Determining Optimal Sampling Numbers to Investigate the Soil Organic Matter in a Typical County of the Yellow River Delta, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Soil Sampling and Laboratory Analysis
2.3. Data Processing and Analysis
2.4. Spatial Prediction and Validation
3. Results
3.1. Descriptive Statistics of SOM
3.2. Characteristics of the Spatial-Variation Structure of SOM
3.3. Effect of Different Sampling-Point Numbers on the Prediction Accuracy of SOM Content
3.4. Effect of Sampling-Point Numbers on the Expression of Spatial Distribution of SOM Content
4. Discussion
4.1. Comparison of the Rational Sampling Numbers in Different Regions
4.2. Number of Sampling Points and Spatial-Prediction Error
4.3. Influencing Factors of Spatial Prediction of SOM
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Number of Samples | Minimum | Maximum | Mean | Standard Deviation | Skewness | Kurtosis | Median | Coefficient of Variation/(%) |
---|---|---|---|---|---|---|---|---|
/(g/kg) | /(g/kg) | /(g/kg) | /(g/kg) | /(g/kg) | ||||
900 | 5.00 | 27.30 | 10.96 | 3.93 | 0.98 | 4.13 | 10.30 | 35.80 |
800 | 5.00 | 27.30 | 10.89 | 3.99 | 1.05 | 4.30 | 10.20 | 36.60 |
700 | 5.00 | 27.30 | 10.93 | 4.05 | 1.06 | 4.33 | 10.25 | 37.05 |
600 | 5.00 | 27.30 | 10.97 | 3.99 | 0.96 | 4.09 | 10.35 | 36.40 |
500 | 5.00 | 25.50 | 10.99 | 3.87 | 0.86 | 3.76 | 10.40 | 35.18 |
400 | 5.00 | 24.60 | 11.10 | 3.80 | 0.67 | 3.25 | 10.60 | 34.27 |
300 | 5.00 | 24.60 | 11.22 | 3.81 | 0.70 | 3.41 | 10.80 | 33.99 |
200 | 5.00 | 24.60 | 11.23 | 3.89 | 0.82 | 3.65 | 10.80 | 34.63 |
150 | 5.00 | 23.20 | 11.03 | 3.79 | 0.70 | 3.20 | 10.60 | 34.38 |
100 | 5.20 | 24.60 | 11.49 | 4.18 | 0.88 | 3.47 | 10.60 | 36.42 |
75 | 5.20 | 24.60 | 11.61 | 4.33 | 0.90 | 3.45 | 10.80 | 37.29 |
50 | 5.80 | 24.60 | 11.56 | 3.99 | 0.99 | 3.93 | 10.75 | 34.49 |
Number of Samples | Minimum | Maximum | Mean | Standard Deviation | Skewness | Kurtosis | Median | Coefficient of Variation/(%) |
---|---|---|---|---|---|---|---|---|
/(g/kg) | /(g/kg) | /(g/kg) | /(g/kg) | /(g/kg) | ||||
900 | 1.61 | 3.31 | 2.33 | 0.35 | 0.10 | 2.53 | 2.33 | 14.85 |
800 | 1.61 | 3.31 | 2.33 | 0.35 | 0.15 | 2.55 | 2.32 | 15.09 |
700 | 1.61 | 3.31 | 2.33 | 0.35 | 0.16 | 2.54 | 2.33 | 15.23 |
600 | 1.61 | 3.31 | 2.33 | 0.35 | 0.07 | 2.48 | 2.34 | 15.19 |
500 | 1.61 | 3.24 | 2.34 | 0.35 | 0.01 | 2.49 | 2.34 | 14.81 |
400 | 1.61 | 3.20 | 2.35 | 0.34 | −0.10 | 2.42 | 2.36 | 14.68 |
300 | 1.61 | 3.20 | 2.36 | 0.34 | −0.13 | 2.53 | 2.38 | 14.51 |
200 | 1.61 | 3.20 | 2.36 | 0.34 | −0.03 | 2.56 | 2.38 | 14.52 |
150 | 1.61 | 3.14 | 2.34 | 0.35 | 0.10 | 2.43 | 2.36 | 15.12 |
100 | 1.65 | 3.20 | 2.38 | 0.35 | 0.10 | 2.43 | 2.36 | 14.89 |
75 | 1.65 | 3.20 | 2.39 | 0.36 | 0.11 | 2.45 | 2.38 | 15.17 |
50 | 1.76 | 3.20 | 2.39 | 0.33 | 0.23 | 2.47 | 2.37 | 13.76 |
Number of Samples | Model Type | Nugget (C0) | Sill (C0 + C) | Nugget Ratio (C0/sill) | Range/(m) | R2 | RS |
---|---|---|---|---|---|---|---|
800 | Spherical | 9.09 | 13.02 | 69.81% | 2780.00 | 0.22 | 14.60 |
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Wang, W.; Duan, M.; Zhang, X.; Song, X.; Liu, X.; Cui, D. Determining Optimal Sampling Numbers to Investigate the Soil Organic Matter in a Typical County of the Yellow River Delta, China. Appl. Sci. 2022, 12, 6062. https://doi.org/10.3390/app12126062
Wang W, Duan M, Zhang X, Song X, Liu X, Cui D. Determining Optimal Sampling Numbers to Investigate the Soil Organic Matter in a Typical County of the Yellow River Delta, China. Applied Sciences. 2022; 12(12):6062. https://doi.org/10.3390/app12126062
Chicago/Turabian StyleWang, Wenjing, Mengqi Duan, Xiaoguang Zhang, Xiangyun Song, Xinwei Liu, and Dejie Cui. 2022. "Determining Optimal Sampling Numbers to Investigate the Soil Organic Matter in a Typical County of the Yellow River Delta, China" Applied Sciences 12, no. 12: 6062. https://doi.org/10.3390/app12126062