Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI5
volume of deadwood (stemwood)
conifers and broadleaves · diameter at breast height (10 classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
conifers and broadleaves	diameter at breast height (10 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
conifers	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	7	71	7	85	5	100	10	51	24	45	17	43	197	19	63	27	50	33	42	29	3	100	3	71	16	59	0	.	20	59	32	48	35	43	12	46	0	0	20	44	45	30	38	37	68	31	77	22	154	24	65	39	35	48	17	51	76	38	14	50	4	71	5	72	1	100	29	44	16	70	21	55	35	56	13	60	33	68	31	35	17	50	30	41	5	62	6	76	10	81	7	72	11	51	14	48	47	46	10	65	19	58	1	100	9	100	4	68	17	46	14	48	0	0	0	.	0	.	7	85	3	58	6	73	6	61	0	0	35	42	23	40	9	84	22	49	21	81	0	0	29	34	2	100	4	100	12	58	11	71	14	52	5	60	4	100	1	100	0	0	6	71	143	20	147	26	130	31	20	55	35	88	13	58	2	71	5	100	3	100	0	0	5	79	0	.	2288	5
	21-30 cm	17	71	14	100	0	0	20	59	5	100	100	34	317	25	156	31	99	35	151	31	0	0	15	73	21	71	0	.	0	0	34	51	96	43	36	60	0	.	53	56	90	44	43	43	131	35	361	23	214	28	108	40	29	64	15	71	252	40	0	0	0	0	12	100	0	0	8	100	44	46	29	61	25	51	8	100	30	65	40	53	5	100	21	71	24	61	6	100	24	75	27	71	23	58	22	82	117	62	41	74	6	100	28	71	21	100	0	0	44	62	35	60	0	0	0	.	0	.	18	65	0	0	5	100	45	52	21	72	86	37	9	72	4	100	109	63	8	100	0	0	24	76	0	0	0	0	16	100	16	74	10	100	21	100	6	100	25	73	26	100	14	100	181	27	119	27	236	27	27	82	52	86	24	100	11	100	14	100	18	74	7	100	0	0	0	.	4171	6
	31-40 cm	24	71	0	0	0	0	22	100	14	100	81	57	786	17	177	36	118	40	203	43	31	74	0	0	40	60	0	.	22	100	29	59	85	58	65	74	0	0	94	46	139	33	43	55	165	43	345	27	310	31	219	46	37	58	65	54	179	30	18	100	46	60	0	0	15	100	43	61	37	50	31	61	119	55	23	71	0	0	81	55	33	100	95	49	48	45	21	100	0	0	0	0	54	53	51	62	29	76	47	63	0	0	11	100	43	100	17	72	58	47	44	52	0	0	0	.	0	.	8	100	12	100	0	0	7	100	26	58	92	55	19	71	74	52	78	74	21	73	0	0	31	100	0	0	18	70	17	71	28	59	27	73	64	71	0	0	0	0	11	100	20	100	264	24	337	27	379	21	44	77	38	59	0	0	31	58	0	0	23	71	0	0	0	0	0	.	5928	6
	41-50 cm	0	0	15	100	17	100	77	58	28	71	56	58	671	20	143	38	13	100	216	42	0	0	13	100	38	58	0	.	0	0	27	71	157	37	75	56	14	100	39	73	81	43	144	47	360	33	218	29	186	36	102	47	14	100	96	46	193	34	0	0	0	0	13	100	19	100	25	72	52	77	52	73	106	50	38	71	55	59	92	40	19	100	102	56	139	81	15	100	27	71	22	100	0	0	22	100	133	52	32	71	16	100	0	0	39	71	9	100	55	45	76	46	0	0	0	.	0	0	25	71	19	100	0	0	34	100	0	0	58	51	24	71	39	75	84	55	64	67	0	0	59	87	0	0	16	100	25	71	0	0	16	100	38	72	0	0	0	0	16	100	0	0	255	30	184	28	237	24	0	0	0	0	79	54	14	100	12	100	0	0	0	0	0	0	0	.	5448	7
	51-60 cm	0	0	0	0	0	0	71	74	20	100	103	82	510	25	278	33	86	50	138	47	0	0	0	0	0	0	0	.	0	0	0	0	203	51	43	72	0	.	70	76	39	71	163	40	321	34	278	28	215	34	84	50	0	0	62	72	88	60	0	0	0	0	0	0	23	100	24	100	35	72	23	100	43	74	0	0	50	72	36	72	29	100	20	100	45	100	0	0	0	0	0	0	0	0	36	71	64	74	83	73	0	0	0	0	23	100	16	100	160	71	48	75	0	0	0	.	0	0	0	0	0	0	19	100	0	0	55	58	63	59	37	71	22	100	17	100	0	0	0	0	113	68	0	0	18	100	0	0	0	0	25	100	43	72	0	0	19	100	0	0	0	0	271	33	111	47	285	35	43	100	100	62	33	100	47	72	0	0	0	0	0	0	0	0	0	.	4850	8
	61-70 cm	68	71	0	.	0	0	0	0	0	0	31	100	75	59	36	100	30	100	88	58	0	.	0	0	0	0	0	.	0	0	0	0	68	73	62	100	0	.	32	100	57	72	93	59	23	100	252	32	0	0	71	72	58	100	40	100	388	46	0	0	0	0	0	0	0	0	0	0	0	0	55	71	53	71	0	0	0	0	76	58	0	0	0	0	39	100	33	100	0	0	63	71	0	0	0	0	0	0	0	0	0	0	0	0	34	100	0	0	0	0	112	82	0	0	0	0	0	0	0	0	24	100	0	0	84	59	29	100	26	100	0	0	23	100	66	74	0	0	0	0	31	100	0	.	0	0	0	0	0	0	0	0	0	0	0	0	34	100	0	.	0	0	138	41	90	50	63	72	0	0	68	100	0	0	0	0	0	0	0	.	0	0	0	0	0	.	2614	12
	71-80 cm	0	.	0	0	0	0	0	0	0	0	0	0	69	72	103	71	79	100	0	0	0	.	0	0	0	0	0	.	0	0	0	0	170	58	0	0	0	.	0	0	0	0	0	0	32	100	0	0	0	0	87	71	96	71	41	100	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	50	100	0	0	0	0	0	0	85	71	0	0	0	0	0	0	0	.	0	.	0	0	0	0	47	100	0	0	0	0	0	0	0	0	25	100	54	100	0	.	0	0	0	.	27	100	0	0	0	0	0	0	0	0	0	0	0	0	34	100	0	0	0	.	0	.	39	100	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	24	100	66	71	0	0	0	0	41	100	0	.	0	0	34	100	0	.	0	0	0	.	0	.	1203	20
	81-90 cm	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	0	92	71	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	63	71	41	100	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	0	44	100	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	.	39	100	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0	55	100	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	.	333	36
	91-100 cm	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	100	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	60
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	106	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	106	100
	total	115	47	36	60	22	81	200	37	92	40	388	40	2626	14	956	21	567	24	891	28	33	69	31	59	115	42	0	.	42	60	122	43	814	35	294	45	14	100	358	33	452	24	525	29	1102	29	1593	17	1120	21	736	25	269	41	336	35	1177	26	32	68	50	56	30	59	58	77	235	59	185	43	211	47	381	41	132	50	168	48	356	29	146	56	353	40	300	60	81	61	62	57	119	52	89	42	145	36	390	41	259	42	41	54	41	71	169	57	47	56	361	37	383	48	39	100	0	0	0	0	85	54	58	67	30	70	176	37	131	60	359	36	111	40	259	46	377	40	114	53	0	0	326	56	2	100	56	49	70	43	55	55	93	45	171	65	10	73	80	60	52	71	40	62	1302	17	1054	21	1331	18	134	48	334	51	148	47	105	49	65	60	45	48	7	100	5	79	0	.	27071	4
broadleaves	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	41	43	5	73	2	100	9	53	2	78	13	58	57	29	27	43	31	37	25	36	19	61	3	100	20	49	0	.	22	51	23	45	17	60	30	69	5	51	59	36	11	53	9	92	28	38	110	29	14	53	124	28	25	41	23	51	41	37	18	57	13	58	6	100	0	0	1	100	7	61	8	65	21	65	3	100	6	54	23	33	10	54	21	44	30	38	1	100	13	68	25	63	17	48	15	64	2	100	28	62	10	54	9	100	2	100	28	54	37	70	9	66	125	40	65	37	227	25	75	31	56	43	127	35	14	47	17	100	2	100	24	61	31	53	4	51	9	66	6	71	24	47	4	61	5	79	4	81	17	53	14	58	17	59	14	62	2	100	7	100	0	0	28	31	39	38	85	27	4	100	0	0	3	72	28	48	12	66	3	77	6	64	0	0	0	.	2258	6
	21-30 cm	23	60	20	78	0	0	14	71	0	0	32	60	53	52	32	59	19	73	51	55	42	71	7	100	28	78	0	.	13	100	30	66	0	0	5	100	12	100	49	43	34	76	8	73	63	60	76	38	43	85	93	37	0	0	17	71	71	50	11	71	0	0	0	0	0	0	0	0	35	59	0	0	30	52	6	100	31	100	59	41	74	65	32	51	17	100	0	0	79	54	13	72	42	55	28	78	18	78	24	51	0	0	12	100	0	0	20	66	0	0	7	71	26	53	121	42	147	36	48	43	61	57	83	36	14	74	0	0	0	0	28	57	5	100	6	100	23	59	5	100	67	57	0	.	8	100	0	0	6	100	0	0	17	73	13	74	0	0	0	0	0	0	17	59	8	71	50	46	0	0	5	100	5	100	72	51	28	61	0	0	0	0	12	71	0	.	2246	8
	31-40 cm	23	100	7	100	0	0	0	0	0	0	75	54	43	82	49	52	44	62	48	88	9	100	31	58	31	76	0	.	16	71	7	100	43	59	0	0	0	0	42	64	5	100	5	100	5	100	59	50	41	77	225	63	21	76	9	100	41	67	11	100	0	0	0	0	0	0	16	100	46	70	0	0	0	0	29	59	56	75	44	60	15	100	18	100	9	100	0	0	0	0	10	100	41	70	13	100	11	100	0	0	0	0	0	0	0	0	0	.	13	71	11	71	16	70	93	50	43	90	5	100	64	46	44	49	0	0	0	0	0	0	18	72	16	100	0	0	0	0	0	0	36	100	0	0	0	0	0	0	27	73	0	0	26	71	24	71	0	0	0	0	0	0	12	71	0	0	19	72	0	0	0	0	13	100	19	72	0	0	0	0	6	100	11	100	0	.	1711	13
	41-50 cm	12	100	51	74	0	0	23	71	0	0	11	100	31	60	37	59	52	46	0	0	0	0	45	59	13	100	0	.	9	100	12	100	0	0	11	100	0	0	70	49	0	0	14	71	22	71	10	100	0	0	77	45	0	0	14	100	48	59	14	100	0	0	0	0	0	0	0	0	0	0	8	100	17	100	29	71	12	100	0	0	0	0	44	59	0	0	11	100	12	100	37	59	17	71	0	0	18	100	12	100	11	100	18	100	18	100	0	.	0	0	17	78	0	0	26	65	72	50	12	100	47	51	45	42	6	100	0	0	0	0	25	72	0	0	0	0	0	0	0	0	13	100	0	0	0	0	0	0	0	0	0	0	10	100	0	0	0	0	0	0	0	0	7	100	0	0	18	71	12	100	14	100	0	0	35	100	0	0	0	0	0	0	16	100	0	.	1217	11
	51-60 cm	91	62	41	71	0	0	14	100	0	.	0	0	0	0	0	0	20	100	0	0	44	100	39	100	0	0	0	.	0	0	39	100	0	0	28	100	0	0	34	71	13	100	0	0	0	0	18	100	0	0	0	0	0	0	0	0	0	0	20	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	17	100	14	100	0	0	35	100	16	100	0	0	0	0	20	100	19	100	0	0	0	0	0	0	0	0	21	100	0	0	0	0	15	100	0	.	0	0	0	0	12	100	16	71	0	0	20	71	0	0	0	.	0	.	14	100	21	100	0	0	0	0	0	0	27	100	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	670	19
	61-70 cm	81	100	33	100	0	0	0	0	0	0	0	0	57	100	40	71	0	0	23	100	0	0	0	0	29	100	0	.	0	.	0	0	0	.	22	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	28	100	0	0	0	0	0	.	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	17	100	21	100	12	100	0	0	13	100	0	0	0	.	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	375	33
	71-80 cm	0	0	0	0	0	0	26	100	0	.	0	0	0	0	33	100	31	100	0	.	40	100	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	.	20	100	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	.	14	100	0	.	0	0	0	0	16	100	0	0	0	0	53	73	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	.	234	35
	81-90 cm	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	19	100	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	56	74
	91-100 cm	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	23	100	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	23	100
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	271	45	157	57	2	100	86	50	2	78	131	42	242	38	217	30	198	38	146	45	153	63	124	64	121	45	0	.	59	65	111	66	60	45	96	58	17	76	254	36	83	56	36	60	119	42	274	26	98	69	519	32	46	42	64	44	201	38	74	53	13	58	6	100	0	0	17	100	88	59	17	59	68	43	67	41	122	65	140	32	99	54	177	38	72	43	12	90	103	48	105	45	136	40	56	59	48	87	64	50	21	71	59	74	20	92	48	45	80	50	64	65	166	33	346	33	538	26	168	26	227	35	386	26	34	41	17	100	2	100	110	42	72	56	10	61	32	47	11	78	167	47	4	61	13	71	4	81	50	47	14	58	71	51	52	58	2	100	7	100	0	0	64	34	47	37	209	30	16	79	20	78	20	68	153	50	40	59	3	77	13	59	39	72	0	.	8791	5
indeterminable	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	73
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	71
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	65	51
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	94	44
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	48	38	12	57	7	100	19	37	26	43	31	35	254	16	90	23	81	28	66	27	24	51	5	62	36	38	0	.	41	46	55	36	52	35	43	59	5	51	79	32	55	27	47	35	96	26	187	20	168	23	189	23	60	41	40	40	118	28	32	41	17	48	11	63	1	100	31	42	23	52	30	44	56	43	15	60	39	60	55	25	27	39	50	31	35	34	8	65	23	52	32	51	28	37	28	40	49	44	38	49	29	42	10	88	11	86	32	48	54	50	23	41	125	40	65	37	227	25	82	29	59	42	133	34	20	38	17	100	37	40	47	40	40	45	27	45	29	61	6	71	53	32	6	60	8	63	16	51	28	51	29	39	22	49	18	65	3	72	7	100	6	71	171	18	186	22	216	23	24	49	35	88	15	49	30	46	17	55	6	62	6	64	5	79	0	.	4550	4
	21-30 cm	40	46	34	62	0	0	34	45	5	100	132	29	370	23	187	28	118	31	202	27	42	71	22	59	49	54	0	.	13	100	64	43	96	43	41	55	12	100	103	36	124	38	51	38	195	31	436	20	257	28	201	27	29	64	31	50	323	34	11	71	0	0	12	100	0	0	8	100	79	40	29	61	55	36	14	71	62	60	99	32	80	61	53	41	41	55	6	100	114	43	40	64	65	43	49	57	135	56	65	50	6	100	40	58	21	100	20	66	44	62	42	51	26	53	121	42	147	36	66	36	61	57	88	34	59	47	21	72	86	37	37	46	9	71	115	60	32	64	5	100	91	46	0	0	8	100	16	100	22	79	10	100	38	64	19	59	25	73	26	100	14	100	211	24	127	27	286	23	27	82	57	79	28	85	83	49	42	52	18	74	7	100	12	71	0	.	6442	5
	31-40 cm	47	61	7	100	0	0	22	100	14	100	155	44	844	17	226	31	162	34	251	39	40	62	31	58	71	47	0	.	38	83	36	52	128	48	65	74	0	0	136	37	159	30	48	50	171	42	404	24	351	31	444	39	58	46	75	52	220	28	29	72	46	60	0	0	15	100	58	52	84	45	31	61	119	55	52	52	56	75	125	41	48	75	113	44	57	41	21	100	15	100	10	100	95	53	65	68	39	62	47	63	0	0	11	100	43	100	17	72	71	41	55	48	16	70	93	50	43	90	13	73	76	42	44	49	7	100	26	58	92	55	37	50	89	47	78	74	21	73	0	0	66	71	0	0	18	70	17	71	55	47	27	73	91	54	24	71	0	0	11	100	20	100	276	24	337	27	398	21	44	77	38	59	13	100	49	45	0	0	44	60	6	100	11	100	0	.	7705	5
	41-50 cm	12	100	66	61	17	100	99	48	28	71	67	51	702	20	180	32	65	42	216	42	0	0	58	50	51	63	0	.	9	100	39	58	157	37	86	50	14	100	109	43	81	43	158	44	382	31	228	28	186	36	180	33	14	100	110	42	241	33	14	100	0	0	13	100	19	100	25	72	52	77	60	64	123	45	67	51	66	52	92	40	19	100	146	43	139	81	26	71	39	58	59	70	17	71	22	100	151	50	44	58	28	72	18	100	58	58	9	100	55	45	93	42	0	0	26	65	72	50	37	58	65	46	45	42	40	86	0	0	58	51	49	60	39	75	84	55	64	67	0	0	72	89	0	0	16	100	25	71	0	0	16	100	48	61	0	0	0	0	16	100	0	0	262	30	184	28	255	23	12	100	14	100	79	54	48	77	12	100	0	0	0	0	16	100	0	.	6665	6
	51-60 cm	91	62	41	71	0	0	85	64	20	100	103	82	510	25	278	33	106	45	138	47	44	100	39	100	0	0	0	.	0	0	39	100	203	51	72	59	0	0	103	56	53	59	163	40	321	34	296	27	215	34	84	50	0	0	62	72	88	60	20	100	0	0	0	0	23	100	24	100	35	72	23	100	43	74	0	0	68	60	49	59	29	100	55	73	61	78	0	0	0	0	20	100	19	100	36	71	64	74	83	73	0	0	21	100	23	100	16	100	175	65	48	75	0	0	0	0	12	100	16	71	0	0	39	60	0	0	55	58	63	59	51	58	42	71	17	100	0	0	0	0	140	58	0	0	18	100	0	0	0	0	25	100	43	72	0	0	19	100	0	0	0	0	271	33	111	47	285	35	43	100	100	62	33	100	47	72	0	0	0	0	0	0	0	0	0	.	5520	7
	61-70 cm	149	63	33	100	0	0	0	0	0	0	31	100	132	55	76	60	30	100	110	50	0	0	0	0	29	100	0	.	0	0	0	0	68	73	84	79	0	0	32	100	57	72	93	59	23	100	252	32	0	0	71	72	58	100	40	100	388	46	0	0	0	0	0	0	0	0	0	0	0	0	55	71	53	71	0	0	0	0	76	58	0	0	28	100	39	100	33	100	0	0	63	71	0	0	0	0	0	0	0	0	0	0	0	0	34	100	0	0	0	0	112	82	0	0	17	100	21	100	12	100	24	100	13	100	84	59	29	100	26	100	0	0	23	100	66	74	0	0	0	0	31	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	34	100	0	.	0	0	138	41	90	50	63	72	0	0	68	100	0	0	0	0	0	0	0	0	0	0	0	0	0	.	2989	12
	71-80 cm	0	0	0	0	0	0	26	100	0	0	0	0	69	72	136	59	111	77	0	0	40	100	0	0	0	0	0	.	0	0	0	0	170	58	0	0	0	0	0	0	20	100	0	0	32	100	0	0	0	0	87	71	96	71	41	100	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	50	100	0	0	0	0	0	0	85	71	0	0	0	0	0	0	0	0	0	.	0	0	0	0	47	100	0	0	0	0	0	0	0	0	40	73	54	100	0	0	0	0	16	100	27	100	0	0	53	73	0	0	0	0	0	0	0	0	34	100	0	0	0	0	0	.	39	100	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	24	100	66	71	0	0	0	0	41	100	0	0	0	0	34	100	0	0	0	0	0	0	0	.	1437	17
	81-90 cm	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	92	71	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	63	71	41	100	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	44	100	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	19	100	39	100	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	55	100	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	37	100	0	.	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	.	389	33
	91-100 cm	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	.	23	100	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	100	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	152	53
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	106	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	106	100
	total	387	35	194	48	24	76	286	30	94	39	519	35	2883	14	1173	19	765	21	1037	25	189	53	154	52	236	36	0	.	101	50	233	39	874	33	391	38	31	64	612	26	550	22	561	28	1221	27	1867	15	1218	21	1256	20	315	37	400	31	1379	25	106	45	63	45	36	62	58	77	252	56	273	35	228	46	450	35	198	37	290	39	496	24	246	42	530	34	372	50	93	55	192	40	224	44	225	34	201	34	438	39	323	35	63	43	99	53	189	57	95	36	440	35	447	43	205	33	346	33	538	26	253	25	285	31	416	25	210	33	148	54	361	36	221	35	331	39	387	39	146	48	11	78	492	43	6	60	69	42	74	41	105	43	107	41	242	48	61	54	82	58	59	71	40	62	1379	17	1101	20	1540	16	149	49	354	48	168	42	258	40	105	45	69	49	19	52	44	65	0	.	35956	3

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 2279475. Birmensdorf, Swiss Federal Research Institute WSL

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