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
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
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
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	276	29	208	32	175	45	90	36	19	44	113	56	316	31	414	27	616	21	535	20	144	46	108	47	127	43	2	.	43	57	115	42	255	29	185	40	17	71	238	25	393	22	232	23	468	21	1210	15	933	16	491	20	146	35	79	36	116	38	72	55	71	40	112	45	80	48	108	41	107	50	219	33	330	43	86	62	104	40	327	34	124	45	80	44	217	31	229	38	153	41	178	41	122	40	214	30	117	41	109	56	97	46	72	54	36	45	213	33	409	26	194	28	405	26	80	44	144	35	579	21	338	29	258	26	116	58	153	42	125	37	191	32	121	42	161	54	31	60	31	54	204	33	93	56	113	47	142	31	101	38	174	49	163	50	60	52	42	59	21	61	59	86	810	20	547	20	659	17	44	46	50	44	43	59	89	45	228	40	73	45	125	60	41	41	0	.	18855	3
	21-30 cm	346	27	289	35	320	35	251	32	49	61	359	46	750	24	581	24	841	20	1293	19	110	46	162	42	343	35	0	.	107	42	394	38	646	30	256	47	18	100	323	35	811	22	660	23	838	20	2155	14	1834	14	883	19	186	41	279	40	192	42	90	69	146	35	240	40	173	41	179	69	247	59	357	42	580	28	169	46	221	40	494	32	300	53	285	40	431	26	299	31	213	38	187	38	268	39	405	37	527	32	100	40	265	39	168	49	96	62	123	41	800	26	257	32	467	33	212	41	293	40	1127	20	676	25	538	28	341	40	119	41	369	33	270	39	173	35	223	49	131	79	80	74	290	37	94	60	103	64	362	26	311	35	296	39	91	51	159	51	107	74	47	89	-6	100	1273	17	1101	24	1450	18	216	41	125	37	86	68	117	52	187	63	273	47	87	49	139	45	0	.	34824	3
	31-40 cm	346	33	431	34	243	45	248	37	465	43	449	38	724	24	919	23	1117	20	1928	18	106	58	262	39	440	33	10	.	341	50	335	34	816	25	639	36	65	82	595	35	1266	20	870	23	915	26	2766	14	2839	13	1584	20	308	35	530	45	290	41	77	59	291	53	126	50	165	57	181	41	499	48	437	32	886	30	152	47	380	37	790	28	390	43	451	35	822	26	535	37	576	34	169	36	389	39	472	27	466	42	388	34	286	57	97	55	143	40	232	43	1011	45	427	37	425	28	227	42	310	36	727	23	502	27	465	28	338	45	118	61	389	37	406	31	218	39	141	55	266	51	50	83	674	28	162	78	160	50	479	29	328	30	434	37	195	50	184	43	126	46	184	64	0	0	1508	19	1319	20	1814	16	318	36	181	47	118	47	161	51	142	56	182	42	171	48	237	37	0	.	46347	3
	41-50 cm	457	29	170	40	502	33	493	28	372	39	450	41	1015	26	1041	25	1131	19	1947	16	89	65	264	41	330	42	23	.	415	44	512	35	632	31	752	37	61	84	1212	25	1640	20	914	21	1093	24	2913	12	3535	13	1916	19	256	40	544	33	312	40	117	59	317	47	290	37	227	45	285	35	386	47	334	32	813	28	327	54	351	40	753	25	255	42	233	38	1009	24	427	32	489	31	366	38	370	33	689	31	479	33	667	31	226	83	130	59	242	49	227	42	1033	34	594	30	193	33	97	40	252	56	653	22	471	32	469	30	203	49	104	47	469	41	453	48	684	40	210	51	155	48	64	100	665	29	64	78	236	44	460	28	331	31	631	30	324	36	283	51	164	62	129	64	0	.	1488	19	1418	19	1795	17	310	43	256	40	260	47	97	65	274	43	141	42	206	50	260	41	0	.	50894	3
	51-60 cm	338	34	291	36	270	43	792	29	369	47	603	44	881	27	977	24	1776	18	1507	20	269	51	185	50	330	47	82	.	368	47	423	34	631	32	545	38	0	.	1051	24	1253	22	1048	24	1197	25	2432	16	2559	14	1792	19	528	32	361	47	253	48	152	52	323	45	250	45	121	54	354	45	62	58	305	43	634	32	340	51	226	41	887	29	413	36	255	42	543	30	612	36	106	43	414	43	376	37	719	27	616	38	669	31	63	71	197	53	329	42	316	36	1004	41	442	37	201	35	66	65	42	60	517	27	343	33	442	34	206	61	156	48	325	42	402	47	424	35	199	50	226	46	105	100	480	29	107	100	58	73	354	31	391	39	422	35	395	41	145	69	243	59	162	59	52	100	1314	18	1096	23	1712	21	240	44	336	46	215	44	25	100	190	48	272	55	153	45	251	40	0	.	46104	3
	61-70 cm	231	54	203	49	195	53	385	40	262	55	185	60	656	31	669	30	1037	22	479	31	72	100	117	58	102	59	75	.	69	71	251	51	188	46	218	38	0	.	603	33	738	26	829	25	768	31	1646	19	1218	24	1047	26	326	66	337	49	130	59	31	100	109	100	180	72	166	62	333	56	213	71	39	71	304	40	296	48	172	62	270	47	334	52	554	48	557	30	466	44	28	100	102	77	183	52	507	37	111	74	233	62	71	71	136	50	210	52	193	39	353	35	298	37	181	45	0	.	61	100	367	31	66	58	218	53	111	60	177	52	125	61	223	63	486	47	92	72	122	59	34	100	625	48	73	77	102	58	213	41	302	49	190	49	295	48	91	75	130	51	0	.	46	100	787	22	438	34	1105	24	0	.	255	54	180	53	126	82	258	52	29	100	63	100	181	41	0	.	27238	4
	71-80 cm	34	100	38	100	200	45	371	37	43	100	107	71	486	44	452	37	450	35	311	38	0	0	141	73	164	74	0	.	34	100	77	71	133	61	170	61	0	.	584	40	261	48	360	43	461	34	794	24	599	26	422	46	122	75	135	74	30	100	128	58	181	50	0	.	140	74	97	71	34	100	98	58	244	47	228	50	67	71	184	51	474	49	185	63	226	54	251	53	35	100	89	100	0	.	442	34	79	71	281	77	0	.	88	100	40	100	256	65	294	62	161	57	25	100	45	100	82	59	154	45	132	52	171	54	70	72	108	100	266	63	159	52	258	43	70	71	79	71	0	.	87	73	0	.	38	100	89	71	136	76	46	100	28	100	0	.	124	77	0	.	73	100	896	32	221	50	659	30	89	71	147	58	64	100	55	100	0	0	0	.	129	60	42	100	0	.	15823	6
	81-90 cm	66	100	52	100	215	62	118	71	49	100	0	.	143	73	200	50	151	58	122	100	0	.	0	.	0	.	61	.	0	.	98	100	0	.	135	71	0	.	210	47	293	60	167	50	231	47	562	41	374	44	104	71	0	.	126	71	0	.	159	73	69	100	53	100	122	71	0	.	0	.	105	100	40	100	0	.	45	100	68	100	83	71	189	62	0	.	52	100	62	100	0	.	0	.	112	72	0	.	0	.	0	.	0	.	176	75	79	100	180	60	0	.	28	100	32	100	0	.	184	46	28	100	89	71	0	.	0	.	53	100	92	75	0	0	0	.	46	100	0	.	0	.	0	.	0	.	0	.	0	.	48	100	49	100	0	.	0	.	0	.	78	100	476	38	32	100	36	100	0	.	135	71	59	100	49	100	0	.	60	100	37	100	0	.	0	.	6680	10
	91-100 cm	0	.	54	100	0	.	117	71	0	.	0	.	0	.	61	100	98	71	127	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	232	51	56	100	182	58	65	100	308	45	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	114	100	0	.	75	100	58	100	0	.	0	.	113	100	0	.	0	.	0	.	68	100	0	.	55	100	0	.	0	.	109	71	0	.	143	73	0	.	38	100	0	.	56	100	77	100	0	.	0	.	0	.	0	.	0	.	0	.	48	100	57	100	0	.	0	.	0	.	0	.	0	.	81	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	125	59	90	71	78	58	0	.	60	100	0	.	0	.	0	.	45	100	0	.	0	.	0	.	2836	15
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	36	100	217	73	66	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	0	.	0	.	155	71	233	52	52	100	81	100	0	.	0	.	0	.	0	.	70	100	0	.	0	.	0	0	0	.	0	.	0	.	0	.	50	100	38	100	0	.	61	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	100	129	100	148	79	0	.	0	.	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	.	39	100	0	.	64	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1634	22
	total	2093	23	1735	26	2121	26	2864	24	1628	41	2267	36	5007	21	5530	18	7283	14	8249	14	790	37	1239	33	1835	32	252	.	1377	39	2204	29	3301	23	2900	29	162	80	5099	20	6712	16	5263	17	6190	17	15017	10	13991	10	8320	15	1871	27	2390	32	1324	36	826	42	1576	36	1251	36	1192	39	1538	33	1548	42	1892	31	3832	22	1714	36	1617	32	3887	22	2432	31	2294	34	3806	21	2982	25	1661	29	1505	28	1707	30	3629	24	2395	27	2503	29	1008	44	887	37	1476	34	1768	29	5376	28	2373	24	1963	21	760	34	1240	32	4384	16	2556	21	2650	24	1385	36	937	38	2120	30	2196	31	2460	30	1153	37	1057	39	363	59	3025	24	592	59	809	34	2181	24	1900	30	2241	26	1540	30	922	41	936	37	544	60	302	87	8716	13	6262	16	9372	13	1217	35	1545	33	1023	33	718	43	1279	31	1075	33	972	32	1150	34	0	.	251235	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	80	24	52	0	.	74	50	53	65	43	62	1094	14	512	15	793	25	171	40	6	72	39	71	83	44	0	.	14	71	180	35	85	87	180	74	12	73	23	47	147	30	83	43	147	36	97	46	133	45	133	29	227	41	326	35	424	23	27	68	114	36	5	100	35	72	99	45	128	52	21	.	50	45	45	96	73	58	140	43	23	54	155	29	80	58	21	80	0	.	21	82	19	144	130	69	35	57	43	57	82	61	131	54	153	33	14	.	5	72	20	59	74	59	233	37	447	27	42	62	27	74	266	35	62	47	27	71	43	49	39	44	81	53	104	52	28	103	38	80	79	56	5	82	0	.	37	53	36	86	23	90	56	53	37	84	4	100	0	.	65	86	338	31	287	24	266	30	23	75	38	54	64	49	153	38	81	58	6	76	7	74	85	51	0	.	9987	5
	21-30 cm	40	60	25	80	0	.	252	52	239	43	272	43	1849	13	1247	19	1269	21	332	36	9	100	56	74	120	44	0	.	34	85	349	40	325	53	303	41	77	90	180	59	250	40	403	49	381	35	267	36	172	46	318	32	372	40	657	28	703	23	65	80	256	41	6	100	30	100	334	40	399	40	64	.	127	49	325	36	280	52	270	56	208	72	369	28	190	54	41	79	6	.	43	82	241	49	63	69	34	53	114	68	247	32	134	53	363	44	26	.	32	76	102	71	119	56	402	34	808	27	245	71	144	76	374	37	236	47	151	55	154	53	114	42	14	213	452	45	197	51	65	100	135	61	0	.	0	.	93	50	123	79	51	80	94	59	141	52	8	100	0	.	68	68	462	28	664	26	413	29	61	61	53	54	90	60	407	42	215	42	40	100	9	100	216	51	0	.	21683	5
	31-40 cm	98	58	90	65	0	.	263	53	394	43	503	36	3479	12	1509	19	1468	18	340	37	0	0	108	81	237	49	0	.	60	86	539	38	345	43	467	36	67	100	156	46	379	30	351	43	408	34	335	47	130	55	625	31	657	29	447	27	1081	21	145	87	295	43	17	100	119	84	241	36	363	47	54	.	307	40	419	40	452	47	239	45	354	47	729	28	250	54	120	100	9	.	74	72	200	51	67	66	297	47	151	47	533	38	224	46	317	42	-1	.	20	100	60	59	70	57	450	30	626	26	126	72	79	61	313	41	320	52	292	56	82	64	188	52	136	71	407	45	230	57	49	72	279	44	0	.	0	.	138	47	77	61	75	71	111	71	67	57	80	81	0	.	124	61	678	33	482	30	541	42	82	61	191	62	142	48	611	31	451	31	130	60	48	100	77	60	0	.	28341	4
	41-50 cm	437	46	116	65	0	.	278	51	511	42	540	35	4535	13	2086	16	1955	20	531	37	12	100	128	100	242	40	0	.	100	82	522	35	449	38	499	34	33	99	243	46	864	31	264	39	407	36	605	35	189	46	1109	26	770	30	764	27	1108	22	99	75	359	40	88	81	93	63	613	33	313	41	12	.	342	53	570	40	461	47	300	35	406	52	1193	27	174	52	111	70	41	.	118	72	439	40	105	93	198	52	236	63	472	32	320	41	242	36	13	.	43	73	72	71	16	71	257	28	462	32	45	72	95	53	238	42	240	51	421	63	176	65	234	49	175	50	331	42	277	55	132	71	130	56	11	100	0	.	239	44	67	62	95	87	239	54	111	62	27	72	0	.	156	60	778	36	443	37	484	35	211	57	191	66	252	48	606	29	862	31	68	60	103	75	87	74	0	.	34988	4
	51-60 cm	253	65	139	52	0	.	392	43	524	43	739	42	4077	13	2839	17	1845	19	383	37	26	100	20	100	146	49	0	.	75	100	464	39	490	33	573	37	52	100	182	57	775	29	148	47	601	40	503	31	165	48	584	36	838	39	993	27	1676	22	86	61	317	50	92	71	67	73	633	31	242	50	9	.	263	61	613	41	222	50	168	47	523	44	1237	24	120	53	138	71	0	.	152	100	200	48	109	72	351	51	134	62	390	45	263	56	249	40	6	.	0	.	89	100	0	.	192	38	296	50	10	100	66	74	112	60	200	49	132	62	26	100	110	51	69	66	321	43	203	55	62	71	126	47	85	80	0	.	75	51	50	100	104	81	119	72	97	63	102	79	0	.	144	62	696	31	460	35	484	39	201	59	143	72	380	51	509	37	597	36	190	57	59	74	134	53	0	.	32733	4
	61-70 cm	103	76	79	71	0	.	103	58	307	46	283	57	2427	17	1610	23	1373	23	257	45	0	.	0	.	170	53	0	.	34	100	165	47	195	52	257	43	37	100	62	71	347	36	84	59	350	46	206	43	331	54	528	32	251	44	910	35	533	36	0	.	222	46	41	100	26	100	259	47	253	45	0	.	195	79	46	100	224	100	238	60	210	71	1304	26	35	100	27	100	0	.	0	0	38	100	27	100	161	82	182	60	302	46	271	56	119	58	31	.	12	100	20	100	0	.	182	51	136	58	0	0	0	.	50	74	150	62	117	63	37	100	128	81	87	61	565	46	79	71	29	100	40	100	34	100	0	.	21	100	37	100	0	.	108	71	25	100	57	100	0	.	163	61	344	62	390	35	243	43	145	71	99	73	33	100	588	40	200	48	118	74	106	75	104	78	0	.	19663	6
	71-80 cm	131	60	37	100	0	.	371	46	95	71	104	59	1104	20	979	27	564	34	114	59	42	100	0	.	84	71	0	.	0	.	190	57	260	59	40	100	45	100	45	100	440	35	57	100	318	48	31	100	0	.	173	60	268	58	295	39	413	39	81	71	0	.	0	.	0	.	211	45	93	61	33	.	277	77	206	77	90	100	226	62	52	100	453	40	88	71	171	71	0	.	0	.	24	100	0	.	52	100	0	.	71	74	0	.	165	76	0	.	0	.	31	100	21	100	56	71	0	.	29	100	36	100	39	71	0	.	34	100	0	.	39	100	224	46	48	100	94	72	0	.	0	.	0	.	0	.	0	.	45	100	0	.	0	.	0	.	24	100	0	.	37	100	80	71	69	72	193	52	0	.	28	100	0	.	482	40	133	100	107	71	0	.	0	.	0	.	10343	8
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	138	59	388	45	457	42	63	100	0	.	0	.	0	.	0	.	0	.	77	100	199	59	0	.	0	.	0	.	178	58	46	100	0	.	70	71	0	.	0	.	0	.	112	71	44	100	0	.	46	100	0	.	0	.	0	.	0	.	0	.	0	.	264	59	70	100	95	58	52	100	255	51	0	.	0	.	0	.	0	.	0	.	0	.	84	100	39	100	0	.	185	74	120	71	0	.	0	.	93	76	0	.	41	100	27	100	0	.	0	.	27	100	60	73	0	.	43	100	0	.	59	100	0	.	75	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	100	166	61	56	72	32	100	97	100	0	.	76	100	48	100	0	.	0	.	0	.	50	100	0	.	3971	13
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	237	50	115	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	25	100	41	100	0	.	49	100	0	.	0	.	68	100	0	.	0	.	0	.	65	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	100	0	.	0	.	0	.	68	100	0	.	0	.	0	.	0	.	0	.	0	.	52	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	63	100	0	.	0	.	0	.	0	.	55	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	71	46	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1144	22
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	72	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	100	0	.	0	.	0	.	0	.	0	.	82	100	0	.	104	100	58	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	100	0	.	0	.	68	100	0	.	194	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	198	100	0	.	175	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1196	31
	total	1072	40	510	43	0	.	1732	36	2123	39	2483	33	18939	10	11284	13	9796	15	2191	27	94	71	350	84	1083	37	0	.	317	78	2486	33	2419	28	2391	28	323	71	916	41	3421	23	1434	39	2662	28	2196	28	1121	41	3643	23	3442	25	4504	21	5982	19	567	56	1607	32	250	71	370	68	2390	31	1790	32	193	.	1561	47	2489	34	1872	41	1732	33	1827	41	5696	20	937	42	699	59	56	.	408	74	1272	37	502	57	1211	43	968	46	2149	29	1723	35	1728	28	90	.	111	64	486	59	300	55	1813	27	2801	21	696	53	448	53	1630	29	1269	43	1174	52	562	54	851	43	908	42	2229	35	1181	54	374	68	789	42	252	76	0	.	603	43	436	62	348	69	727	51	479	52	302	71	0	.	796	54	3644	26	2898	23	2657	26	819	55	742	53	1037	44	3404	25	2539	28	658	52	332	70	752	42	0	.	164049	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	286	28	232	29	175	45	164	30	72	49	157	44	1409	13	926	15	1409	16	707	18	150	44	147	39	210	31	2	.	58	46	295	27	341	31	365	42	30	51	261	24	540	18	315	20	615	18	1307	14	1065	15	625	17	372	29	405	29	541	19	98	44	184	27	117	43	115	40	206	30	235	36	239	32	381	37	132	52	178	34	467	27	148	39	236	24	297	27	250	35	153	41	198	37	141	40	344	32	152	34	152	43	179	37	203	40	189	28	227	32	413	25	214	26	479	23	313	30	591	22	621	20	365	27	524	22	178	41	180	37	168	30	230	28	202	33	265	38	59	58	68	50	283	29	98	54	113	47	180	27	137	36	196	45	219	39	97	45	46	54	21	61	124	60	1148	17	834	15	925	15	66	39	88	34	107	37	242	29	309	33	78	42	131	57	126	37	0	.	28841	3
	21-30 cm	385	25	314	33	320	35	502	30	288	37	631	32	2599	12	1827	15	2110	15	1625	16	119	43	218	36	463	28	0	.	140	38	743	28	971	26	559	31	95	75	503	31	1061	19	1063	23	1219	18	2421	13	2006	13	1201	16	558	30	936	23	895	20	155	52	401	29	246	39	203	38	513	35	646	34	421	39	707	24	493	28	500	34	764	29	509	43	654	23	621	24	340	29	220	37	230	34	509	31	468	33	561	30	214	41	512	25	302	36	460	37	148	38	832	25	359	30	587	29	615	26	1101	22	1372	21	820	24	912	22	577	31	270	35	523	28	384	30	187	36	675	34	327	44	145	60	425	32	94	60	103	64	455	23	435	34	348	35	185	39	300	36	115	69	47	89	62	75	1736	14	1766	18	1864	16	277	34	178	30	176	45	524	34	402	37	313	43	96	45	355	36	0	.	56508	2
	31-40 cm	444	29	521	30	243	45	511	32	859	30	952	26	4203	10	2428	14	2585	13	2267	16	106	58	369	36	677	27	10	.	401	44	874	27	1161	22	1106	26	132	65	750	29	1645	17	1221	20	1324	20	3101	13	2969	13	2210	16	966	23	977	27	1371	19	222	60	585	33	143	46	284	48	423	27	861	34	492	30	1193	24	571	32	831	30	1029	24	743	31	1180	21	1073	24	655	36	585	34	243	33	589	31	539	25	763	31	539	28	818	32	320	36	459	32	232	43	1031	44	487	33	495	26	677	24	937	20	853	22	581	24	778	23	658	34	411	44	471	32	593	27	353	36	548	36	496	38	99	55	954	24	162	78	160	50	617	25	405	27	508	33	307	41	251	35	206	42	184	64	124	61	2186	16	1800	17	2355	15	400	31	372	39	259	34	772	27	594	27	312	35	220	43	314	31	0	.	74688	2
	41-50 cm	894	27	286	35	502	33	771	25	883	29	990	26	5550	12	3127	14	3086	15	2477	14	101	59	392	42	572	29	23	.	515	38	1033	25	1081	24	1251	26	95	64	1455	22	2504	17	1178	19	1500	20	3518	12	3724	12	3025	15	1026	24	1308	21	1420	19	216	47	676	30	379	34	320	37	898	25	699	32	345	31	1155	25	898	32	812	32	1053	20	661	36	1427	24	1183	22	538	29	529	29	484	33	808	26	794	29	677	28	903	28	698	34	450	33	484	30	240	40	1076	32	667	28	209	31	354	23	713	28	698	21	566	28	707	24	442	36	525	52	645	34	686	36	859	34	541	32	432	39	196	58	795	26	75	68	236	44	700	23	398	28	726	28	563	31	394	40	191	54	129	64	156	60	2266	17	1861	16	2278	15	521	34	447	36	511	34	703	27	1136	26	209	34	309	42	346	36	0	.	85882	2
	51-60 cm	592	34	429	29	270	43	1184	24	893	32	1342	30	4958	12	3816	14	3622	13	1889	18	295	47	205	46	476	35	82	.	443	43	887	26	1121	23	1118	27	52	100	1233	22	2029	17	1196	22	1798	21	2935	14	2724	13	2375	17	1366	27	1354	24	1930	20	238	40	639	33	343	38	187	43	987	26	304	42	314	42	896	29	953	32	449	32	1055	25	936	29	1493	21	663	26	750	32	106	43	566	41	576	29	828	25	967	30	803	28	453	40	460	39	579	30	322	35	1004	41	530	35	201	35	258	33	337	45	527	27	409	30	554	30	406	39	288	38	351	40	512	38	493	31	520	33	429	35	166	68	606	25	191	66	58	73	429	27	440	36	526	32	513	35	243	48	345	48	162	59	196	53	2011	16	1556	19	2195	18	441	36	479	39	595	36	534	35	787	29	462	40	213	38	385	32	0	.	78837	2
	61-70 cm	334	44	282	40	195	53	488	34	569	35	468	42	3083	15	2279	18	2410	16	736	26	72	100	117	58	272	40	75	.	103	58	415	36	382	35	475	29	37	100	665	30	1086	21	913	23	1118	26	1851	17	1549	22	1576	20	577	42	1246	28	663	31	31	100	330	45	221	61	191	56	592	37	466	41	39	71	500	39	342	44	396	63	508	37	544	42	1858	23	593	29	493	42	28	100	102	77	221	46	534	35	272	57	416	44	373	40	407	41	328	39	224	36	365	34	318	35	181	45	182	51	197	51	367	31	66	58	269	46	261	44	295	40	162	52	351	50	573	40	658	41	201	45	63	71	664	45	107	61	102	58	234	39	339	45	190	49	403	40	116	62	187	47	0	.	210	52	1131	24	829	24	1348	21	145	71	354	44	213	47	714	36	457	36	147	63	170	60	286	38	0	.	46901	3
	71-80 cm	165	52	75	71	200	45	742	29	138	58	211	46	1590	19	1430	22	1014	24	425	32	42	100	141	73	249	55	0	.	34	100	267	46	392	44	209	53	45	100	629	38	701	28	417	39	779	28	825	23	599	26	595	37	390	46	430	35	442	37	209	45	181	50	0	.	140	74	308	38	127	52	131	50	522	47	434	45	157	65	411	41	525	45	639	34	314	43	422	42	35	100	89	100	24	100	442	34	131	58	281	77	71	74	88	100	206	64	256	65	294	62	192	50	46	71	101	59	82	59	183	41	168	46	210	46	70	72	143	80	266	63	198	46	482	31	118	59	173	51	0	.	87	73	0	.	38	100	89	71	181	62	46	100	28	100	0	.	148	67	0	.	110	74	976	30	290	42	852	26	89	71	175	51	64	100	537	37	133	100	107	71	129	60	42	100	0	.	26166	5
	81-90 cm	66	100	52	100	215	62	118	71	49	100	0	.	281	47	588	34	607	35	186	74	0	.	0	.	0	.	61	.	0	.	175	71	199	59	135	71	0	.	210	47	471	43	213	45	231	47	632	37	374	44	104	71	0	.	238	50	44	100	159	73	115	72	53	100	122	71	0	.	0	.	105	100	40	100	264	59	115	72	163	54	135	58	444	40	0	.	52	100	62	100	0	.	0	.	112	72	84	100	39	100	0	.	185	74	296	53	79	100	180	60	93	76	28	100	73	71	27	100	184	46	28	100	116	59	60	73	0	.	96	71	92	75	59	100	0	.	121	73	0	.	0	.	0	.	0	.	0	.	0	.	48	100	49	100	0	.	0	.	0	.	117	74	642	32	88	59	69	71	97	100	135	71	135	71	97	71	0	.	60	100	37	100	50	100	0	.	10652	8
	91-100 cm	0	.	54	100	0	.	117	71	0	.	0	.	237	50	176	58	98	71	127	71	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	257	47	96	72	182	58	114	71	308	45	48	100	68	100	0	.	0	.	0	.	65	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	114	100	0	.	130	72	58	100	0	.	0	.	181	73	0	.	0	.	0	.	68	100	0	.	55	100	52	100	0	.	109	71	0	.	143	73	0	.	38	100	0	0	56	100	77	100	0	.	35	100	0	.	0	.	0	.	0	.	111	71	57	100	0	.	0	.	0	.	55	100	0	.	81	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	225	45	136	58	78	58	0	.	60	100	0	.	0	.	0	.	45	100	0	.	0	.	0	.	3980	12
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	36	100	217	73	138	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	100	0	.	51	100	0	.	0	.	155	71	315	46	52	100	185	71	58	100	0	.	0	.	0	.	70	100	0	.	0	.	0	0	0	.	0	.	0	.	0	.	50	100	38	100	0	.	61	100	0	.	0	.	0	.	0	.	112	100	0	.	0	.	68	100	0	.	194	100	94	100	129	100	148	79	0	.	0	.	0	.	0	.	198	100	0	.	175	59	0	.	0	.	0	.	0	.	49	100	0	.	0	.	0	.	0	.	63	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	0	.	64	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2830	18
	total	3166	20	2245	22	2121	26	4596	20	3751	28	4750	24	23946	9	16814	10	17079	10	10439	12	884	34	1589	32	2918	24	252	.	1694	35	4690	22	5719	17	5291	20	485	54	6015	18	10133	13	6697	16	8852	15	17213	9	15112	10	11963	12	5313	19	6895	17	7305	16	1393	33	3184	23	1501	32	1562	34	3928	22	3339	26	2086	29	5392	21	4202	25	3489	26	5619	18	4259	25	7990	17	4744	18	3681	23	1717	28	1913	27	2980	23	4130	22	3607	23	3470	24	3157	24	2610	26	3204	21	1857	28	5487	28	2859	22	2264	20	2573	21	4041	17	5080	15	3004	19	4280	19	2654	28	2111	34	2682	26	3047	25	3368	25	3382	26	2238	34	738	45	3813	21	845	47	809	34	2784	21	2336	27	2589	24	2267	26	1401	32	1238	33	544	60	1098	46	12360	12	9160	13	12028	12	2036	30	2287	28	2060	28	4122	22	3819	21	1733	29	1304	30	1903	26	0	.	415285	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. 2279369. Birmensdorf, Swiss Federal Research Institute WSL

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