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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
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	264	35	414	27	616	21	525	21	144	46	108	47	118	45	2	.	43	57	95	46	224	30	177	42	8	100	208	28	276	25	181	25	347	26	1080	16	713	19	491	20	127	39	79	36	116	38	72	55	71	40	112	45	80	48	108	41	97	54	211	34	318	44	86	62	96	43	257	39	124	45	80	44	217	31	229	38	153	41	178	41	122	40	146	37	84	51	92	64	97	46	72	54	26	49	191	37	343	29	163	31	378	27	61	53	139	36	468	25	312	31	194	31	108	61	79	55	118	39	179	34	117	44	139	60	31	60	31	54	204	33	93	56	113	47	140	32	101	38	174	49	163	50	60	52	42	59	21	61	59	86	712	23	517	21	599	18	44	46	48	45	43	59	64	49	228	40	73	45	125	60	41	41	0	.	17079	4
	21-30 cm	346	27	289	35	320	35	251	32	49	61	359	46	700	26	581	24	841	20	1273	19	110	46	162	42	283	36	0	.	107	42	368	40	579	31	256	47	18	100	295	37	705	23	487	23	522	24	1746	16	1481	15	883	19	167	44	279	40	192	42	90	69	146	35	240	40	173	41	179	69	247	59	357	42	580	28	169	46	175	46	462	34	300	53	285	40	431	26	299	31	213	38	187	38	268	39	283	44	443	36	77	42	265	39	168	49	96	62	106	45	761	27	257	31	373	37	181	46	293	40	795	24	601	27	415	34	323	42	55	56	333	36	212	41	163	36	199	53	131	79	80	74	290	37	94	60	103	64	356	27	303	36	283	40	91	51	137	56	107	74	47	89	-6	100	1078	18	1076	25	1323	19	216	41	115	39	86	68	115	53	187	63	273	47	87	49	139	45	0	.	31560	3
	31-40 cm	346	33	431	34	243	45	248	37	465	43	449	38	693	25	919	23	1117	20	1916	18	106	58	262	39	416	34	10	.	341	50	302	36	772	26	639	36	65	82	430	31	1165	22	788	24	700	31	2497	15	2535	14	1584	20	257	37	530	45	290	41	77	59	291	53	126	50	165	57	181	41	499	48	411	34	878	30	152	47	334	41	684	31	390	43	451	35	822	26	535	37	576	34	169	36	389	39	380	30	460	42	336	37	286	57	97	55	136	42	196	47	985	46	427	37	325	30	206	45	302	36	621	25	441	29	353	33	337	46	65	74	374	38	355	32	201	42	141	55	266	51	50	83	674	28	162	78	160	50	477	29	319	31	425	38	195	50	175	45	126	46	184	64	0	0	1385	20	1249	21	1678	17	268	39	181	47	118	47	114	60	142	56	182	42	171	48	237	37	0	.	43605	3
	41-50 cm	457	29	170	40	502	33	493	28	372	39	450	41	989	27	1041	25	1131	19	1912	16	89	65	264	41	278	46	23	.	415	44	467	37	609	32	615	39	61	84	961	26	1469	21	742	22	978	26	2659	13	3369	13	1916	19	176	37	544	33	312	40	117	59	317	47	290	37	227	45	285	35	386	47	334	32	801	28	327	54	265	42	655	28	255	42	233	38	1009	24	390	33	489	31	366	38	370	33	596	34	406	38	667	31	226	83	130	59	230	52	177	47	1025	34	581	31	156	38	89	42	252	56	620	23	375	32	403	34	203	49	77	52	469	41	453	48	593	45	210	51	155	48	64	100	665	29	64	78	236	44	467	27	319	32	631	30	324	36	283	51	164	62	129	64	0	.	1406	20	1318	19	1730	18	246	48	256	40	260	47	95	67	274	43	141	42	206	50	260	41	0	.	48206	3
	51-60 cm	338	34	291	36	270	43	792	29	369	47	603	44	846	28	977	24	1776	18	1290	20	269	51	185	50	302	50	82	.	368	47	392	36	631	32	489	41	0	.	1005	25	1081	23	974	25	1159	26	2281	17	2486	14	1792	19	507	33	361	47	253	48	152	52	323	45	250	45	121	54	354	45	62	58	305	43	610	33	340	51	177	45	812	30	413	36	255	42	543	30	528	38	106	43	414	43	376	37	651	29	510	42	669	31	63	71	197	53	329	42	281	40	988	41	427	38	174	39	66	65	42	60	425	30	287	37	414	36	206	61	138	53	325	42	402	47	374	38	199	50	226	46	105	100	480	29	107	100	58	73	336	32	371	41	403	36	395	41	145	69	243	59	162	59	52	100	1238	19	1065	24	1700	21	177	47	336	46	215	44	25	100	190	48	272	55	153	45	251	40	0	.	44183	3
	61-70 cm	231	54	203	49	195	53	385	40	262	55	185	60	620	33	669	30	1037	22	451	33	72	100	117	58	56	71	75	.	69	71	251	51	188	46	218	38	0	.	603	33	681	28	700	26	687	33	1485	20	1218	24	1047	26	326	66	337	49	130	59	31	100	109	100	180	72	166	62	333	56	213	71	17	100	304	40	296	48	76	63	203	53	334	52	554	48	557	30	403	48	28	100	102	77	183	52	480	39	111	74	233	62	71	71	136	50	210	52	168	43	353	35	258	41	181	45	0	.	61	100	346	33	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	747	22	390	36	1027	26	0	.	255	54	180	53	99	100	258	52	29	100	63	100	181	41	0	.	26146	5
	71-80 cm	34	100	38	100	200	45	371	37	43	100	107	71	449	47	452	37	450	35	214	45	0	0	141	73	109	100	0	.	34	100	77	71	133	61	170	61	0	.	546	43	261	48	300	47	461	34	762	25	599	26	422	46	122	75	135	74	30	100	128	58	181	50	0	.	140	74	97	71	34	100	98	58	211	52	228	50	67	71	184	51	474	49	185	63	226	54	177	62	35	100	89	100	0	.	403	37	79	71	281	77	0	.	88	100	40	100	256	65	294	62	106	78	25	100	45	100	82	59	154	45	85	58	171	54	70	72	108	100	189	79	159	52	193	52	70	71	79	71	0	.	87	73	0	.	38	100	89	71	136	76	46	100	28	100	0	.	124	77	0	.	73	100	868	33	221	50	564	33	89	71	147	58	64	100	55	100	0	0	0	.	129	60	42	100	0	.	14992	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	.	184	51	256	67	121	58	164	52	495	45	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	0	.	180	60	0	.	28	100	32	100	0	.	151	51	0	.	48	100	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	.	6257	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	109	71	65	100	247	50	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	114	100	0	.	0	.	58	100	0	.	0	.	0	.	0	.	0	.	0	.	68	100	0	.	55	100	0	.	0	.	109	71	0	.	143	73	0	.	0	.	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	51	72	0	.	60	100	0	.	0	.	0	.	45	100	0	.	0	.	0	.	2449	16
	>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	178	60	52	100	81	100	0	.	0	.	0	.	0	.	70	100	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	38	100	0	.	61	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	100	111	100	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	.	1398	24
	total	2093	23	1735	26	2121	26	2864	24	1628	41	2267	36	4739	22	5530	18	7283	14	7830	14	790	37	1239	33	1563	36	252	.	1377	39	2051	30	3135	23	2698	30	152	85	4517	22	5948	17	4401	18	5236	19	13429	11	12876	11	8320	15	1680	29	2390	32	1324	36	826	42	1576	36	1251	36	1192	39	1538	33	1538	42	1837	32	3743	22	1714	36	1235	34	3364	24	2432	31	2294	34	3806	21	2613	25	1661	29	1505	28	1707	30	3118	26	2093	29	2410	29	1008	44	887	37	1353	36	1504	32	5184	29	2220	25	1640	23	680	37	1226	32	3657	18	2167	24	2217	28	1358	37	699	47	1985	32	2074	32	2224	33	1107	38	1057	39	363	59	3025	24	592	59	809	34	2159	24	1851	30	2200	26	1540	30	891	42	936	37	544	60	302	87	8074	14	5956	17	8773	14	1039	38	1534	34	1023	33	615	47	1279	31	1075	33	972	32	1150	34	0	.	235875	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	.	63	56	47	72	43	62	868	16	510	15	788	25	171	40	6	72	39	71	78	47	0	.	14	71	138	34	63	112	180	74	12	73	23	47	122	33	81	44	120	42	84	51	106	52	133	29	220	42	326	35	410	23	27	68	114	36	5	100	35	72	99	45	125	53	21	.	30	54	45	96	73	58	137	44	23	54	124	33	80	58	21	80	0	.	21	82	34	57	130	69	35	57	35	67	82	61	131	54	153	33	14	.	5	72	14	73	36	81	206	40	446	27	30	78	27	74	256	36	62	47	27	71	30	64	36	47	71	59	48	79	28	103	38	80	44	64	1	.	0	.	34	57	36	86	20	100	56	53	37	84	4	100	0	.	65	86	306	33	215	28	138	40	15	100	38	54	64	49	153	38	81	58	6	76	2	100	85	51	0	.	9036	5
	21-30 cm	40	60	25	80	0	.	242	54	207	47	272	43	1424	13	1234	19	1246	21	332	36	9	100	56	74	113	46	0	.	34	85	349	40	241	62	303	41	77	90	180	59	144	38	390	51	290	42	218	42	160	49	318	32	372	40	657	28	663	24	65	80	256	41	6	100	30	100	334	40	338	44	64	.	86	63	325	36	280	52	249	60	208	72	293	32	190	54	41	79	6	.	43	82	232	51	63	69	34	53	98	77	247	32	134	53	363	44	26	.	32	76	102	71	81	72	395	34	808	27	140	100	144	76	362	38	236	47	103	65	124	63	78	52	6	480	359	50	197	51	65	100	59	57	0	.	0	.	93	50	123	79	51	80	94	59	141	52	8	100	0	.	68	68	387	32	500	29	223	38	31	71	53	54	90	60	407	42	215	42	40	100	9	100	216	51	0	.	19643	5
	31-40 cm	98	58	90	65	0	.	247	56	352	46	503	36	2905	13	1456	19	1477	18	340	37	0	0	108	81	222	52	0	.	60	86	441	41	227	41	467	36	67	100	149	48	336	33	321	46	319	36	320	49	130	55	625	31	632	30	447	27	1032	22	145	87	295	43	17	100	119	84	221	39	354	48	54	.	192	48	419	40	452	47	173	49	354	47	563	32	250	54	120	100	9	.	74	72	190	54	67	66	297	47	151	47	533	38	224	46	317	42	-1	.	20	100	46	71	54	71	450	30	626	26	77	100	59	75	298	43	320	52	280	59	74	71	155	61	125	76	407	45	230	57	49	72	196	47	0	.	0	.	130	50	77	61	75	71	111	71	67	57	80	81	0	.	124	61	574	38	369	35	279	40	66	71	191	62	142	48	611	31	451	31	130	60	48	100	77	60	0	.	26027	4
	41-50 cm	437	46	116	65	0	.	259	54	494	44	540	35	3744	14	2054	17	1943	21	531	37	12	100	128	100	242	40	0	.	100	82	457	38	437	39	499	34	33	99	243	46	806	33	237	42	334	40	414	44	174	50	1109	26	713	31	764	27	1045	23	99	75	315	43	88	81	93	63	570	34	274	44	12	.	342	53	570	40	461	47	215	37	406	52	921	32	174	52	111	70	41	.	118	72	316	48	105	93	198	52	220	67	472	32	320	41	242	36	13	.	43	73	72	71	8	100	251	29	462	32	26	100	69	62	238	42	240	51	374	70	155	73	146	64	175	50	321	43	277	55	132	71	130	56	11	.	0	.	196	49	67	62	95	87	239	54	111	62	27	72	0	.	156	60	641	41	414	39	428	38	148	68	191	66	252	48	606	29	862	31	68	60	103	75	87	74	0	.	32379	4
	51-60 cm	253	65	139	52	0	.	328	47	450	48	739	42	3455	14	2839	17	1775	20	383	37	26	100	20	100	146	49	0	.	75	100	464	39	480	34	573	37	52	100	182	57	755	29	131	52	582	41	300	39	165	48	584	36	838	39	993	27	1598	23	86	61	299	53	92	71	67	73	587	33	242	50	9	.	263	61	613	41	222	50	168	47	523	44	912	28	120	53	138	71	0	.	152	100	168	54	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	84	78	26	100	88	59	69	66	292	46	203	55	62	71	126	47	64	.	0	.	64	57	50	100	82	100	119	72	97	63	102	79	0	.	144	62	609	34	433	36	416	44	100	63	143	72	380	51	509	37	597	36	190	57	59	74	134	53	0	.	30700	4
	61-70 cm	103	76	79	71	0	.	103	58	246	51	283	57	2130	18	1593	23	1335	24	257	45	0	.	0	.	136	62	0	.	34	100	165	47	195	52	257	43	37	100	62	71	347	36	84	59	311	50	178	47	331	54	528	32	251	44	910	35	533	36	0	.	188	52	41	100	26	100	259	47	230	48	0	.	195	79	46	100	224	100	238	60	210	71	999	30	35	100	27	100	0	.	0	0	0	.	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	55	72	37	100	128	81	47	73	449	52	79	71	29	100	40	100	0	.	0	.	0	.	37	100	0	.	108	71	25	100	57	100	0	.	163	61	344	62	371	37	199	48	145	71	99	73	33	100	588	40	200	48	118	74	106	75	104	78	0	.	18414	6
	71-80 cm	131	60	37	100	0	.	297	52	95	71	104	59	1044	21	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	253	58	31	100	0	.	173	60	268	58	295	39	384	41	81	71	0	.	0	.	0	.	211	45	62	76	33	.	277	77	206	77	90	100	191	71	52	100	341	48	88	71	171	71	0	.	0	.	0	.	0	.	52	100	0	.	71	74	0	.	165	76	0	.	0	.	0	.	21	100	56	71	0	.	29	100	36	100	39	71	0	.	34	100	0	.	39	100	165	51	48	100	94	72	0	.	0	.	0	.	0	.	0	.	45	100	0	.	0	.	0	.	24	100	0	.	37	100	36	100	69	72	175	57	0	.	28	100	0	.	482	40	133	100	107	71	0	.	0	.	0	.	9763	8
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	138	59	388	45	457	42	0	.	0	.	0	.	0	.	0	.	0	.	77	100	199	59	0	.	0	.	0	.	178	58	46	100	0	.	70	71	0	.	0	.	0	.	112	71	0	.	0	.	46	100	0	.	0	.	0	.	0	.	0	.	0	.	264	59	70	100	65	72	52	100	126	71	0	.	0	.	0	.	0	.	0	.	0	.	84	100	39	100	0	.	185	74	120	71	0	.	0	.	28	100	0	.	41	100	27	100	0	.	0	.	27	100	60	73	0	.	43	100	0	.	0	.	0	.	75	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	100	113	76	34	100	32	100	0	.	0	.	76	100	48	100	0	.	0	.	0	.	50	100	0	.	3410	14
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	185	58	115	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	71	46	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	903	25
	>100 cm	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	.	0	.	0	.	104	100	58	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	940	36
	total	1072	40	510	43	0	.	1539	39	1892	42	2483	33	15894	11	11167	13	9656	15	2127	28	94	71	350	84	1022	39	0	.	317	78	2279	35	2101	30	2391	28	323	71	909	41	3169	25	1346	41	2258	31	1616	34	1067	43	3643	23	3353	25	4504	21	5666	19	567	56	1511	34	250	71	370	68	2281	32	1626	35	193	.	1385	53	2489	34	1872	41	1490	36	1827	41	4278	23	937	42	699	59	56	.	408	74	941	43	502	57	1211	43	927	48	2149	29	1723	35	1728	28	90	.	111	64	371	71	200	73	1772	27	2800	21	511	63	401	58	1593	30	1269	43	956	60	491	61	670	50	659	43	1924	39	1181	54	374	68	595	45	76	.	0	.	516	48	436	62	323	74	727	51	479	52	302	71	0	.	796	54	3110	29	2451	26	1891	32	505	63	742	53	1037	44	3404	25	2539	28	658	52	327	71	752	42	0	.	151214	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	153	31	66	53	157	44	1132	15	924	15	1404	16	697	18	150	44	147	39	196	33	2	.	58	46	233	28	287	34	357	43	20	59	231	26	398	20	262	22	467	22	1165	16	819	18	625	17	347	30	405	29	527	20	98	44	184	27	117	43	115	40	206	30	223	38	232	33	348	41	132	52	169	35	394	30	148	39	204	26	297	27	250	35	153	41	198	37	156	34	276	38	119	40	127	50	179	37	203	40	179	29	205	35	348	28	178	29	413	26	267	33	585	22	498	24	339	29	450	24	171	43	106	45	148	34	214	29	188	35	187	49	59	58	68	50	248	29	94	56	113	47	173	28	137	36	194	45	219	39	97	45	46	54	21	61	124	60	1018	19	732	17	737	17	59	43	87	35	107	37	217	30	309	33	78	42	127	59	126	37	0	.	26115	3
	21-30 cm	385	25	314	33	320	35	493	31	257	40	631	32	2124	12	1814	15	2087	15	1605	16	119	43	218	36	397	29	0	.	140	38	717	28	820	28	559	31	95	75	475	32	849	20	877	26	812	22	1964	15	1641	14	1201	16	539	31	936	23	855	21	155	52	401	29	246	39	203	38	513	35	584	36	421	39	666	25	493	28	455	37	711	31	509	43	578	25	621	24	340	29	220	37	230	34	500	31	346	38	477	34	176	47	512	25	302	36	460	37	131	41	792	26	359	30	454	33	575	28	1101	22	936	25	745	26	777	25	559	31	158	47	457	31	289	33	169	39	558	37	327	44	145	60	349	32	94	60	103	64	449	23	427	34	334	36	185	39	279	38	115	69	47	89	62	75	1464	15	1575	19	1546	17	247	37	168	32	176	45	522	35	402	37	313	43	96	45	355	36	0	.	51203	2
	31-40 cm	444	29	521	30	243	45	495	33	817	31	952	26	3598	11	2375	14	2594	13	2255	16	106	58	369	36	638	29	10	.	401	44	742	28	998	22	1106	26	132	65	579	26	1501	18	1109	22	1018	24	2817	14	2666	14	2210	16	889	24	977	27	1322	19	222	60	585	33	143	46	284	48	402	28	853	34	466	32	1070	26	571	32	785	32	858	26	743	31	1015	24	1073	24	655	36	585	34	243	33	579	31	447	27	757	31	486	29	818	32	320	36	452	32	195	47	1005	45	474	34	379	28	656	25	928	21	698	25	500	27	651	26	657	34	345	50	448	34	510	29	326	39	548	36	496	38	99	55	871	24	162	78	160	50	606	25	395	28	500	34	307	41	241	36	206	42	184	64	124	61	1959	18	1618	18	1957	15	334	34	372	39	259	34	725	28	594	27	312	35	220	43	314	31	0	.	69632	2
	41-50 cm	894	27	286	35	502	33	752	26	866	30	990	26	4733	12	3095	14	3074	15	2442	15	101	59	392	42	520	31	23	.	515	38	924	26	1046	24	1113	26	95	64	1204	23	2274	18	978	19	1311	22	3073	13	3543	13	3025	15	888	26	1308	21	1357	20	216	47	632	31	379	34	320	37	856	26	660	33	345	31	1143	25	898	32	727	33	869	23	661	36	1155	27	1183	22	502	30	529	29	484	33	686	28	701	32	604	30	887	29	698	34	450	33	472	31	190	44	1068	33	653	28	164	36	340	24	713	28	646	22	444	29	641	26	442	36	450	59	624	35	599	39	768	36	531	33	432	39	196	58	795	26	75	68	236	44	663	24	386	28	726	28	563	31	394	40	191	54	129	64	156	60	2047	18	1732	17	2158	16	394	39	447	36	511	34	700	27	1136	26	209	34	309	42	346	36	0	.	80585	2
	51-60 cm	592	34	429	29	270	43	1120	24	820	34	1342	30	4301	13	3816	14	3551	13	1673	18	295	47	205	46	448	37	82	.	443	43	857	27	1111	23	1062	28	52	100	1187	23	1836	18	1105	23	1741	22	2581	15	2651	13	2375	17	1345	27	1354	24	1852	21	238	40	621	34	343	38	187	43	941	26	304	42	314	42	873	29	953	32	399	34	980	26	936	29	1167	23	663	26	667	34	106	43	566	41	544	30	760	27	860	32	803	28	453	40	460	39	579	30	287	39	988	41	515	36	174	39	258	33	337	45	435	30	353	33	526	31	406	39	222	44	351	40	490	40	444	34	490	34	429	35	166	68	606	25	171	73	58	73	401	28	421	38	485	34	513	35	243	48	345	48	162	59	196	53	1847	17	1498	20	2116	19	276	38	479	39	595	36	534	35	787	29	462	40	213	38	385	32	0	.	74883	2
	61-70 cm	334	44	282	40	195	53	488	34	509	37	468	42	2750	16	2262	18	2372	16	708	26	72	100	117	58	192	48	75	.	103	58	415	36	382	35	475	29	37	100	665	30	1028	22	784	24	998	27	1663	19	1549	22	1576	20	577	42	1246	28	663	31	31	100	297	49	221	61	191	56	592	37	444	42	17	100	500	39	342	44	300	76	441	40	544	42	1552	25	593	29	430	45	28	100	102	77	183	52	507	37	272	57	416	44	373	40	407	41	328	39	199	39	365	34	277	39	181	45	182	51	197	51	346	33	66	58	269	46	261	44	232	43	162	52	351	50	533	43	542	45	201	45	63	71	664	45	73	77	102	58	213	41	339	45	190	49	403	40	116	62	187	47	0	.	210	52	1091	25	761	25	1227	23	145	71	354	44	213	47	687	37	457	36	147	63	170	60	286	38	0	.	44559	3
	71-80 cm	165	52	75	71	200	45	668	31	138	58	211	46	1493	20	1430	22	1014	24	328	35	42	100	141	73	194	64	0	.	34	100	267	46	392	44	209	53	45	100	591	40	701	28	357	43	714	30	793	24	599	26	595	37	390	46	430	35	414	39	209	45	181	50	0	.	140	74	308	38	97	61	131	50	489	49	434	45	157	65	375	44	525	45	526	38	314	43	348	47	35	100	89	100	0	.	403	37	131	58	281	77	71	74	88	100	206	64	256	65	294	62	106	78	46	71	101	59	82	59	183	41	122	50	210	46	70	72	143	80	189	79	198	46	358	37	118	59	173	51	0	.	87	73	0	.	38	100	89	71	181	62	46	100	28	100	0	.	148	67	0	.	110	74	905	32	290	42	740	29	89	71	175	51	64	100	537	37	133	100	107	71	129	60	42	100	0	.	24755	5
	81-90 cm	66	100	52	100	215	62	118	71	49	100	0	.	281	47	588	34	607	35	122	100	0	.	0	.	0	.	61	.	0	.	175	71	199	59	135	71	0	.	184	51	433	46	167	50	164	52	565	41	374	44	104	71	0	.	238	50	0	.	159	73	115	72	53	100	122	71	0	.	0	.	105	100	40	100	264	59	115	72	133	62	135	58	315	47	0	.	52	100	62	100	0	.	0	.	112	72	84	100	39	100	0	.	185	74	296	53	0	.	180	60	28	100	28	100	73	71	27	100	151	51	0	.	76	73	60	73	0	.	96	71	92	75	0	0	0	.	121	73	0	.	0	.	0	.	0	.	0	.	0	.	48	100	49	100	0	.	0	.	0	.	117	74	589	34	65	71	69	71	0	.	135	71	135	71	97	71	0	.	60	100	37	100	50	100	0	.	9667	8
	91-100 cm	0	.	54	100	0	.	117	71	0	.	0	.	185	58	176	58	98	71	127	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	257	47	96	72	109	71	114	71	247	50	48	100	68	100	0	.	0	.	0	.	65	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	114	100	0	.	55	100	58	100	0	.	0	.	68	100	0	.	0	.	0	.	68	100	0	.	55	100	52	100	0	.	109	71	0	.	143	73	0	.	0	.	0	0	56	100	77	100	0	.	35	100	0	.	0	.	0	.	0	.	48	100	57	100	0	.	0	.	0	.	0	.	0	.	81	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	225	45	136	58	51	72	0	.	60	100	0	.	0	.	0	.	45	100	0	.	0	.	0	.	3352	13
	>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	178	60	52	100	185	71	58	100	0	.	0	.	0	.	70	100	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	38	100	0	.	61	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	100	0	.	194	100	0	.	129	100	111	100	0	.	0	.	0	.	0	.	198	100	0	.	175	59	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	.	2338	20
	total	3166	20	2245	22	2121	26	4404	20	3520	29	4750	24	20632	9	16697	10	16939	10	9958	12	884	34	1589	32	2585	26	252	.	1694	35	4330	23	5236	18	5089	21	475	55	5426	19	9118	14	5748	17	7494	16	15046	10	13943	10	11963	12	5033	19	6895	17	6990	17	1393	33	3088	24	1501	32	1562	34	3819	23	3164	27	2030	30	5129	22	4202	25	3108	28	4855	20	4259	25	6573	19	4744	18	3312	23	1717	28	1913	27	2648	24	3620	24	3304	24	3337	25	3157	24	2610	26	3080	22	1593	31	5295	29	2591	24	1840	22	2452	22	4026	17	4168	18	2569	22	3810	20	2627	28	1655	40	2476	28	2743	27	2883	27	3031	29	2238	34	738	45	3620	21	669	53	809	34	2675	22	2286	27	2523	25	2267	26	1370	33	1238	33	544	60	1098	46	11185	13	8408	14	10664	13	1544	33	2276	28	2060	28	4019	22	3819	21	1733	29	1299	30	1903	26	0	.	387089	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. 2279365. Birmensdorf, Swiss Federal Research Institute WSL

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