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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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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NFI4
growing stock (stemwood)
ownership (2 categories) · 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 2009/17
		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
ownership (2 categories)	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	.
public	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	220	35	146	49	287	45	129	30	1	1351	111	58	315	30	160	36	567	20	594	20	79	48	65	62	158	31	0	.	94	62	153	38	218	30	141	40	16	83	162	36	274	31	233	23	258	31	1028	17	843	16	625	18	128	42	93	33	150	36	106	46	105	40	67	35	84	42	92	41	124	55	172	34	237	37	110	51	105	39	236	33	213	37	44	44	266	29	192	32	162	41	117	49	89	39	180	38	169	42	62	64	166	40	125	54	52	49	171	44	310	31	125	30	320	27	83	54	139	36	492	24	313	32	234	34	149	43	78	73	184	36	162	38	94	42	84	60	84	72	17	52	200	33	49	51	86	48	189	31	114	51	174	39	71	38	105	60	49	62	8	100	9	73	621	19	621	24	626	20	61	43	88	61	28	196	24	58	120	63	82	45	29	44	140	48	0	.	17057	3
	21-30 cm	306	30	439	41	269	32	290	38	71	49	279	43	724	22	503	24	893	21	1614	18	90	50	250	39	339	32	2	.	204	55	480	37	597	36	315	48	72	74	688	33	779	24	358	26	417	26	1756	16	1632	14	888	18	203	37	269	40	243	33	100	49	139	38	159	42	114	43	136	56	288	44	384	38	597	30	93	65	220	45	382	31	351	44	225	41	816	27	215	36	298	35	149	43	276	41	246	44	362	35	160	43	197	47	136	46	281	51	100	65	567	25	259	29	423	33	256	61	252	47	743	25	639	27	349	34	332	51	46	61	363	33	227	47	137	43	88	62	132	91	95	70	446	32	71	54	85	48	430	32	321	40	420	36	132	50	208	57	120	46	37	98	15	100	1072	19	1004	25	1382	19	192	45	182	44	100	54	135	58	140	62	222	40	171	45	279	40	0	.	33469	3
	31-40 cm	354	32	254	40	489	32	405	29	402	43	454	44	1157	22	994	24	961	20	1970	18	108	56	318	35	547	35	23	.	379	45	514	35	519	31	510	32	125	76	621	39	1124	21	744	23	679	28	2582	15	2887	15	1956	19	227	34	666	42	418	50	146	48	191	38	203	50	245	45	256	33	545	38	357	36	682	28	157	66	339	44	789	28	489	32	310	45	952	24	434	36	655	31	337	37	395	37	364	31	410	36	528	36	122	71	151	41	171	39	192	47	522	27	553	35	234	32	114	41	189	45	561	24	307	31	419	34	309	42	78	66	514	38	292	33	317	51	176	52	181	59	82	90	583	31	126	81	194	44	485	29	263	32	503	32	297	42	243	51	101	62	234	55	7	100	1411	20	1209	20	1570	18	201	42	192	43	258	52	179	57	303	43	225	41	483	43	250	35	0	.	45978	3
	41-50 cm	442	32	213	39	386	39	875	25	399	40	671	44	1494	26	821	23	1587	18	2074	16	158	64	280	48	411	42	38	.	381	50	530	36	514	38	546	36	34	78	1105	25	1391	20	798	22	1105	27	2590	14	3237	13	2348	18	399	33	436	35	245	39	223	48	201	50	288	40	256	41	332	37	295	46	286	37	737	26	303	45	296	46	620	30	203	45	222	43	1177	26	378	33	739	34	421	35	453	34	562	32	424	34	516	32	159	82	233	53	358	42	98	54	548	23	786	34	127	47	66	46	108	66	443	27	321	31	259	37	189	57	75	58	521	41	436	53	362	41	209	45	122	57	140	72	594	29	65	73	181	43	472	30	243	40	670	30	479	31	286	55	242	50	153	69	15	136	1212	18	1270	21	1588	19	156	56	174	46	286	41	58	59	254	37	166	47	343	41	306	35	0	.	49210	3
	51-60 cm	380	40	333	35	283	42	1052	31	337	45	435	48	1134	26	1083	23	1903	20	1286	20	248	49	131	61	479	38	70	.	208	52	421	38	640	35	409	39	0	.	1058	25	1032	26	1034	23	1090	25	2472	17	2275	16	1768	18	395	38	368	44	198	40	136	60	248	73	242	68	199	46	418	40	131	63	236	52	561	35	315	48	100	47	634	35	755	37	414	41	632	30	740	37	244	45	469	54	292	39	533	30	529	40	596	34	45	71	318	53	399	37	241	45	750	29	427	42	228	34	32	128	18	100	331	34	235	41	255	39	136	71	131	59	326	41	366	45	596	43	352	73	139	71	0	.	440	32	77	100	199	50	330	30	516	37	445	41	598	43	230	54	106	59	260	52	114	77	1238	21	970	24	1916	19	122	61	365	49	225	45	102	100	364	41	347	59	192	46	397	39	0	.	45821	3
	61-70 cm	223	47	195	49	124	51	711	31	154	56	256	63	785	37	620	34	982	27	422	36	154	60	176	68	125	78	0	.	102	74	278	51	368	61	315	40	0	.	442	38	503	34	620	28	720	29	1232	19	971	22	682	29	384	49	277	47	150	52	141	60	112	58	66	100	113	74	328	48	91	72	71	82	331	43	200	46	84	71	328	45	246	45	353	51	500	40	294	44	108	58	211	50	108	59	666	34	129	62	210	55	34	100	74	100	134	51	195	69	284	40	324	42	105	63	27	100	82	100	263	40	74	58	293	60	123	61	128	61	131	60	201	48	211	52	66	71	105	59	0	.	547	46	50	72	146	78	200	39	184	64	30	100	164	49	35	100	163	60	0	.	141	100	609	36	340	35	751	28	43	100	449	44	102	100	44	100	82	72	116	75	104	75	195	61	0	.	24010	5
	71-80 cm	141	59	89	71	209	60	204	58	100	100	44	100	222	53	372	40	603	38	87	71	0	.	90	70	0	.	44	.	0	.	131	71	102	71	154	64	0	.	502	42	411	50	203	44	343	37	767	28	466	30	359	38	56	100	205	65	45	100	129	74	44	100	90	71	197	59	42	100	0	.	147	66	214	51	96	71	86	58	203	73	619	42	331	60	96	71	207	51	0	.	63	100	0	.	465	43	0	.	211	84	0	.	104	100	47	100	175	52	235	51	163	55	29	100	0	.	0	.	172	46	77	58	123	55	101	58	92	100	134	77	68	74	71	71	63	71	0	.	0	.	88	71	45	100	53	100	90	72	83	100	98	71	137	61	0	.	0	.	111	100	123	100	904	28	131	51	425	38	28	100	93	71	0	.	0	.	0	0	0	.	90	71	43	100	0	.	13388	7
	81-90 cm	41	100	53	100	87	71	161	73	0	.	0	.	105	72	251	45	238	51	175	72	0	0	58	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	179	52	44	100	187	52	260	42	546	33	304	42	41	100	0	.	0	.	0	.	0	.	62	100	0	.	0	.	0	.	0	.	0	.	0	.	79	100	0	.	0	.	185	58	0	.	0	.	0	.	52	100	0	.	0	.	66	100	0	.	60	100	0	.	0	.	194	71	0	.	165	60	51	100	26	100	29	100	0	.	26	100	0	.	0	.	0	.	0	.	46	100	100	75	52	100	49	100	37	100	0	.	0	.	51	100	0	.	67	100	0	.	0	.	72	100	0	.	0	.	81	100	0	.	236	47	170	53	131	50	0	.	113	72	50	100	47	100	80	100	100	71	85	71	0	.	0	.	5290	10
	91-100 cm	0	.	0	.	0	.	54	100	0	.	0	.	34	100	79	100	143	100	65	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	144	59	92	100	0	.	61	100	69	100	61	100	0	.	0	.	0	.	0	.	0	.	84	100	72	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	81	100	0	.	0	.	0	.	0	.	0	.	65	100	0	.	41	100	0	.	0	.	0	.	37	100	53	100	0	.	0	.	0	.	0	.	75	100	0	.	0	.	0	.	47	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	100	0	.	0	.	28	100	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1494	22
	>100 cm	0	.	67	100	0	.	86	100	0	.	0	.	0	.	200	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	0	.	0	.	127	71	191	61	52	100	67	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	163	100	117	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1285	26
	total	2108	23	1790	26	2133	26	3966	21	1465	40	2252	37	5970	20	5082	19	7879	14	8288	14	836	39	1367	33	2058	30	177	.	1369	39	2507	29	2958	26	2390	30	247	69	4952	22	5650	18	4177	19	5060	19	13233	11	12728	11	8734	14	1792	30	2315	33	1448	33	980	40	1185	39	1186	37	1208	37	1604	31	1476	37	1653	33	3359	22	1351	37	1231	36	3229	25	3061	28	1980	37	4439	19	2461	27	2258	25	1768	30	1613	30	3147	25	2022	30	2384	30	722	47	1143	39	1637	33	1373	33	3551	21	2687	26	1493	24	608	43	788	36	3106	18	1966	23	1931	28	1339	39	675	47	2218	31	1851	33	1879	33	1086	45	800	47	333	60	2898	24	533	60	944	36	2264	23	1724	31	2340	26	1948	29	1106	46	841	39	883	55	423	70	7369	13	5764	16	8443	14	803	39	1655	34	1049	36	590	53	1343	33	1257	38	1497	35	1610	29	0	.	237002	2
private	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	10	253	22	66	0	.	24	50	39	58	96	40	852	16	548	16	555	25	127	38	8	71	26	72	29	69	0	.	25	78	116	33	175	40	155	66	6	77	41	73	92	34	109	43	216	42	66	38	79	56	126	29	222	39	354	31	358	22	37	58	133	46	8	100	18	71	82	58	87	49	32	.	23	56	63	41	119	72	106	47	47	65	114	37	60	61	17	69	0	.	17	82	38	48	98	55	40	43	33	98	136	35	176	46	210	41	28	71	0	.	17	78	92	78	211	33	560	28	6	100	101	72	212	42	66	47	30	63	50	71	57	54	81	52	85	45	69	54	48	81	42	63	0	.	0	.	47	53	41	80	33	74	42	53	50	78	40	94	0	.	36	84	269	34	294	28	178	38	15	75	55	62	55	58	160	32	42	38	21	83	51	80	25	94	0	.	9378	5
	21-30 cm	85	55	4	400	0	.	222	79	242	51	325	35	1513	15	1303	20	1214	19	408	44	14	100	55	71	69	66	0	.	47	71	411	41	241	44	344	44	48	72	171	45	61	64	279	47	246	37	203	53	177	45	322	30	356	30	406	27	785	24	127	91	207	39	0	.	50	71	251	43	276	44	70	.	115	59	297	38	212	54	181	65	185	62	389	30	206	58	25	100	20	.	63	73	248	56	103	64	15	84	103	55	375	35	202	67	342	39	33	71	0	.	75	75	73	77	492	33	714	23	0	.	201	83	299	34	289	49	128	67	41	75	115	49	84	61	387	62	161	49	48	100	109	52	16	.	0	.	78	59	141	71	47	63	109	61	149	52	95	85	0	.	86	71	429	33	495	28	247	35	70	58	70	83	73	48	512	36	306	34	127	88	32	78	50	71	0	.	19994	5
	31-40 cm	223	37	129	56	0	.	236	60	325	46	745	31	2766	13	1762	16	1476	19	659	31	3	537	63	100	112	74	0	.	82	71	500	38	401	41	482	36	81	71	225	40	443	31	220	46	382	35	350	47	128	57	765	28	610	30	694	32	968	23	110	73	451	47	37	72	80	77	296	36	304	47	36	.	244	52	392	35	452	42	210	51	209	56	737	28	172	57	129	91	39	.	104	77	219	49	120	48	254	48	184	70	719	35	338	40	418	40	11	100	0	.	37	72	46	79	434	34	600	24	0	.	97	69	308	56	195	53	249	59	31	72	175	60	133	53	291	46	180	61	70	79	189	50	9	.	0	.	131	52	67	62	85	61	207	55	73	51	70	83	0	.	102	58	571	26	342	31	322	39	33	74	237	56	162	46	511	31	721	30	189	54	67	76	0	.	0	.	28025	4
	41-50 cm	488	35	97	53	0	.	385	49	448	42	793	31	3625	14	2264	15	2241	17	742	35	15	100	103	100	138	54	0	.	90	83	556	38	575	41	495	34	145	78	417	42	862	34	218	46	597	34	263	44	92	60	1059	24	828	34	1021	26	1757	22	124	59	479	39	109	78	93	90	638	34	172	44	14	.	271	61	456	42	488	44	96	45	367	52	1127	27	162	56	61	71	52	.	62	71	270	45	244	54	273	47	220	65	427	34	286	47	246	35	31	71	0	.	90	71	8	100	286	30	383	38	21	100	52	58	232	41	184	47	271	66	132	75	220	54	142	55	296	49	189	58	136	59	115	53	120	.	0	.	195	49	49	72	124	92	196	58	133	59	134	81	0	.	132	65	667	38	363	36	429	38	165	68	197	64	295	44	774	26	839	30	213	47	167	75	72	83	0	.	35100	4
	51-60 cm	285	50	113	68	0	.	311	48	369	51	874	37	3552	16	2762	16	2111	19	418	50	21	100	0	.	159	60	0	.	20	100	425	37	371	39	623	31	52	100	191	63	585	30	91	54	673	37	287	44	247	71	594	28	715	47	1023	27	1507	26	84	51	328	47	97	100	63	89	371	38	323	52	0	.	325	55	494	49	153	52	193	47	347	48	1017	28	25	100	153	71	0	.	113	100	79	63	71	80	280	47	101	55	471	41	332	53	209	37	19	100	0	.	28	100	0	.	160	45	208	52	8	100	43	100	144	55	225	45	67	73	16	100	114	71	53	73	273	40	160	55	42	71	119	62	44	.	0	.	35	72	40	100	67	100	184	66	41	71	53	71	0	.	176	58	461	35	351	43	223	47	120	72	151	65	150	46	714	35	459	38	312	58	144	73	125	81	0	.	29542	5
	61-70 cm	202	52	37	100	0	.	245	58	222	59	472	45	1608	21	1675	21	1301	23	125	64	88	100	0	.	96	74	0	.	28	100	157	54	383	49	79	59	35	100	95	58	317	38	67	62	235	41	109	56	53	100	349	38	536	44	792	33	709	33	138	78	87	75	68	100	0	.	356	35	118	51	31	.	164	77	96	71	85	100	247	63	143	82	626	37	69	100	143	71	0	.	39	100	0	.	25	100	100	76	135	58	84	58	144	50	195	76	30	100	0	.	0	.	19	100	128	47	133	48	15	100	0	.	37	80	88	59	52	72	33	100	108	61	89	71	303	65	66	72	0	.	0	.	0	.	0	.	0	.	76	100	0	.	45	100	26	100	87	74	0	.	62	72	359	58	285	37	95	79	91	100	168	100	33	100	477	36	150	61	70	71	0	.	77	100	0	.	16311	6
	71-80 cm	61	100	49	100	0	.	249	52	49	100	85	100	650	25	1167	24	669	32	65	73	60	100	0	.	81	71	0	.	0	.	69	83	167	74	35	100	0	.	43	100	437	33	54	100	36	100	0	.	0	.	161	58	179	83	153	58	360	53	0	.	40	100	0	.	0	.	55	100	0	.	0	.	119	71	236	66	107	71	188	56	84	71	416	40	37	100	59	100	0	.	0	.	0	.	0	.	51	100	0	.	67	71	114	71	127	75	31	100	0	.	9	100	0	.	83	59	15	100	31	100	24	100	42	71	94	61	0	.	37	100	82	73	51	71	0	.	85	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	40	100	94	59	23	100	76	58	0	.	0	.	51	100	82	71	40	100	95	71	0	.	0	.	0	.	7685	9
	81-90 cm	0	.	0	.	0	.	63	100	0	.	0	.	471	44	368	49	256	50	0	.	0	.	0	.	0	.	0	.	0	.	71	100	91	72	0	.	0	.	23	100	111	72	39	100	35	100	66	72	0	.	104	71	0	.	0	.	0	.	56	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	100	0	.	282	100	0	.	66	100	0	.	0	.	0	.	0	.	53	100	32	100	56	100	116	100	65	100	0	.	0	.	16	100	0	.	0	.	27	100	0	.	0	.	27	100	0	.	0	.	0	.	0	.	0	.	0	.	65	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	100	170	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2813	18
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	149	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	100	53	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	.	168	100	0	.	0	.	0	.	0	.	0	.	0	0	29	100	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	100	0	.	0	.	114	71	46	100	0	.	0	.	0	.	46	100	0	.	0	.	0	.	0	.	0	.	0	.	750	33
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	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	.	43	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	166	100	0	.	183	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	564	40
	total	1354	33	451	43	0	.	1734	40	1693	42	3390	28	15037	12	11849	13	10041	14	2545	28	209	79	248	87	685	51	0	.	293	76	2304	32	2405	29	2281	28	367	75	1206	39	2907	26	1078	40	2420	29	1343	36	776	46	3549	22	3499	27	4445	21	6444	21	676	51	1725	33	319	74	304	70	2048	31	1280	38	182	.	1261	51	2033	34	1615	37	1264	38	1381	44	4708	23	730	48	652	58	110	.	398	70	854	44	661	47	1064	44	850	47	2334	29	1876	38	1811	29	184	71	0	.	273	71	238	73	1793	27	2705	20	248	76	519	58	1504	33	1140	40	797	59	341	71	872	46	633	45	1635	41	976	52	345	60	573	46	190	.	0	.	486	49	414	60	355	70	784	51	472	52	556	72	0	.	676	58	3134	27	2199	25	1569	32	494	61	878	54	866	37	3229	24	2557	27	1027	46	461	64	350	71	0	.	150163	3
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	230	35	168	43	287	45	153	26	40	71	207	36	1166	14	708	15	1123	16	722	18	87	44	91	49	187	28	0	.	120	52	270	26	394	24	296	40	22	64	204	32	365	25	342	21	475	25	1094	16	922	16	752	15	350	29	448	25	508	19	143	37	238	31	75	33	101	36	174	35	211	38	204	33	259	34	173	36	223	42	342	27	259	32	158	29	326	26	209	30	162	41	134	44	127	31	277	31	209	35	95	54	301	27	301	35	262	34	199	39	310	31	142	28	412	27	294	28	698	23	497	24	415	30	446	27	215	33	108	55	234	32	219	31	175	33	169	38	153	47	65	61	242	29	49	51	86	48	236	27	155	43	207	35	113	31	155	48	89	54	8	100	45	68	890	17	916	18	804	18	76	37	142	44	83	76	184	28	162	48	103	40	80	54	164	43	0	.	26435	3
	21-30 cm	392	26	444	40	269	32	511	40	313	41	604	27	2237	12	1806	16	2107	14	2022	17	105	45	306	34	408	29	2	.	251	46	891	27	837	29	659	33	121	52	858	28	840	23	637	25	663	21	1959	15	1809	13	1210	15	559	23	675	23	1028	20	227	55	346	27	159	42	164	37	387	34	564	31	454	35	712	27	390	33	432	35	563	29	536	36	613	24	1022	25	240	34	318	34	212	37	524	34	349	36	377	34	263	34	572	28	338	44	623	31	133	52	567	25	334	28	496	30	748	30	966	20	743	25	840	28	648	24	621	35	174	52	405	31	342	35	221	35	475	52	293	49	143	57	555	28	87	48	85	48	508	28	462	35	467	33	241	39	356	39	215	45	37	98	101	63	1501	16	1499	19	1629	17	262	37	252	39	174	37	647	31	447	30	349	41	203	40	330	36	0	.	53463	2
	31-40 cm	577	24	383	32	489	32	640	29	727	31	1199	25	3923	11	2756	13	2437	14	2628	15	111	56	381	34	659	31	23	.	461	39	1014	26	920	25	993	24	206	53	846	31	1567	17	965	20	1061	22	2932	14	3015	14	2721	15	838	24	1361	26	1386	22	255	42	641	34	240	44	325	38	552	24	849	30	393	34	926	25	548	31	791	30	999	25	698	28	1048	24	1124	22	563	35	693	29	442	34	614	29	484	26	663	29	712	32	841	31	489	30	589	30	203	45	522	27	590	33	281	30	548	28	789	21	561	24	404	28	728	31	504	33	327	48	546	36	467	30	450	39	467	35	361	42	152	60	771	26	135	76	194	44	616	25	329	28	588	28	504	33	316	41	171	50	234	55	108	55	1983	16	1552	17	1893	16	233	38	429	37	420	36	690	27	1024	25	414	33	550	39	250	35	0	.	74003	2
	41-50 cm	930	24	310	31	386	39	1260	23	848	29	1464	26	5119	12	3084	13	3828	12	2817	15	172	59	383	44	548	34	38	.	472	43	1086	26	1089	28	1040	25	178	65	1522	21	2253	17	1016	20	1701	21	2853	13	3329	13	3407	14	1227	25	1457	21	2002	20	347	37	680	31	397	36	348	39	970	25	467	33	300	35	1008	25	758	31	785	33	716	27	569	37	1349	24	1339	24	440	30	791	32	482	32	723	27	806	28	696	28	735	29	586	33	519	35	604	28	129	44	548	23	876	32	135	45	352	26	491	32	465	26	373	27	491	27	373	37	347	53	652	36	656	40	505	33	505	34	311	42	276	46	709	25	185	70	181	43	667	26	292	36	794	29	674	28	418	42	375	43	153	69	147	60	1878	18	1633	18	2017	17	321	44	371	40	582	30	832	25	1093	24	378	33	510	37	378	32	0	.	84310	2
	51-60 cm	664	31	446	31	283	42	1363	26	706	34	1309	29	4686	13	3845	13	4013	14	1704	19	269	46	131	61	638	32	70	.	228	49	846	26	1011	26	1032	24	52	100	1249	23	1617	20	1125	22	1763	21	2759	16	2523	16	2363	15	1110	33	1392	23	1705	24	220	42	576	41	339	56	263	41	789	27	454	41	236	52	886	30	809	35	253	36	827	29	1102	30	1431	23	656	29	893	33	244	45	582	47	371	33	604	28	809	31	697	30	516	38	650	37	608	27	260	42	750	29	454	40	228	34	192	43	226	49	339	33	278	38	399	31	361	38	198	46	342	39	480	38	650	40	625	45	299	44	42	71	560	28	121	73	199	50	365	28	557	35	512	38	782	37	271	47	159	46	260	52	290	46	1699	18	1321	21	2139	17	242	47	516	39	375	32	816	33	823	28	658	41	336	41	523	36	0	.	75363	2
	61-70 cm	425	35	232	44	124	51	956	27	376	41	728	37	2393	18	2295	18	2284	17	547	31	242	53	176	68	221	54	0	.	130	62	435	38	751	39	394	34	35	100	537	33	821	25	687	26	955	24	1340	18	1024	22	1030	23	920	33	1069	27	859	29	280	49	199	46	134	71	113	74	684	29	209	42	101	65	495	39	296	38	169	62	575	37	389	41	979	30	568	37	437	38	108	58	250	45	108	59	691	33	229	48	344	40	118	50	219	47	329	50	225	61	284	40	324	42	124	56	156	43	215	48	278	38	74	58	330	54	210	43	180	48	164	52	309	38	299	42	369	55	171	45	0	.	547	46	50	72	146	78	200	39	260	54	30	100	209	44	61	71	251	47	0	.	203	73	968	31	626	25	846	26	134	75	617	42	136	79	521	34	232	47	186	53	104	75	272	52	0	.	40321	4
	71-80 cm	202	51	138	58	209	60	453	39	149	75	130	74	873	23	1540	20	1272	25	152	51	60	100	90	70	81	71	44	.	0	.	199	55	269	53	190	56	0	.	545	39	848	30	257	40	379	35	767	28	466	30	520	32	234	67	358	45	405	48	129	74	84	71	90	71	197	59	97	71	0	.	147	66	333	42	331	51	193	47	391	46	703	38	747	34	132	58	265	45	0	.	63	100	0	.	465	43	51	100	211	84	67	71	218	60	175	61	207	47	235	51	173	52	29	100	83	59	15	100	204	42	101	50	165	45	195	42	92	100	171	64	151	52	122	51	63	71	85	71	0	.	88	71	45	100	53	100	90	72	83	100	98	71	137	61	0	.	20	100	111	100	163	79	999	26	154	45	501	34	28	100	93	71	51	100	82	71	40	100	95	71	90	71	43	100	0	.	21073	5
	81-90 cm	41	100	53	100	87	71	224	60	0	.	0	.	575	38	619	34	495	36	175	72	0	0	58	100	0	.	0	.	0	.	71	100	91	72	0	.	0	.	202	48	154	59	227	46	295	39	611	30	304	42	145	58	0	.	0	.	0	.	56	100	62	100	0	.	0	.	0	.	0	.	0	.	0	.	79	100	0	.	44	100	185	58	282	100	0	.	66	100	52	100	0	.	0	.	66	100	53	100	92	74	56	100	116	100	259	59	0	.	165	60	67	80	26	100	29	100	27	100	26	100	0	.	27	100	0	.	0	.	46	100	100	75	52	100	49	100	102	73	0	.	0	.	51	100	0	.	67	100	0	.	0	.	72	100	0	.	0	.	81	100	42	100	405	42	170	53	131	50	0	.	113	72	50	100	47	100	80	100	100	71	85	71	0	.	0	.	8103	9
	91-100 cm	0	.	0	.	0	.	54	100	0	.	0	.	34	100	79	100	292	61	65	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	144	59	92	100	0	.	61	100	69	100	61	100	69	100	53	100	0	.	0	.	0	.	84	100	72	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	81	100	0	.	0	.	0	.	0	.	0	.	65	100	0	.	41	100	0	.	168	100	0	.	37	100	53	100	0	.	0	.	0	0	29	100	75	100	0	.	20	100	0	.	47	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	116	71	0	.	0	.	142	60	94	71	0	.	0	.	0	.	46	100	0	.	0	.	0	.	0	.	0	.	0	.	2244	18
	>100 cm	0	.	67	100	0	.	86	100	0	.	0	.	0	.	200	73	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	100	0	.	51	100	0	.	0	.	127	71	191	61	52	100	67	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	163	100	117	100	0	.	0	.	0	.	36	100	166	100	0	.	183	59	0	.	0	.	0	.	0	.	38	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1849	22
	total	3462	19	2241	23	2133	26	5700	19	3158	29	5641	22	21007	10	16932	10	17920	10	10833	12	1046	35	1615	31	2743	25	177	.	1662	34	4812	21	5363	19	4671	20	614	53	6159	19	8558	14	5255	17	7480	16	14576	11	13504	10	12284	12	5290	21	6759	18	7893	18	1656	32	2911	24	1505	33	1512	32	3653	22	2756	27	1835	31	4620	21	3385	25	2846	26	4492	21	4442	23	6688	19	5169	18	3113	24	2368	25	2166	28	2467	24	3809	22	3086	24	3234	25	3056	25	3018	28	3448	21	1557	30	3551	21	2960	25	1731	23	2401	23	3493	17	3354	18	2485	22	3435	21	2479	28	1472	39	2559	28	2723	27	2512	27	2721	30	1776	36	679	42	3471	22	722	51	944	36	2750	21	2138	27	2696	24	2732	25	1578	36	1397	37	883	55	1099	45	10503	12	7963	14	10012	13	1297	33	2534	29	1915	26	3819	22	3900	21	2284	29	1958	30	1960	27	0	.	387165	1

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. 2300027. Birmensdorf, Swiss Federal Research Institute WSL

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