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Sciences in Cold and Arid Regions ›› 2020, Vol. 12 ›› Issue (4): 200-216.doi: 10.3724/SP.J.1226.2020.00217.

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Comparison of sampling schemes for spatial prediction of soil organic carbon in Northern China

XuYang Wang1,2,YuQiang Li1,2,3(),YuLin Li1,2,3,YinPing Chen4,Jie Lian1,2,WenJie Cao4   

  1. 1.Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    2.University of Chinese Academy of Sciences, Beijing 100049, China
    3.Naiman Desertification Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Tongliao, Inner Mongolia 028300, China
    4.School of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China
  • Received:2020-05-15 Accepted:2020-07-20 Online:2020-08-31 Published:2020-09-04
  • Contact: YuQiang Li E-mail:liyq@lzb.ac.cn

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Abstract:

Determining an optimal sample size is a key step in designing field surveys, and is particularly important for detecting the spatial pattern of highly variable properties such as soil organic carbon (SOC). Based on 550 soil sampling points in the near-surface layer (0 to 20 cm) in a representative region of northern China’s agro-pastoral ecotone, we studied effects of four interpolation methods such as ordinary kriging (OK), universal kriging (UK), inverse distance weighting (IDW) and radial basis function (RBF) and random subsampling (50, 100, 200, 300, 400, and 500) on the prediction accuracy of SOC estimation. When the Shannon's Diversity Index (SHDI) and Shannon's Evenness Index (SHEI) was 2.01 and 0.67, the OK method appeared to be a superior method, which had the smallest root mean square error (RMSE) and the mean error (ME) nearest to zero. On the contrary, the UK method performed poorly for the interpolation of SOC in the present study. The sample size of 200 had the most accurate prediction; 50 sampling points produced the worst prediction accuracy. Thus, we used 200 samples to estimate the study area's soil organic carbon density (SOCD) by the OK method. The total SOC storage to a depth of 20 cm in the study area was 117.94 Mt, and its mean SOCD was 2.40 kg/m2. The SOCD kg/(C?m2) of different land use types were in the following order: woodland (3.29) > grassland (2.35) > cropland (2.19) > sandy land (1.55).

Key words: soil organic carbon, sample size, geostatistics, kriging, prediction accuracy

Figure 1

Locations of the study area and sampling sites. The study area is located in northeastern China, and it belongs to China’s northern agro-pastoral ecotone. (a) Location of the 550 field sampling sites and land use patterns in the study area in 2015. The land use dataset was interpreted from Landsat 8 images at a scale of 1:100,000. (b) The study area includes five counties: Aohan, Wengniute, Balinyou, Balinzuo, and Ar Horqin"

Table 1

The area of each county in the study area and the corresponding sample size"

CountyArea (km2)Proportion of total areaSample size (No. of sites)Sample proportion
Ar Horqin12,76225.94%13925.27%
Balinzuo6,46013.13%6311.45%
Balinyou9,82619.97%10819.64%
Wengniute11,86124.11%12522.73%
Aohan8,28916.85%11520.91%
Total49,201100.00%550100.00%

Figure 2

Locations of soil sampling sites with random subsamples ranging in size from 50 to 500 sites. A total of 50 randomly selected sites were used for independent validation of the results with these subsamples"

Table 2

Descriptive statistics for soil organic carbon density (SOCD), bulk density (BD) and stoniness. N represents the sample size"

ParameterNRangeMinimumMaximumMeanStandard deviation
SOCD (kg/m2)55011.180.0511.242.241.48
BD (g/cm3)5500.850.781.631.310.14
Stoniness (vol%)306.530.196.721.971.32

Table 3

Descriptive statistics for soil organic carbon density (SOCD) to a depth of 20 cm under different sampling sizes. CV, coefficient of variation"

Sample sizeMinimumMaximumMean (kg/m2)SDSkewnessKurtosisCV
500.447.412.411.461.361.8360.79%
1000.447.412.371.371.181.1357.84%
2000.1110.122.381.581.704.1266.68%
3000.1110.122.351.541.563.5265.37%
4000.0510.122.291.491.453.1565.27%
5000.0510.122.211.431.563.7264.60%
5500.0511.242.241.481.825.5466.00%

Table 4

Semi-variance parameters of soil organic carbon density (SOCD) in different soil layers under different sample sizes"

Soil layer (cm)Sample sizeNuggetPartial sillSillNugget/sill ratioRange (km)
0-10500.080.200.2829.24%64.81
1000.010.240.242.12%42.38
2000.090.240.3427.96%27.96
3000.220.250.4647.18%74.44
4000.190.180.3751.16%14.14
5000.080.280.3621.83%12.86
10-20500.170.060.2274.05%165.91
1000.170.080.2568.12%34.98
2000.150.280.4334.49%77.15
3000.080.240.3224.34%16.32
4000.080.280.3621.54%13.49
5000.020.310.347.12%14.01
0-20500.070.130.2033.00%56.33
1000.080.140.2238.00%60.46
2000.070.230.3124.00%32.08
3000.100.190.2934.00%17.11
4000.110.220.3334.00%14.14
5000.040.270.3113.00%13.72

Figure 3

The mean error and root-mean-square error (RMSE) for SOC spatial distribution under different sampling sizes using different interpolation methods, including ordinary kriging (OK), universal kriging (UK), inverse distance weighting (IDW) and radial basis function (RBF). The dashed red line in (a) indicates y = 0"

Figure 4

The mean standardized error (MSE) and mean error (ME) for SOCD in comparison with the test (validation) dataset at different sample sizes. Negative values mean that the predicted value underestimated the actual value"

Figure 5

The root-mean-square error (RMSE) and root mean square standardized error (RMSSE) for SOCD compared with the test (validation) dataset at different sample sizes"

Figure 6

The relationship between measured and predicted soil organic carbon density (SOCD). The validation dataset was used to test the reliability of kriging-based SOCD estimates, which interpolated by different sample sizes (50, 100, 200, 300, 400 and 500)"

Figure 7

Spatial distribution of soil organic carbon density (SOCD) at different sample sizes using ordinary kriging interpolation"

Figure 8

The mean soil organic carbon density (SOCD) to a depth of 20 cm at different sample sizes for the five counties in the study area and for the study area as a whole"

Table 5

The soil organic carbon density (SOCD) and storage to a depth of 20 cm for the five counties in the study area. Min and Max represent minimum and maximum densities, and SD is the Standard Deviation"

CountyArea (km2)MinimumMaximumMean (kg/m2)SDStorage (Mt)
Ar Horqin12,7621.247.703.211.0441.02
Balinzuo6,4601.317.613.291.3221.28
Balinyou9,8290.927.092.410.9123.70
Wengniute11,8610.194.761.570.7318.59
Aohan8,2890.653.011.610.4113.35

Figure 9

Topography (elevation) of the study area in Northern China"

Table 6

The proportions of the main land use and cover types in each county"

CountyProportion of total area
CroplandWoodlandGrasslandSandy land
Ar Horqin24.10%19.32%41.75%7.52%
Balinzuo30.62%36.19%28.86%0.75%
Balinyou14.29%16.00%54.93%8.08%
Wengniute24.07%9.36%40.07%19.66%
Aohan47.36%16.47%27.09%1.83%

Table 7

The soil organic carbon density (SOCD) and storage to a depth of 20 cm among different land use types for each county and total study area"

CountyLand use/coverArea (km2)SOCD (kg/(C·m2))SOC storage (Mt·C)
Ar HorqinCropland3,1172.79 ± 0.748.70
Woodland2,4714.21 ± 1.1310.41
Grassland5,3103.09 ± 0.9116.43
Sandy land9372.91 ± 0.712.72
BalinzuoCropland2,0182.70 ± 0.855.45
Woodland2,3173.93 ± 1.409.11
Grassland1,8773.18 ± 1.245.97
Sandy land471.82 ± 0.330.09
BalinyouCropland1,3762.41 ± 0.663.32
Woodland1,5943.08 ± 1.204.92
Grassland5,3482.28 ± 0.8212.22
Sandy land7922.04 ± 0.541.62
WengniuteCropland2,8051.88 ± 0.525.26
Woodland1,0862.00 ± 0.652.17
Grassland4,7191.61 ± 0.767.58
Sandy land2,3540.86 ± 0.392.03
AohanCropland3,9261.59 ± 0.376.26
Woodland1,3331.70 ± 0.422.27
Grassland2,2631.61 ± 0.443.65
Sandy land1471.06 ± 0.220.16
Total study areaCropland13,2472.19 ± 0.8029.02
Woodland8,8223.29 ± 1.4829.02
Grassland19,5372.35 ± 1.0745.94
Sandy land4,2791.55 ± 0.996.61
Bhunia GS, Shit PK, Maiti R, 2016. Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC). Journal of the Saudi Society of Agricultural, 17(2): 114-126. DOI: 10.1016/j.jssas.2016.02.001.
doi: 10.1016/j.jssas.2016.02.001
Blake GR, Hartge H, 1986. Bulk Density. In: Madison. Methods of Soil Analysis, Part1: Physical and Mineralogical Methods. USA: American Society of Agronomy Press, pp. 365-375.
Boivin P, Gascuel OC, 1994. Variability of variograms and spatial estimates due to soil sampling: a case study. Geoderma, 62: 165-182. DOI: 10.1016/0016-7061(94)90034-5.
doi: 10.1016/0016-7061(94)90034-5
Bourennane H, King D, Couturier A, 2000. Comparison of kriging with external drift and simple linear regression for predicting soil horizon thickness with different sample densities. Geoderma, 97: 255-271. DOI: 10.1016/S0016-7061(00)00042-2.
doi: 10.1016/S0016-7061(00)00042-2
Bradford MA, Tordoff GM, Black HIJ, et al., 2007. Carbon dynamics in a model grassland with functionally different soil communities. Functional Ecology, 21: 690-697. DOI: 10. 1111/j.1365-2435.2007.01268.x.
doi: 10. 1111/j.1365-2435.2007.01268.x
Cambardella CA, 1994. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal, 58: 1501-1511. DOI: 10.2136/sssaj1994.03615995005800050033x.
doi: 10.2136/sssaj1994.03615995005800050033x
Chen T, Liu XM, Li X, et al., 2009. Heavy metal sources identification and sampling uncertainty analysis in a field-scale vegetable soil of Hangzhou, China. Environmental Pollution, 157: 1003-1010. DOI: 10.1016/j.envpol.2008. 10.011.
doi: 10.1016/j.envpol.2008. 10.011
Cools N, De Vos B, 2010. Sampling and Analysis of Soil. Manual Part X, In: Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests, United Nations Economic Commission for Europe (UNECE), ICP Forests, Hamburg.
Don A, Schumacher J, Scherer M, 2007. Spatial and vertical variation of soil carbon at two grassland sites-Implications for measuring soil carbon stocks. Geoderma, 141: 272-282. DOI: 10.1016/j.geoderma.2007.06.003.
doi: 10.1016/j.geoderma.2007.06.003
Fathian F, Aliyari H, Kahya E, et al., 2015. Temporal trends in precipitation using spatial techniques in GIS over Urmia Lake Basin, Iran. International Journal of Hydrology Science & Technology, 6 (1): 62-81. DOI: 10.1504/IJHST. 2016.073883.
doi: 10.1504/IJHST. 2016.073883
Gao P, Wang B, Geng GP, et al., 2013. Spatial distribution of soil organic carbon and total nitrogen based on GIS and geostatistics in a small watershed in a hilly area of northern China. Plos One, 8: e83592. DOI: 10.1371/journal.pone. 0083592.
doi: 10.1371/journal.pone. 0083592
Göl C, Bulut S, Bolat F, 2017. Comparison of different interpolation methods for spatial distribution of soil organic carbon and some soil properties in the Black Sea backward region of Turkey. Journal of African Earth Sciences, 134: 85-91. DOI: 10.1016/j.jafrearsci.2017.06.014.
doi: 10.1016/j.jafrearsci.2017.06.014
Heim A, Wehrli L, Eugster W, et al., 2009. Effects of sampling design on the probability to detect soil carbon stock changes at the Swiss CarboEurope site Lägeren. Geoderma, 149: 347-354. DOI: 10.1016/j.geoderma.2008.12.018.
doi: 10.1016/j.geoderma.2008.12.018
Houghton JET, Ding YH, Griggs DJ, 2001. IPCC2001. Climate Change 2001: the Scientific Basis. Cambridge University Press, Cambridge, U.K.. pp. 227-239.
Houghton RA, Woodwell GM, Sedjo RA, 1988. The global carbon cycle. American Scientist, 241: 1736-1738. DOI: 10. 1126/science.241.4874.1736-b.
doi: 10. 1126/science.241.4874.1736-b
Kerry R, Oliver MA, 2007. Comparing sampling needs for variograms of soil properties computed by the method of moments and residual maximum likelihood. Geoderma, 140: 383-396. DOI: 10.1016/j.geoderma.2007.04.019.
doi: 10.1016/j.geoderma.2007.04.019
Khalil MI, Kiely G, O'Brien P, et al., 2013. Organic carbon stocks in agricultural soils in Ireland using combined empirical and GIS approaches. Geoderma, 193-194: 222-235. DOI: 10.1016/j.geoderma.2012.10.005.
doi: 10.1016/j.geoderma.2012.10.005
Kumar S, Lal R, Liu D, 2012. A geographically weighted regression kriging approach for mapping soil organic carbon stock. Geoderma, 189-190: 627-634. DOI: 10.1016/j.geoderma.2012.05.022.
doi: 10.1016/j.geoderma.2012.05.022
Li D, Shao MA, 2014. Soil organic carbon and influencing factors in different landscapes in an arid region of northwestern China. Catena, 116: 95-104. DOI: 10.1016/j.catena. 2013.12.014.
doi: 10.1016/j.catena. 2013.12.014
Lal R, 2008. Carbon sequestration. Philos. Trans. R. Soc. London, Ser. B, 363: 815-830. DOI: 10.1098/rstb.2007.2185.
doi: 10.1098/rstb.2007.2185
Lal R, 2004. Soil carbon sequestration impacts on global climate change and food security. Science, 304: 1623-1627. DOI: 10.1126/science.1097396.
doi: 10.1126/science.1097396
Li FR, Zhao LY, Zhang H, et al., 2004. Wind erosion and airborne dust deposition in farmland during spring in the Horqin Sandy Land of eastern Inner Mongolia, China. Soil & Tillage Research, 75: 121-130. DOI: 10.1016/j.still. 2003.08.001.
doi: 10.1016/j.still. 2003.08.001
Li FR, Zhou ZY, Zhao LY,et al., 2007. Efficacy of exclosures in conserving local shrub biodiversity in xeric sandy grassland, Mongolia Inner, China. In: Sosebee RE, Wester DB, Britton CM, McArthur ED, Kitchen SG (eds.). Proceedings: Shrubland dynamics-fire and water. Fort Collins, CO: U.
S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, pp. 163-169.
Li Y, Zhou S, Wu CF, et al., 2007. Improved prediction and reduction of sampling density for soil salinity by different geostatistical methods. Agricultural Sciences in China, 6: 832-841. DOI: 10.1016/s1671-2927(07)60119-9. (in Chinese)
doi: 10.1016/s1671-2927(07)60119-9
Liao QL, Zhang XH, Li ZP, et al., 2009. Increase in soil organic carbon stock over the last two decades in China's Jiangsu Province. Global Change Biology, 15: 861-875. DOI: 10.1111/j.1365-2486.2008.01792.x.
doi: 10.1111/j.1365-2486.2008.01792.x
Liu X, Wu J, Xu J, 2006. Characterizing the risk assessment of heavy metals and sampling uncertainty analysis in paddy field by geostatistics and GIS. Environmental Pollution, 141: 257. DOI: 10.1016/j.envpol.2005.08.048.
doi: 10.1016/j.envpol.2005.08.048
Mai JS, Zhao TN, Zheng JK, et al., 2015. Spatial variability of surface soil nutrients in the landslide area of Beichuan County, Southwestern China, after5.12 Wenchuan Earthquake. Chinese Journal of Applied Ecology, 26: 3588-3594. DOI: Y2015/V26/I12/3588. (in Chinese)
doi: Y2015/V26/I12/3588
McBratney AB, Webster R, 1983. How many observations are needed for regional estimation of soil properties? Soil Science, 135: 177-183. DOI: 10.1097/00010694-198303000-00007.
doi: 10.1097/00010694-198303000-00007
Mishra U, Lal R, Liu D, et al., 2010. Predicting the spatial variation of the soil organic carbon pool at a regional scale. Soil Science Society of America Journal, 74: 906-914. DOI: 10.2136/sssaj2009.0158.
doi: 10.2136/sssaj2009.0158
Mueller T, 2003. Soil carbon maps: Enhancing spatial estimates with simple terrain attributes at multiple scales. Soil Science Society of America Journal, 67: 258-267. DOI: 10.2136/sssaj2003.0258.
doi: 10.2136/sssaj2003.0258
Neff JC, Townsend AR, Gleixner G, et al., 2002. Variable effects of nitrogen additions on the stability and turnover of soil carbon. Nature, 419: 915-917. DOI: 10.1038/nature01136.
doi: 10.1038/nature01136
Nelson DW, Sommers LE, 1982. Total carbon, organic carbon and organic matter. In: Page AL, Miller RH, Keeney DR (ed.). Methods of Soil Analysis, Part2, 2nd ed., American Society of Agronomy, Madison, WI, USA, pp. 539-577.
Nelson DW, 1996. Total carbon, organic carbon, and organic matter. Methods of Soil Analysis, 9: 961-1010. DOI: 10. 2136/sssabookser5.3.c34.
doi: 10. 2136/sssabookser5.3.c34
Zhang ZQ, Yu DS, Shi XZ, et al., 2015. Priority selection rating of sampling density and interpolation method for detecting the spatial variability of soil organic carbon in China. Environment Earth Sciences, 73: 2287-2297. DOI: 10.1007/s12665-014-3580-3.
doi: 10.1007/s12665-014-3580-3
Reza SK, Baruah U, Sarkar D, 2016. Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India. Arabian Journal of Geosciences, 9: 1-8. DOI: 10.1007/s12517-016-2474-y.
doi: 10.1007/s12517-016-2474-y
Rozpondek R, Wancisiewicz K, 2016. Distribution of pollution in sediments in the coastal zone of Ostrowy water reservoir in Biała Oksza river-GIS based approach. Environment Protection Engineering, 19: 37-49. DOI: 10.17512/ios. 2016.1.4.
doi: 10.17512/ios. 2016.1.4
Sahrawat KL, Rego TJ, Wani SP, et al., 2008. Stretching soil sampling to watershed: evaluation of soil‐test parameters in a semi‐arid tropical watershed. Commun, Communications in Soil Science & Plant Analysis, 39: 2950-2960. DOI: 10.1080/00103620802432857.
doi: 10.1080/00103620802432857
Sandeep K, 2015. Estimating spatial distribution of soil organic carbon for the Midwestern United States using historical database. Chemosphere, 127: 49-57. DOI: 10.1016/j.chemosphere.2014.12.027.
doi: 10.1016/j.chemosphere.2014.12.027
Schloeder CA, Zimmerman NE, Jacobs MJ, 2001. Comparison of methods for interpolating soil properties using limited data. Soil Science Society of America Journal, 65: 470-479. DOI: 10.2136/sssaj2001.652470x.
doi: 10.2136/sssaj2001.652470x
Shit PK, Bhunia GS, Maiti R, 2016. Spatial analysis of soil properties using GIS based geostatistics models. Modeling Earth Systems & Environment, 2: 1-6. DOI: 10.1007/s40808-016-0160-4.
doi: 10.1007/s40808-016-0160-4
Simbahan GC, Dobermann A, 2006. Sampling optimization based on secondary information and its utilization in soil carbon mapping. Geoderma, 133: 345-362. DOI: 10.1016/j.geoderma.2005.07.020.
doi: 10.1016/j.geoderma.2005.07.020
Steffens M, Kölbl A, Giese M, et al., 2009. Spatial variability of topsoils and vegetation in a grazed steppe ecosystem in Inner Mongolia (PR China). Journal of Plant Nutrition and Soil Science, 172: 78-90. DOI: 10.1002/jpln.200700309.
doi: 10.1002/jpln.200700309
Sun W, Zhao Y, Huang B, et al., 2012. Effect of sampling density on regional soil organic carbon estimation for cultivated soils. Journal of Plant Nutrition and Soil Science, 175: 671-680. DOI: 10.1002/jpln.201100181.
doi: 10.1002/jpln.201100181
Tao D, Singh N, Goswami C, 2018. Spatial variability of soil organic carbon and available nutrients under different topography and land uses in Meghalaya, India. International Journal of Plant & Soil Science, 21: 1-16. DOI: 10.9734/ijpss/2018/39615.
doi: 10.9734/ijpss/2018/39615
Wang J, Yang R, Feng Y, 2017. Spatial variability of reconstructed soil properties and the optimization of sampling number for reclaimed land monitoring in an opencast coal mine. Arabian Journal of Geosciences, 10: 46. DOI: 10.1007/s12517-017-2836-0.
doi: 10.1007/s12517-017-2836-0
Wang J, Yang R, Bai Z, 2015. Spatial variability and sampling optimization of soil organic carbon and total nitrogen for minesoils of the Loess Plateau using geostatistics. Ecological Engineering, 82: 159-164. DOI: 10.1016/j.ecoleng. 2015.04.103.
doi: 10.1016/j.ecoleng. 2015.04.103
Wang T, Han P, Wu SB, et al., 2011. Deserts and Aeolian Desertification in China. Science Press, pp. 24.
Wang T, Wu W, Zhao HL, et al., 2004. Analyses on driving factors to sandy desertification process in Horqin Region, China. Journal of Desert Research, 135: 348-357. DOI: 10.1111/j.1365-2249.2004.02384.x. (in Chinese)
doi: 10.1111/j.1365-2249.2004.02384.x
Webster R, Oliver MA, 1992. Sample adequately to estimate variograms of soil properties. European Journal of Soil Science, 43: 177-192. DOI: 10.1111/j.1365-2389.1992.tb00128.x.
doi: 10.1111/j.1365-2389.1992.tb00128.x
Webster R, Oliver MA, 2007. Geostatistics for Environmental Scientists, Second Edition. Statistics for Earth and Environmental Scientists, pp. 271.
Were K, Bui DT, Dick OB, et al., 2015. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators, 52: 394-403. DOI: 10.1016/j.ecolind.2014.12.028.
doi: 10.1016/j.ecolind.2014.12.028
Wiesmeier M, Spörlein P, Geuß U, et al., 2012. Soil organic carbon stocks in southeast Germany (Bavaria) as affected by land use, soil type and sampling depth. Global Change Biology, 18: 2233-2245. DOI: 10.1111/j.1365-2486.2012. 02699.x.
doi: 10.1111/j.1365-2486.2012. 02699.x
Wiesmeier M, Hübner R, Barthold F, et al., 2013. Amount, distribution and driving factors of soil organic carbon and nitrogen in cropland and grassland soils of southeast Germany (Bavaria). Agriculture, Ecosystems & Environment, 176: 39-52. DOI: 10.1016/j.agee.2013.05.012.
doi: 10.1016/j.agee.2013.05.012
Wilson BR, Koen TB, Barnes P, et al., 2011. Soil carbon and related soil properties along a soil type and land-use intensity gradient, New South Wales, Australia. Soil Use & Management, 27: 437-447. DOI: 10.1111/j.1475-2743.2011. 00357.x.
doi: 10.1111/j.1475-2743.2011. 00357.x
Zhang C, Liu G, Xue S, et al., 2013. Soil organic carbon and total nitrogen storage as affected by land use;in a small watershed of the Loess Plateau, China. European Journal of Soil Biology, 54: 16-24. DOI: 10.1016/j.ejsobi.2012. 10.007.
doi: 10.1016/j.ejsobi.2012. 10.007
Yang R, Su Y, Wang M, et al., 2013. Field-scale spatial and temporal variation of soil organic carbon in a reclaimed sandy farmland. Journal of Desert Research, 33: 1078-1083. DOI: 210.72.80.159. (in Chinese)
doi: 210.72.80.159
Yasrebi J, Saffari M, Fathi H, et al., 2012. Evaluation and comparison of Ordinary Kriging and Inverse Distance Weighting methods for prediction of spatial variability of some soil chemical parameters. Research Journal of Biological Sciences, 4: 385-394. DOI: 10.5556/j.tkjm.42.2011.
doi: 10.5556/j.tkjm.42.2011
Yu DS, Zhang ZQ, Yang H, et al., 2011. Effect of soil sampling density on detected spatial variability of soil organic carbon in a red soil region of China. Pedosphere, 21: 207-213. DOI: 10.1016/s1002-0160(11)60119-7.
doi: 10.1016/s1002-0160(11)60119-7
Yuan ZQ, Gazol A, Lin F, et al., 2013. Soil organic carbon in an old-growth temperate forest: Spatial pattern, determinants and bias in its quantification. Geoderma, 195-196:48-55. DOI: 10.1016/j.geoderma.2012.11.008.
doi: 10.1016/j.geoderma.2012.11.008
Zare MA, Zare CA, Ahvazi LK, 2011. Comparing geostatistical approaches for mapping soil properties in Poshtkouh rangelands of Yazd Province. Iran. International Journal of Plant Research, 24: 77-88.
Zhao Y, Xu X, Tian K, et al., 2016. Comparison of sampling schemes for the spatial prediction of soil organic matter in a typical black soil region in China. Environmental Earth Sciences, 75: 1-14. DOI: 10.1007/s12665-015-4895-4.
doi: 10.1007/s12665-015-4895-4
Zhou RL, Li YQ, Zhao HL, et al., 2008. Desertification effects on C and N content of sandy soils under grassland in Horqin, northern China. Geoderma, 145: 370-375. DOI: 10. 1016/j.geoderma.2008.04.003.
doi: 10. 1016/j.geoderma.2008.04.003
Zhu Q, Lin HS, 2010. Comparing ordinary kriging and regression kriging for soil properties in contrasting landscapes. Pedosphere, 20: 594-606. DOI: 10.1016/s1002-0160(10)60049-5.
doi: 10.1016/s1002-0160(10)60049-5
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