| Literature DB >> 25988714 |
Zhao Fazhu1, Sun Jiao2, Ren Chengjie1, Kang Di3, Deng Jian1, Han Xinhui1, Yang Gaihe1, Feng Yongzhong1, Ren Guangxin1.
Abstract
Changes in land use might affect the combined C, N and P stoichiometry in soil. The Grain-to-Green Program (GTGP), which converts low-yield croplands or abandoned lands into forest, shrub, and/or grassland, was the largest land reforestation project in China. This study collected the reported C, N and P contents of soil in GTGP zones to achieve the factors driving the changes in the C:N, C:P, and N:P values. The results showed that the annual average precipitation exerted significant effects on the C:P value, and on the N:P value became significant 20 years after the change in land use. The annual average temperature was the main factor affecting the C:N value during the first 10 years, while the annual average precipitation strongly affected this value afterwards. In addition, "Redfield-like" interactions between C, N, and P in the soil may exist. A linear regression revealed significant positive correlations between the C:N, C:P, and N:P values and the restoration age, temperature, and precipitation after a change in land use. Therefore large-scale changes in land use under the 'GTGP' program might significantly affect the C:N, C:P and N:P ratios in soil.Entities:
Year: 2015 PMID: 25988714 PMCID: PMC4650801 DOI: 10.1038/srep10195
Source DB: PubMed Journal: Sci Rep ISSN: 2045-2322 Impact factor: 4.379
Figure 1Distribution of soil C:N value (a), C:P value (b) and N:P value (c) value under ‘Grain-to-Green Program related zones’. The map plotted by Arcgis9.3 using inverse distance weighting (IDW) method.
Figure 2Frequency distribution of the soil C:N (a), C:P (b) and N:P ratios (c) in the China ‘Grain-to-Green Program’.
Figure 3Change in the soil C:N (a), C:P (b), and N:P (c) ratios as a function of the restoration age after the change of slop cropland or abandoned land to forest. The error bars represent the standard errors for the slope of Equation 3 (k), and the values above the bars are the corresponding number of observations (the meaning of the error bars and the values are the same in Figs. 5,6).
Figure 4Change in the soil C:N (a), C:P (b), and N:P (c) ratios as a function of the restoration age under the ‘Grain-to-Green Program’ for converted shrublands.
Figure 5Change in the soil C:N (a), C:P (b), and N:P (c) ratios as a function of the restoration age under the ‘Grain-to-Green Program’ for converted grasslands.
Stepwise regression to detect factors affecting soil C:N, C:P, and N:P value at different restoration ages.
| C:N | <10 | RCN = 18.10 + 2.81T–0.04P | 0.06 | 0.01 |
| 10–20 | RCN = 20.20–0.04P + 3.15T | 0.11 | 0.01 | |
| >20 | RCN = 41.06–0.05P + 2.68T | 0.08 | 0.004 | |
| C:P | <10 | RCP = –10.45 + 0.13P–1.08T | 0.29 | 0.000 |
| 10–20 | RCP = –29.56 + 0.24P + 24.84T | 0.45 | 0.000 | |
| >20 | RCP = 9.55 + 0.21P–9.29T + 0.70A | 0.33 | 0.000 | |
| N:P | <10 | RNP = 3.79 + 0.37A | 0.34 | 0.000 |
| 10–20 | RNP = 12.46 + 0.18A + 0.25T–0.02P | 0.28 | 0.007 | |
| >20 | RNP = 30.95 + 0.02P–0.62T + 0.08A | 0.17 | 0.030 |
Note: RCN, RCP, and RNP are soil C:N, C:P, and N:P value, respectively; T (°C) is annual average temperature; P (mm) is annual average precipitation; and A (year) is restoration age after land use change.
*indicates Significant at P < 0.05 and.
**indicates extremely significant at P < 0.01.
effects of annual average temperature, average precipitation, land use change, restoration age, and there interactions on soil C:N, C:P, and N:P ratios under ‘Grain-to-Green Program.
| T | 2 | 0.636 | 0.530 | 2 | 13.363 | 0.000 | 2 | 7.147 | 0.031 |
| P | 2 | 6.662 | 0.030 | 2 | 14.450 | 0.000 | 2 | 10.814 | 0.000 |
| L | 2 | 11.236 | 0.000 | 2 | 4.400 | 0.013 | 2 | 8.217 | 0.000 |
| A | 2 | 10.245 | 0.000 | 2 | 8.171 | 0.000 | 2 | 13.780 | 0.000 |
| T × P | 2 | 0.847 | 0.429 | 2 | 5.306 | 0.005 | 3 | 0.972 | 0.406 |
| T × L | 4 | 0.491 | 0.742 | 4 | 0.867 | 0.484 | 4 | 0.305 | 0.875 |
| T × A | 4 | 0.634 | 0.638 | 4 | 1.393 | 0.235 | 4 | 2.000 | 0.093 |
| P × L | 4 | 4.382 | 0.033 | 4 | 0.510 | 0.728 | 4 | 0.522 | 0.720 |
| P × A | 4 | 0.255 | 0.906 | 4 | 1.410 | 0.229 | 4 | 1.985 | 0.096 |
| L × A | 4 | 0.078 | 0.989 | 4 | 2.499 | 0.042 | 4 | 0.463 | 0.763 |
| T × P × L | 3 | 0.420 | 0.739 | 2 | 0.346 | 0.707 | 2 | 0.205 | 0.815 |
| T × L × A | 5 | 0.684 | 0.636 | 4 | 0.377 | 0.825 | 4 | 0.480 | 0.751 |
| P × L × A | 5 | 0.448 | 0.815 | 4 | 1.058 | 0.377 | 4 | 0.340 | 0.851 |
| T × P × L × A | 3 | 0.606 | 0.611 | 3 | 4.174 | 0.006 | 2 | 1.099 | 0.334 |
Note: T, P, L, and A are the annual average temperature, average precipitation, land use change, restoration age, respectively.
*indicates Significant at P < 0.05 and.
**indicates extremely significant at P < 0.01.
Correlations among soil organic C (g/kg), total N (g/kg) and total P (g/kg) under ‘Grain-to-Green Program’.
| Soil C | Soil N | 557 | 0.71 | <0.0001 |
| Soil C | Soil P | 394 | 0.28 | <0.0001 |
| Soil N | Soil P | 394 | 0.48 | <0.0001 |
soils C, N and P stoichiometry for other countries under different land use types.
| Forest | New Zealand | 12 | 20.58 | 358.27 | 16.97 | Ross |
| India | 19 | 11.59 | 237.92 | 30.91 | Barbhuiya | |
| Deve and Yadava (2006); | ||||||
| Singh and Singh (1995); | ||||||
| Srivastava (1998) | ||||||
| Costa Rica | 4 | 12.93 | 234.04 | 18.23 | Cleveland | |
| Germany | 3 | 18.26 | 34.52 | 1.91 | Joergensen | |
| UK | 2 | 10.37 | 103.36 | 9.97 | Turner | |
| Slovak | 2 | 16.39 | 227.04 | 13.63 | Kopacek | |
| USA | 10 | 25.50 | 90.00 | 3.50 | Liptzin | |
| Average | 16.52 | 183.59 | 13.59 | |||
| China | 14.51 | 144.90 | 3.93 | This study | ||
| Shurbland | Australia | 5 | 20.5 | 289.00 | 14.25 | Bui |
| China | 15.00 | 75.16 | 4.8 | This study | ||
| Grassland | Australia | 10 | 18.00 | 173.50 | 9.30 | Bui |
| New Zealand | 22 | 16.60 | 69.50 | 4.00 | Mulder | |
| India | 6 | 9.95 | 206.12 | 20.71 | Srivastava | |
| UK | 29 | 13.82 | 351.40 | 25.19 | Turner | |
| Panama | 4 | 17.11 | 459.82 | 26.87 | Yavitt | |
| Australia | 2 | 14.00 | 54.50 | 3.50 | Bui | |
| USA | 72 | 13.80 | 166.00 | 12.30 | Cleveland | |
| Poland | 15 | 17.36 | 308.06 | 17.74 | Kopáček | |
| Average | 14.66 | 230.77 | 15.76 | |||
| China | 12.52 | 58.22 | 9.42 | This study | ||
Figure 6Distribution of sampling sites in the dataset. The map plotted by Arcgis9.3.