Stratification of regional sampling by model-predicted changes of carbon stocks in forested mineral soils
Monitoring changes in soil C has recently received interest due to reporting under the Kyoto Protocol. Model-based approaches to estimate changes in soil C stocks exist, but they cannot fully replace repeated measurements. Measuring changes in soil C is laborious due to small expected changes an...
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| Format: | Article |
| Language: | English |
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Finnish Society of Forest Science
2007-01-01
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| Series: | Silva Fennica |
| Online Access: | https://www.silvafennica.fi/article/287 |
| _version_ | 1818833807542845440 |
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| author | Peltoniemi, Mikko Heikkinen, Juha Mäkipää, Raisa |
| author_facet | Peltoniemi, Mikko Heikkinen, Juha Mäkipää, Raisa |
| author_sort | Peltoniemi, Mikko |
| collection | DOAJ |
| description | Monitoring changes in soil C has recently received interest due to reporting under the Kyoto Protocol. Model-based approaches to estimate changes in soil C stocks exist, but they cannot fully replace repeated measurements. Measuring changes in soil C is laborious due to small expected changes and large spatial variation. Stratification of soil sampling allows the reduction of sample size without reducing precision. If there are no previous measurements, the stratification can be made with model-predictions of target variable. Our aim was to present a simulation-based stratification method, and to estimate how much stratification of inventory plots could improve the efficiency of the sampling. The effect of large uncertainties related to soil C change measurements and simulated predictions was targeted since they may considerably decrease the efficiency of stratification. According to our simulations, stratification can be useful with a feasible soil sample number if other uncertainties (simulated predictions and forecasted forest management) can be controlled. For example, the optimal (Neyman) allocation of plots to 4 strata with 10 soil samples from each plot (unpaired repeated sampling) reduced the standard error (SE) of the stratified mean by 9â34% from that of simple random sampling, depending on the assumptions of uncertainties. When the uncertainties of measurements and simulations were not accounted for in the division to strata, the decreases of SEs were 2â9 units less. Stratified sampling scheme that accounts for the uncertainties in measured material and in the correlates (simulated predictions) is recommended for the sampling design of soil C stock changes. |
| first_indexed | 2024-12-19T02:24:48Z |
| format | Article |
| id | doaj.art-ce721c4f7f474c15bab57e1fbb7f6813 |
| institution | Directory Open Access Journal |
| issn | 2242-4075 |
| language | English |
| last_indexed | 2024-12-19T02:24:48Z |
| publishDate | 2007-01-01 |
| publisher | Finnish Society of Forest Science |
| record_format | Article |
| series | Silva Fennica |
| spelling | doaj.art-ce721c4f7f474c15bab57e1fbb7f68132022-12-21T20:39:58ZengFinnish Society of Forest ScienceSilva Fennica2242-40752007-01-0141310.14214/sf.287Stratification of regional sampling by model-predicted changes of carbon stocks in forested mineral soilsPeltoniemi, MikkoHeikkinen, JuhaMäkipää, RaisaMonitoring changes in soil C has recently received interest due to reporting under the Kyoto Protocol. Model-based approaches to estimate changes in soil C stocks exist, but they cannot fully replace repeated measurements. Measuring changes in soil C is laborious due to small expected changes and large spatial variation. Stratification of soil sampling allows the reduction of sample size without reducing precision. If there are no previous measurements, the stratification can be made with model-predictions of target variable. Our aim was to present a simulation-based stratification method, and to estimate how much stratification of inventory plots could improve the efficiency of the sampling. The effect of large uncertainties related to soil C change measurements and simulated predictions was targeted since they may considerably decrease the efficiency of stratification. According to our simulations, stratification can be useful with a feasible soil sample number if other uncertainties (simulated predictions and forecasted forest management) can be controlled. For example, the optimal (Neyman) allocation of plots to 4 strata with 10 soil samples from each plot (unpaired repeated sampling) reduced the standard error (SE) of the stratified mean by 9â34% from that of simple random sampling, depending on the assumptions of uncertainties. When the uncertainties of measurements and simulations were not accounted for in the division to strata, the decreases of SEs were 2â9 units less. Stratified sampling scheme that accounts for the uncertainties in measured material and in the correlates (simulated predictions) is recommended for the sampling design of soil C stock changes.https://www.silvafennica.fi/article/287 |
| spellingShingle | Peltoniemi, Mikko Heikkinen, Juha Mäkipää, Raisa Stratification of regional sampling by model-predicted changes of carbon stocks in forested mineral soils Silva Fennica |
| title | Stratification of regional sampling by model-predicted changes of carbon stocks in forested mineral soils |
| title_full | Stratification of regional sampling by model-predicted changes of carbon stocks in forested mineral soils |
| title_fullStr | Stratification of regional sampling by model-predicted changes of carbon stocks in forested mineral soils |
| title_full_unstemmed | Stratification of regional sampling by model-predicted changes of carbon stocks in forested mineral soils |
| title_short | Stratification of regional sampling by model-predicted changes of carbon stocks in forested mineral soils |
| title_sort | stratification of regional sampling by model predicted changes of carbon stocks in forested mineral soils |
| url | https://www.silvafennica.fi/article/287 |
| work_keys_str_mv | AT peltoniemimikko stratificationofregionalsamplingbymodelpredictedchangesofcarbonstocksinforestedmineralsoils AT heikkinenjuha stratificationofregionalsamplingbymodelpredictedchangesofcarbonstocksinforestedmineralsoils AT makipaaraisa stratificationofregionalsamplingbymodelpredictedchangesofcarbonstocksinforestedmineralsoils |