soil productivity responds strongly to erosion, whileincreases, closer inspection shows that soil erosion by others reported a much weaker effect [4]. Several studieswatcr is evident [2]. Long-term soil and water erosion, in the hilly red soil region of southern China have beentogether with the inherent fragility of hilly ecosystems, carried out [3, 8-10]. These studies generally focused onhas caused severe degradation of red soils, the results of estimating differences in soil organic carbon (Soc) inphich include high acidity and low nutrient content in relation to land use, vegetation restoration, climate, andthe soils [3]. As a result, soil and water erosion has topography. Extensive research that seeks to understandbecome one of the greatest limiting factors of soil the response of soil productivity to soil erosion has alsoproductivity in the hilly red soil regions of southern been undertaken. Soil and water conservation (SwC)hasFoundation item: Project(40971170)supported by the National Natural Science Foundation of China; Project(NCET-09-330)supported by the Program forNew Century Excellent Talents in University of ChinaReceived date: 2011-06 20; Accepted date: 2011-10-18Correspondingauthor:liZhong-wu,ProfessorPhD;Tcl:+86-731-88640078;E-mail:lizw@hnu.edu.cn2280J. Cent. South Univ.(2012)19: 2279-2285been found to play an important role in soil nutrients on aThe experiment was conducted in the Institute ofslope. As early as the 1970s, Swc campaigns under the Soil and Water Conservation in Shaoyang City of Hunanumbrella of the World Food Programme were initiated to Province, China(111 22E, 27 03'N)(Fig. 1)over acombat severe soil degradation [11]. Since then, the period of seven years. The altitude in the area rangesimportance of SwC and its effects on runoff and from 231. 18 m to 276.63 m above sea level. The area issedimentation reduction have been demonstrated all over characterized by a typical subtropical monsoon climatethe world [11-14]. HESSEL and TENGE [14] used a with an annual mean air temperature of 171C and anprocess-based erosion model to simulate the effects of annual frost-free period of 278 d. The annual rainfallSwC measures on catchment-scale runoff and erosion here in each year is concentrated between April andand found that their effects differed from those at the September and averages 1 397 mm. The soils in the areapixel scale. The studies, however, focused only on Swc are characterized by quaternary red and yellow soils withmodeling and its impacts on runoff and sedimentora soil texture of sandy loam. Serious soil and waterreduction. Limited information is available on the effects erosion is also found in this areaof swC on soil organic carbon and soil productivity. It isAn erosion-productivity study was started in thetherefore of utmost importance to evaluate soil hilly red soil region of southern China in 2003. Threeproductivity under different SwC mechanisms so as to runoff plots were established in the study area. The slopeprovide useful information for soil erosion predictions gradient, length, and width of the plots were 15, 15 mand rational implementation of swC measures. The and 5 m, respectively. The plots were located parallellycesium-137(Cs)patterns were utilized to observe to each other and separated by 50 cm cement bricks. Atspatial variations in surface soil, with specific objectives the bottom of each plot, a trough was used to collectas follows: 1) To apply theCs technique to assess soil runoff and sediments from august 2008 to Februaryredistribution patterns under different SwC mechanisms; 2010. The sediment yield was 55.5 kg from plot L, 29 kg2) To investigate spatial variations in surface soil from plot II, and 19 kg from plot IIl. The coverproperties and aboveground biomass; 3)To assess the vegetation was made up of weeds (plot D), weeds andinfluence of SwC and its interactions with soil properties pines (plot D), and grass(plot III). Plot I was used foron soil productivity. To achieve these objectivescomparison, and plots ii and iii represent two differentand other soil properties, as well as the aboveground soil and water measuresbiomass of three different runoff slopes, are assessed. amanagement baseline for enhancing soil productivity 2.2 Soil sampling and vegetation measurementspotential was provided in this workAfter setting forest-grass measures, each runoff plotwas divided into three 5 m x 5 m sections; appropriate2 Materials and methodssections were designated as upslope, midslope, anddownslope. Three replicate cores(1 m x 1 m) per section2.1 Description of study sitealong a diagonal transect were set up. Soil sampling inW「 Hunantudy siteProvinceShaoyangDistrict/100kmFig. 1 Location of study siteJ.Cent. South univ.(2012)19:2279-22852281each runoff plot was carried out in February 2010. Soil inferred from the distribution of"Cs inventories on thesamples were collected 0 to 20 cm below the soil surface, sleplot I,Cs inventories were found toand three to five sets of samples were collected trom increase from the upgcach core. After determining their bulk density (Ba), soil lowest at the upslope and highest at the downslope; thesamples were air-dried, crushed, and passed through a 13 Cs inventory of the latter was 2. 47 times higher than2 mm sieve. Coarse materials, such as gravel and roots, that of the former( Fig. 2). In plot Il, however,13cswere removed. Samples of the <2 mm fraction were inventories decreased from the upslope to downslopeweighted and analyzed for soil texture, pH, Cec, Soc, total showing a 39% decrease at the downslope comparednitrogen(Tn), available nitrogen(An), total phosphorus with that at the upslope. In plot Ill, Cs inventories(Tp),available phosphorus (Ap), total potassium(T), were also found to decrease from upslope to downslope,available potassium (Ak), and Cs inventory. The ranging from a low value of 263.7 Bq/m at theanalyses were carried out according to the procedures in downslope to a high value of 779.6 Bq/m at the upslopea previous study [15]. After surface soil sampling(0From the distribution ofCs inventories soil loss could20 cm), aboveground biomass samples of herb were be concluded to occur at the upslope and soilcollected from each core. Vegetation in every core was accumulations at the downslope with no Swc measuressampled destructively. Vegetation samples were oven- (plot I). This pattern of soil redistribution, characterizeddried for 24 h at 70C and weighed to the nearest.01g by soil loss from the upslope and deposition at the[l6]downslope, is typical of erosion in abandoned slopest. little soil lothe2.3 Data statisticsupslope to the downslope occurred under the conditionThe total Cs inventory(Bq/m) of topsoil (0- of forest-grass measures. This result is also consistent20 cm) is calculated bywith that of the studies by herWEG and LIDE [21] andDURAN et al [22], which suggest that the effect of soilC=∑CBD1×10and water forest-grass measures on soil loss is evidentwhere i is the number of sampling depth and n is the totalThis estimation is consistent with the sediment yieldnumber of samples with detectable 37Cs, C; is the 3Cscollected from different plots(55. 5 kg from plot 1, 29 kgactivity of sample i (Bq/kg Bai is the bulk densitfrom plot il, and 19 kg from plot III). Some sedimentg/cm")at depth i, and D, is the depth(m of sampleloss from piots ll and iii was observed, mainly becausethe fine particle -sized fractions were selectively movedThree replicates were used for each analysis, anddata are presented as the mean values of triplicatesout of the slope by water erosion [23Correlations between parameters were calculated bGiven the high variability of soil properties, our firstPearson correlation coefficient(SPSS 13.0 software). approach was to identify whether or not there existed anyPrincipal component analysis (PCa) and multiplestatistical relationship between soil properties(Table 2)regression techniques were run on these data using SPssPearson correlation coefficients indicate some ge13.0 software. In PCA, factors with cumulative samplerelationships. Significant correlations were observedvariances of 80% or above were selected. Factor rotationbetween soil properties and aboveground biomass, aswas done using the varimax method. Soil properties withwell as between soil properties. Aboveground biomassfactor loadings >0.50 were included in each factor. When was positively correlated to Cec and negatively correlatedmore than one properties were selected within a factor,to Tk and Soc: Among soil physical and chemicaltheir correlation coefficients were taken into account for properties, 26 significant correlations were observed.the selection. Among the correlated soil properties atStrong positive correlations were observed among SocP<0.05, the soil property with the highcst factor loading Anly, correlations between pH andwas selected for further consideration [17]. To gainTp, Soc, Bas and Tp: Cec and ap, Tp, and Si; and An and Tofurther insights into how these factors individually affect Ip> Soc and Sa were also positive and highly significant.aboveground biomass, the selected soil properties wereHere, Ba is bulk density; Cec is cation exchange capacityregressed against the aboveground biomassAthe available nitrogen; An is the availablephosphorus; Ak is the available potassium; In is the total3 Results and discussionphosphorus; Tk is the total potassium; Soc is soil organiccarbon;Sa means sand; Sc means silt; Cy means clay3. 1 Soil propertiesThese correlations reflect multicollinearity among soilThe physical and chemical propeof soil(Table physical and che1)exhibited high variability with coefficients of interpretation of multiple regression equations betweenvariation(CV)ranging from 2.27% for pH to 130.41% for aboveground biomass and soil propcrties unreliable [24]available phosphorus(A ) The soil redistribution can be This high multicollinearity of the soil properties indicates2282J.Cent. South univ.(2012)19:2279-2285Table 1 Descriptive statistics of distribution of soil physical and chemical propertiesParameterRaMean Standard deviation Coefficient of variation/% Skew Kurtosis4.3-4.84.520.0365Ba/g cm1.39-1.721.59383-0.462Cs(cmol(+)kg)960-14.1011.559.040.1500.068A,/(mg. kg)3900-830058.3011.0218.900.0260.570A /(mg.kg)0.8060.20l142130.410.180A /(mg.kg2600-1100054.7819.8136.160.6260.087In/(gkg0.62-1.100.770.1316.550.8390.280T (gkg)0.24-1.260.380.1950.412.383TK/ kg)8.40-12.6010.671.1410.690.3640.513Soc(gkg.74-11.608.291.5618.870.0401012w(Sa)/%39.38452741.791.493,570.7190.079w(Sty%17.37-24772.059740.3630.6532.256.060.601-04218.44-12.5410.739710.0030.578900Ea Upslope0 and 0.41 kg/m than that at the upslope and8001 AN Midslormidslope, respectively. The pine height measured when700opethe aboveground biomass was obtained, was on average600highest at the downslope (200 cm), followed by theupslope(76 cm) and the midslope(28 cm). However, the500variation degrees of the aboveground biomass on5400different slopes were different. In the wasteland (plot D)the aboveground biomass ranged from 16.00to20034.21 g/m. The variation degree in plot I was thehighest, followed by that in pine(plot In) and ryegrass100(plot ir)Plot IPlot miPlot illLandscape position3.3 Impact of soil properties on aboveground biomassFig. 2 Distribution of13'cs inventory along transects of slopesCorrelation analysis results on soil properties andsoil productivity indicate a high degrethat no single soil variable yield results in decreased intercorrelation among the soil properties. The highaboveground biomass [25]multicollinearity of the soil properties indicates that noingle soil variable directly impacts soil productivity;3.2 Aboveground biomasshowever, it is a combination of soil properties that resultBecause of different soil physical and chemical in decreased soil productivity. Because it is important toconditions and the presence of a variety of plants on the determine the combinations of variables that best explainslope under conditions of different forest-grass measures, the variations in soil productivity, the data werethe growth of plant and aboveground biomass on each subjected to PCalope presented different statues. In general, plant growthThe factors were rotated using the varimax methodand aboveground biomass were best for depositional and the soil data in three different plots were analyzedsites because of more-favorable soil properties and separately. Based on PCA, the soil properties in plot Imoisture conditions compared with upslope positions. In were reduced to four principal factors(Table 3). The firstthis work, the aboveground biomass, similar to soil and the most important factor, which explain 40%of theproperties, also showed strong spatial variability. Thevariation, has high factor loading (0.50) for propertiesresults are similar for the aboveground biomass at such as pH, Ba, Ap, and Tp. Factor 2 has high loadingeach plot, that is, theSt, and Cy, and collectively explaiboveground biomass is higher at the downslope than atbout 17% of the variation. The highly weightedthe upslope and midslope(Fig 3). For instance, in plot I, variables under Factor 3 are Cec and Sa, both of whichthe aboveground biomass at the downslope was higher explain 14% of the variation Factor 4 includes Ak andJ. Cent South Univ (2012)19: 2279-22852283Table 2 Pearson correlation coefficients for soil properties and aboveground biomasspHBApH0.0510.2350.2850.532(**3730.2970.467(*)0.0510.0270.0330.2960.0670.2410.389(*)0.23500270.3470.427(*)0.1520.3490.551(*)An0.2850.0330.3470.3060.1780.821(*)0.482(*)0.532(*)0.2960.427(*)0.461(*)0.854(**)Ak0.3730.0670.1780.2220.1320.016T0.2970.2410.3490.821(**)0.461(*)0.1320.602(**)0.467(*)0.389(*)0.551(**0482(*)0.854(**)0.0160.602(**)0.2971460.0360.2090.0880.388(*)0.1560.1440.727(**)0.428(*)0.0350870(*)0.430(*)0.1890.2160.3330.428(*)0.1320.0740.3230.3700.2510.0700.448()0.1390.25600830.1410.3540.1850.0450.280.1390.l18SocT0.2830.0480.3230.0470.1050.3030.020-0.1260.2430.2700438()0.0410.1710.014-00270.027S。/TA-0.2970.388(*)0.890.2510.3540.2830.2430.1560.2160.0700.0480.2700.0260.14403330448(*)0.1850.3230438(*)An0.0360.727(*0.428(*)0.0910.3660.047Ap0.1080.428(*)0.1320.1390.0450.1050.171Ak0.2090.0350.0740.2560.2820.3030.014T0.1950.870(*)0.3230.1390.0200.0270.0880430(*)03700.1410.1180.1260.10l0.2450.564(*)0.675(**)0.1310.622(*)0.1010.2260.1780.3110.507(*)0.312(-)0.2260.2190464(*)0.0980.275S0.564(**)0.763(*)0.524(**)0.675(*)0.3110.464(*)0.763(**)0411(0.223SocT0.1310.507(*)0.0980.524(*)0411(*)0.215A0.622(**)0.312(*)0.2750.044-0.215* Correlation is significant at 0.01 level (2-tailed); *Correlation is significant at 0.05 level (2-tailed)140UpslopeSoc/Tn, which explains about 12% of the variationF120 &BMidslopeCorrelation coefficients among pH, Aps and lp uI■ DownslopeFactor 1 are strongly correlated Table 2). Tp is selectedas a representative from Factor 1 because it has thehighest factor loading of 0.952. Meanwhile, Ba and Cyare selected from Factor 1 because they are not日60correlated with others. Similarly, An and St are selected torepresent Factor 2, whereas pH, Ccc, An, Ak, and Soc/ln areselected from Factors 3 and 4. The final soil properties可20selected for plot I are Tp, Cy, S, pH, Cec, An, Ak, and0Plot llPlot iiiThe soil properties in plot II are loaded into threeLandscape positionfactors (Table 4). Factor 1 is made up of ph, Ba, Ak, TnFig 3 Effects of erosion on aboveground biomaTp, and TK, contributing 42% of the variability in soil2284J. Cent. South Univ. (2012)19: 2279-2285Table 3 Rotated factor pattern and loadings of soil properties in Table 5 Rotated factor pattern and loadings of soil properties inplot Iolot IIIParameterFactor 1 Factor 2 Factor 3 Factor 4ParameterFactor IFactor 2 Factor 3H0943-0.1840.042-0.2230.7200.3840.237B(gcm)-0.7980.3450.0390.041B/(gcm)0.3090.4760.538Ce(cmol(+)kg)0.2920.0160906Cec/(cmol(+) kg0.7310.4860.0800.096An(mgkg)014407480.4150.332An/(mgkg)0.1400.273Au(mg.kg)0.7290.6330.072A,(mg kg) 0.920.2890.1300.203A/ (mg.kg0.2330.068A(mgkg)0.1780.0720.1350.868T/g-kg0.30200450.666T/(gkg)-00580.73900.3430.7120.5880.337T(gkg0.9520.034-0.0620.117Tigg0.3390.9(060.030TK/(gkg)-0.164078200820.195Soc/gkg)0.1380.3040.924(gk g)27800360.0610.80.14000320.3440.130.8370.1210.0290071-0.849(S/%0.19208380.3200,276(Cv/%0.7370.062672w(Cy)%o481.7570.3590.1860.3630.1460.2943500.0180.612Variance explained 5.89(42%)3.43(25%)2.22(16%)Variance5.63242Factor loadings in bold correspond to the indicators included in the MDSexplained(40%)(17%)(14%)(12%)and arc considered highly weightedTable 4 Rotated factor patterm and loadings of soil properties jnand Cy, explaining about 42% of the variation in the soilproductivity. Factor 2 consists of An, Ap, Ip, and Tkplot IIwhich explains about 25% of the variation. Factor 3 isParameterFactor 1 Factor 2 Factor 3made uBa, Tns Soc, S, and Cy. This factor explains 16%H0.9030.316of the variation in the soil productivity. The final soilBa/g cm0.8610.3370.166properties selected for plot III are Ap, Sa, Tp, Tk, Ba, St,Cec/(cmol(+kg)00.0700.964and sA/mgkg)0.0000.089Following PCA, multiple regression analysis wasused to gain further insights into how the selected soilAp/(mgkg)0.3890.3910.692properties individually affect aboveground biomass andAw/(mg.kg0.8760.186obtain the quantitative relationship between these soilT/(gkg)0.5460.476properties and aboveground biomass(characterized y)TM(gkg)0.93I0.1520.026Multiple regression analysis using the stepwiseTg.kg0.9570.1460.030elimination procedure was performed to identify thesmallest subset of soil properties for predictingSoc/gkg)0.I160.9760.00aboveground biomass. The following regression0.1740.1720.834equations are obtainedw(S)/%0.1810.9550.077Plot i:w(Cy)%o0.090-0.8860.385Y=1.9×1015-112(pH)+0.53(Cc)(R2=0.94)0.42l0.195Plot llVariance explained5.73(419)3.16(23%)2.27(16%)=1.34×10+1.05(S)+0.34(S)(R2=0.92)productivity. Factor 2 consists of Soc, St, Cy, and Soc/TnY0.187+0.55(7)+0.22(S0)(R2=094)(4)explaining about 23% of the variation factor 3 containsDifferent patterns emerge on the regression of theCec, Ap, and Sa, explaining about 16% of the variation. selected soil properties against the aboveground biomasThe final soil properties selected for plot II are Ak, Tk, Tp, in different plots. Aboveground biomass is significantlyoc]related to pH and Cec in plot L, St and Soc in plot Il, and TkThe soil properties in plot Ill are loaded into threeand Soc in plot III. The correlation coefficients for thefactors(Table 5). Factor 1 consists of pH, Cec, Ap, Tp, Sd, regression of the selected soil properties againstCent. South univ.(2012)19:2279-22852285aboveground biomass in plots I, Il, and Ill are 0.94, 0.928 WANG K, WANG H J, SHI X Z, WEINDORF D C, YU D S,and 0.94, respectively. It is interesting to note that Soc isLIANG Y, SHI D M. Landscape analysis of dynamic soil erosiongnificantly correlated with soil productivity under theSubtropical Chinacondition of forest-grass measures, whereas ph and cec [9 ZHANG Bin, YANG Yan-sheng, ZEPP H. Effect of vegetationare the main factors for soil productivity without swcrestoration on soil and water erosion and nutrient losses of a severelyTherefore, Swc plays an important role in sequesteringeroded clayey Plinthudult in southeastern China []. Catena, 2004,Soc and improving soil productivity57(1):77-90[10] ZHENG Hua, OUYANG Zhi-yun, XU Wei-hua. Variation of carbonConclusionsstorage by different reforestation types in the hilly red soil region ofsouthern China [j]. Forest Ecology and Management. 2008, 255(3/4)ll13-11211)Cs data indicate that soil erosion occurs on[11 HERWEG K, LUDI E. The performance of selected soil and waterslopes without any SwC measure applied to them,onservation measures-Case studies from Ethiopia and Eritrea [Jwhereas soil erosion phenomena do not occur on slopesCatena,1999,361/2):99114with forest-grass measures. Therefore, SwC forest-grass[12] LI Xiao-yan Gravel-sand mulch for soil and water conservation inthe semiarid loess region of northwest China [J]. Catena, 2003, 52(2)measures play an important role in preventing soil fromtransferring from upslope to downslope portions of hilly [13] WU Hui-long, FENG Zheng-yi Ecological engineering methods foregions.soil and water conservation in Taiwan [J]. Ecological Engineering,2) The combined effects of erosion and its2006,28(4):333-344interactions with soil properties on aboveground biomass14]HESSEL R, TENGE A. A pragmatic approach to modelling soil andwater conservation measures with a catchment scale erosion modelare confirmed. The nature of the influence of soil[ J. Catena,208,74(2):119-126properties on aboveground biomass differs among [15 Nanjing Soil Research Institute. Soil physical and chemical analy.different slopes. Based on the regression equations[M]. Shanghai: Shanghai Scicntific and Technical Publishers, 1978determined, Swc forest-grass measures play an [16] ALABACK P B Biomass regression equations for understory plantsimportant rolc in sequestering Soe and improving soilin coastal Alaska: Effects of species and sampling design onestimates [J]. Northwest Science, 1986, 60(2): 90-103productivity[17 JAGADAMMA S, LAL R, HOEFTR G NAFZIGER E, ADEEEA3)Because the relationship between erosion andNitrogen fertilization and cropping system impacts on soil propertiesaboveground biomass is complex, more data for differentand their relationship to crop yield in the ccntral Corn Bell, USA[]seasons and years are required to explore these trendsSoil and Tillage Research, 2008, 98(2): 120-129.fully[18] VAN OOST K, GOVERS G VAN MUYSEN W. A process-basedonversion model for caesium-137 derived erosion rates onReferencesgricultural land: an integrated spatial approach [] Earth SurfaceProcess es and Landforms, 2003, 28(2): 187-207[19] ARNAEZ J, LARREA V, ORTIGOSA L. Surface runoff and soil[1] JAVED I, HU Rong-gui, DU Li-jun, LI Lan, LIN Shan, CHEN Tao,erosion on unpaved forest roads from rainfall simulation testsRUAN Lei-lei. Differences in soil co, flux between different landnortheastern Spain []. Catena, 2004, 57(1 ): 1-14uses types in mid-subtropical China []. Soil bic[20] MALO DD, SCHUMACHER T E, DOOLITTLE JJ. Long-termBiochemistry,2008,40(9):2324-2333cultivation impacts on selected soil properties in the northern Great[2] ZHAO Qi-guo. Some considerations for present soil and waterPlains [J]. Soil and Tillage Research, 2005, 81(2 ): 277-291conservation and ecology security of south China []. Bulletin of Soil [21 HERWEG K, LIDE E. The performance of selected soil and waterand Water Conservation, 2006, 26(2):1-8.(in Chineseconservation measures-case studies from Ethiopia and Eritrea [J][3] ZHANG Ming-kui, XU Jian-ming. Restoration of surface soilCatena31999,36(1/2):99-114fertility of an eroded red soil in southern China [J]. Soil and Tillage[22] DURAN ZUAZO V H, RODRIGUEZ PLEGUEZUELO CRResearch,2005,80(l2):13-2ARROYO PANADERO L, MARTNEz RAYa A, FRANCLA[4] BAKKER MM, GOVERS G ROUNSEVELL M D A, The cropMARTINEZ J R, CARCELES RODRIUEZ B. Soil conservationproductivity-erosion relationship: An analysis based on experimentalmeasures in rainfed olive orchards in south-eastern Spain: Impacts ofwork[. Catena,2004,57(1):55-76plant strips on soil water dynamics [] Pedosphere, 2009, 19(4)[5] OZALP M, CONNER W H, LOCKABY B G Above-ground453-464productivity and litter decomposition in a tidal freshwater forested [23] SU Zheng-an, ZHANG Jian-hui, NIE Xiao-jun. Effect of soil erosionwetland on Bull Island, SC, USA [ J. Forest Ecology andon soil properties and crop yields on slopes in the Sichuan Basin,Management,2007,245(123):31-43China []. Pedosphere, 2010, 20(6): 736-746[6] BERNTSEN J, PETERSEN B M, OLESEN J E Simulating trends in [24] COX M S, GERARD P D, ABSHIRE M J Selected soil propcrticscrop yield and soil carbonlong-term experiment-Effects ovariability and their relationships with yield in three Mississippirising CO2, N deposition and improved cultivation [] Plant and soil,fields [J]. Soil Science, 2006, 171(7):541-5512006,287(1/2:235-24[25] SAWCHIK J, MALLARINO A P. Variability of soil properties, early[7] ZHAO Yun-chen, WANG Ping, LI Jian-long, CHEn Yu-ru, YINGhosphorus and potassium uptake, and incidence of pests and weedsXian-zhi, LIU Shu-ying. The effects of two organic manures on soilin relation to soybean grain yield [J]. Agronomy Journal, 2008,properties and crop yields on a temperate calcareous soil under a1005):14501462wheat-maize cropping system [] European Journal of Agronomy,(Edited by DENG Lui-xiang)2009,31(1):36-42