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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NFI1
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 1983/85
		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	215	32	199	32	190	35	281	31	13	64	165	55	496	19	401	22	560	21	953	18	40	55	177	40	210	37	23	.	98	55	196	40	458	32	146	58	102	73	249	34	290	22	204	23	204	25	796	17	843	15	838	23	138	39	139	44	237	42	38	52	98	47	124	38	126	46	78	45	158	50	195	30	275	35	54	60	150	38	242	34	201	46	137	43	478	26	75	35	226	32	168	37	103	35	116	46	249	45	148	41	67	57	58	54	130	43	54	43	171	23	142	33	233	45	79	46	262	37	351	29	327	29	167	31	136	51	56	70	212	40	132	39	43	63	45	65	47	65	37	100	161	36	118	48	84	42	158	32	210	37	243	33	140	44	145	48	45	51	5	80	29	59	654	21	524	20	652	20	72	45	37	72	56	56	39	60	95	42	48	47	117	50	118	45	0	.	18803	3
	21-30 cm	545	31	278	40	284	41	668	24	257	47	426	40	1436	17	867	20	1410	19	1416	18	108	57	331	42	356	33	7	.	115	54	196	42	877	29	351	39	24	77	730	36	792	24	591	25	630	31	1624	16	2014	16	1540	19	317	31	63	73	312	41	121	45	80	52	263	39	173	44	165	43	157	44	276	36	623	27	217	51	449	41	599	27	238	41	443	41	867	29	220	38	645	29	211	41	373	35	181	33	525	40	237	37	46	71	117	63	275	39	69	45	291	35	357	37	134	34	91	50	204	37	346	27	387	31	198	43	232	34	54	54	467	35	141	42	131	46	146	51	106	44	0	.	363	28	196	58	322	41	299	31	367	29	393	31	441	34	200	50	75	62	110	51	104	56	1132	18	983	20	1230	20	102	59	212	72	173	48	150	74	185	39	86	56	328	45	201	39	0	.	37071	3
	31-40 cm	724	26	545	33	451	37	1437	23	409	40	645	38	2706	18	1674	25	2342	17	2017	18	208	45	175	59	365	36	0	.	307	54	323	41	984	28	962	39	0	.	936	26	1187	23	708	22	1159	24	2441	16	3368	13	2263	17	427	34	280	54	235	42	257	47	102	53	223	52	156	44	371	33	251	53	509	37	906	27	212	48	610	43	672	26	395	42	340	39	1126	24	537	37	675	34	565	34	459	33	430	30	680	33	693	34	240	58	206	49	257	38	121	59	332	40	548	29	97	40	24	100	110	51	197	29	247	39	269	40	340	41	87	65	464	36	293	46	361	56	103	45	296	42	0	.	592	27	190	47	261	37	266	32	259	42	660	30	675	28	208	59	248	50	166	54	267	90	1464	17	1232	19	1342	20	218	54	222	40	438	45	159	72	538	37	332	49	395	44	410	36	0	.	54576	3
	41-50 cm	913	34	604	41	280	36	1481	26	345	42	616	45	2695	18	1735	23	2455	17	1330	19	159	50	94	57	540	34	40	.	185	55	236	38	887	31	418	39	0	.	1002	23	1004	22	942	22	1509	21	2598	16	2273	14	1912	17	436	37	297	65	275	37	255	49	169	47	233	44	148	52	420	37	197	56	416	35	763	32	97	46	313	40	1006	30	509	35	461	39	663	28	728	29	545	36	903	36	442	34	427	31	674	34	453	33	237	65	261	59	334	36	305	37	454	28	358	29	81	50	22	70	78	69	277	33	259	44	160	43	264	46	121	53	311	36	320	50	236	43	218	44	355	47	0	.	553	30	163	55	316	44	402	30	299	34	348	39	552	33	164	76	158	48	115	65	188	51	1253	19	1202	22	1528	19	298	44	412	41	335	38	241	77	657	41	372	44	370	40	336	44	0	.	51996	3
	51-60 cm	465	39	582	38	206	43	696	32	205	56	408	52	1905	20	1064	23	1820	19	626	27	335	45	143	55	225	43	23	.	118	66	152	50	547	39	185	44	0	.	513	36	865	28	996	24	1187	27	1611	17	1548	18	823	23	638	39	265	59	202	75	140	49	43	100	97	69	267	50	346	51	80	71	411	40	551	31	360	42	211	64	678	36	298	51	244	68	424	33	469	40	153	47	289	47	95	84	456	40	395	41	536	45	65	75	20	100	184	48	277	50	462	37	158	45	86	57	0	.	109	88	274	37	138	38	189	61	167	67	110	58	240	52	314	36	188	73	67	61	323	45	0	.	516	43	97	50	294	53	282	39	292	44	141	56	263	50	93	59	111	83	24	100	152	51	860	21	728	23	1127	21	164	63	193	49	132	42	23	100	318	40	121	52	374	53	236	37	0	.	34809	4
	61-70 cm	378	43	196	58	112	59	59	100	106	78	34	100	641	27	489	33	680	24	157	42	163	70	102	77	55	71	36	.	68	71	72	71	323	50	72	100	0	.	481	46	413	34	281	47	517	31	1143	24	521	27	385	33	30	100	146	74	106	73	87	59	0	.	29	100	57	71	0	.	0	.	299	54	253	50	175	54	274	51	126	47	407	46	146	73	108	58	274	56	71	71	34	100	0	.	297	48	0	.	210	48	29	100	0	.	105	58	75	59	217	63	134	55	41	64	13	100	32	100	109	47	90	59	57	52	54	71	144	86	76	73	57	72	26	100	35	100	146	61	0	.	115	52	62	100	97	59	26	100	181	60	28	100	51	71	0	.	193	69	25	100	0	.	663	33	43	71	514	27	65	71	202	55	33	100	0	.	210	48	128	62	56	71	141	64	0	.	14889	6
	71-80 cm	36	100	43	100	44	100	82	71	82	72	0	.	234	48	228	43	333	38	184	60	59	100	0	.	0	.	0	.	0	.	86	71	0	.	52	100	0	.	249	49	126	59	203	53	183	49	689	29	262	33	75	73	74	71	0	.	0	.	42	100	88	71	58	100	176	64	0	.	0	.	0	.	89	74	0	.	194	72	0	.	156	52	33	100	46	100	0	.	0	.	0	.	0	.	92	100	0	.	68	71	38	100	0	.	42	100	60	71	251	72	90	78	0	.	37	100	0	.	64	73	46	100	40	100	0	.	30	100	0	.	74	73	33	100	0	.	97	100	0	.	98	100	36	100	0	.	113	59	41	100	0	.	84	100	0	.	0	.	101	100	0	.	581	29	139	46	111	62	42	100	47	100	0	.	36	100	148	58	39	100	41	100	39	100	0	.	6965	9
	81-90 cm	0	.	0	.	68	100	43	100	0	.	0	.	44	100	183	62	255	70	0	.	81	100	57	100	0	.	0	.	0	.	49	100	0	.	125	71	0	.	150	51	42	100	0	.	169	48	116	58	68	72	0	.	66	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	100	0	.	21	100	62	100	51	100	64	100	0	.	49	100	0	.	0	.	0	.	0	.	58	100	0	.	0	.	0	.	0	.	49	72	0	.	0	.	0	.	0	.	79	100	0	.	0	.	0	.	33	100	34	100	27	100	39	100	33	100	0	.	0	.	33	100	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	74	64	71	90	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2488	15
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	77	100	0	.	0	.	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	0	.	57	100	49	100	0	.	44	100	0	.	0	.	0	.	0	.	0	.	74	100	0	.	56	100	0	.	0	.	0	.	0	.	0	.	93	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	100	58	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	0	.	0	.	0	.	70	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	92	73	0	.	0	.	0	.	38	100	0	.	0	.	68	100	0	.	0	.	0	.	0	.	975	24
	>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	.	112	77	50	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	100	41	100	0	.	0	.	0	.	0	.	293	74	0	.	54	100	0	.	0	.	0	.	0	.	37	100	65	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	746	36
	total	3275	23	2447	27	1634	28	4748	21	1417	39	2294	36	10233	14	6640	18	9855	14	6737	14	1154	38	1079	34	1751	28	128	.	891	44	1310	32	4076	25	2312	33	126	73	4349	22	4718	18	3924	19	5614	18	11179	12	10948	11	7880	14	2126	28	1190	43	1367	36	940	41	580	41	1101	36	1103	38	1436	33	843	45	2106	31	3460	23	1176	41	2200	35	3437	23	2265	29	1854	35	3777	20	2303	27	2364	25	2169	30	1473	29	1998	27	2522	29	2405	29	721	48	661	45	1398	33	1083	32	2268	24	1787	26	672	30	265	48	795	38	1990	22	1494	24	1135	33	1192	36	635	50	1841	31	1358	33	1095	40	712	38	1441	40	37	100	2432	25	914	43	1375	35	1545	24	1651	29	1813	26	2207	27	810	42	831	49	546	54	740	59	6798	14	4916	16	6595	14	959	39	1363	35	1168	36	647	64	2221	31	1127	40	1681	35	1482	32	0	.	223318	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	91	41	89	52	0	.	86	76	136	48	224	34	670	18	819	18	1023	20	199	32	14	100	18	74	28	74	0	.	55	69	214	33	86	44	225	34	81	74	143	51	40	40	106	60	154	40	172	43	61	58	208	30	213	50	410	26	447	22	42	65	183	51	2	100	30	65	125	37	112	37	32	.	77	64	240	43	100	54	140	51	46	53	191	27	59	59	39	90	13	74	32	85	72	54	44	57	41	52	94	64	182	37	162	40	144	44	0	.	34	78	4	.	107	48	430	27	521	24	15	64	98	.	289	30	27	74	56	62	24	73	30	65	66	62	200	52	66	55	18	76	42	49	11	.	0	.	51	55	0	.	63	73	46	61	39	67	126	68	0	.	16	59	220	37	172	36	125	34	8	72	41	62	75	58	188	39	207	36	92	47	59	61	40	57	0	.	11821	5
	21-30 cm	320	39	255	45	0	.	191	44	310	45	891	40	1409	16	1606	18	1793	17	660	32	46	73	54	100	175	64	0	.	184	60	620	35	155	45	685	29	53	62	237	39	312	37	173	57	240	42	269	43	83	57	702	26	510	34	891	23	949	23	54	58	259	39	55	72	73	62	279	42	354	44	46	.	228	56	487	31	273	54	189	41	95	59	715	28	151	57	198	79	40	78	47	88	387	43	127	54	225	48	187	52	305	32	268	44	154	46	0	.	5	72	12	.	42	63	399	28	496	29	23	100	5	.	343	35	140	64	143	68	67	71	85	59	154	47	196	47	202	72	76	83	166	56	116	.	0	.	117	53	22	.	141	59	121	79	48	71	111	58	0	.	194	82	263	33	202	38	326	35	47	61	190	53	193	38	581	30	543	32	214	42	92	78	100	72	0	.	25141	4
	31-40 cm	362	42	235	41	0	.	399	47	398	46	686	33	2503	15	2330	16	2879	17	685	31	89	90	179	66	278	55	0	.	182	63	671	36	229	45	895	33	44	82	285	50	526	34	91	54	313	43	196	47	66	52	1040	25	725	28	2008	22	1580	22	183	74	296	37	152	76	51	82	475	43	293	45	41	.	292	57	721	37	238	49	71	44	269	72	897	31	40	79	184	66	49	81	83	87	297	41	72	63	288	42	116	51	398	43	326	44	270	37	0	.	40	100	59	.	54	89	263	34	291	38	25	72	0	.	197	35	27	76	218	76	23	71	54	72	203	43	298	52	208	56	268	81	113	55	85	.	0	.	189	49	20	.	75	82	30	81	64	73	42	75	0	.	185	82	189	34	234	37	296	41	207	62	148	66	438	45	1087	25	613	30	423	43	143	54	217	60	0	.	33002	4
	41-50 cm	251	39	373	45	0	.	304	43	401	49	690	38	2964	16	3193	16	2899	18	385	32	117	87	61	72	132	86	0	.	98	89	642	35	263	37	701	32	85	95	199	55	619	36	69	57	444	40	164	60	114	61	753	29	599	32	1375	21	1527	22	203	60	379	38	117	75	40	75	372	35	187	49	0	.	324	63	619	37	286	48	117	50	373	53	979	27	128	71	360	61	11	100	53	100	201	55	58	72	425	51	59	57	516	38	498	37	246	43	0	.	43	84	22	.	0	.	95	38	125	49	34	65	0	.	121	42	71	74	137	65	11	71	89	62	117	49	156	51	149	55	28	100	37	82	7	.	0	.	122	58	31	.	49	72	20	84	13	100	35	100	0	.	167	65	256	39	109	48	291	38	78	71	151	59	520	45	845	30	391	38	185	49	208	60	320	46	0	.	31008	4
	51-60 cm	112	59	400	53	0	.	193	64	99	60	312	41	2040	18	1994	19	1275	19	148	44	24	100	16	100	66	100	0	.	91	71	243	62	168	49	222	45	46	100	129	63	538	31	52	58	116	52	120	50	66	74	357	34	464	43	818	27	688	30	17	100	169	52	106	75	26	100	244	57	134	46	32	.	239	74	163	58	104	83	261	57	132	58	733	33	20	100	147	100	0	.	26	100	0	.	13	100	113	61	100	79	233	49	423	37	334	46	0	.	0	.	0	.	12	100	102	57	36	58	0	.	0	.	56	51	108	76	20	100	17	100	130	74	162	75	188	53	53	71	0	.	0	0	0	.	0	.	85	62	44	.	25	100	38	100	11	100	11	100	0	.	19	100	184	50	151	53	185	51	25	100	44	100	86	63	440	32	182	58	142	59	24	100	33	75	0	.	17179	6
	61-70 cm	0	.	63	74	0	.	112	58	0	.	0	.	592	31	784	23	527	39	64	100	34	100	0	.	0	.	0	.	0	.	73	71	216	42	90	58	0	.	27	100	213	48	37	100	28	100	25	100	0	.	157	45	219	56	110	58	234	39	0	.	25	100	0	.	26	100	157	50	54	71	22	.	0	.	67	100	72	100	67	63	40	100	206	58	0	.	43	100	0	.	0	.	0	.	0	.	38	100	0	.	117	60	135	61	248	55	0	.	0	.	0	.	0	.	21	100	17	100	30	100	0	.	13	100	26	100	0	.	31	100	63	72	0	.	0	.	94	73	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	100	0	.	0	.	53	72	20	100	73	59	0	.	26	100	61	100	83	59	27	100	0	.	40	100	0	.	0	.	5637	9
	71-80 cm	0	.	0	.	0	.	66	100	0	.	0	.	140	52	795	30	196	47	0	.	0	.	0	.	0	.	0	.	0	.	59	80	74	71	42	100	0	.	17	100	136	61	23	100	19	100	0	.	0	.	0	.	0	.	38	100	116	70	38	100	0	.	0	.	0	.	81	71	0	.	0	.	0	.	0	.	0	.	84	60	0	.	375	62	0	.	0	.	0	.	0	.	0	.	0	.	47	100	58	72	0	.	39	100	110	58	0	.	0	.	0	.	0	.	0	.	90	62	0	.	0	.	65	62	47	73	0	.	0	.	0	.	38	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	92	58	0	.	44	73	0	.	0	.	0	.	45	100	0	.	0	.	0	.	0	.	0	.	3011	14
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	51	100	258	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	83	77	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	100	0	.	0	.	0	.	560	31
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	32	100	0	.	0	.	47	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	264	36
	>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	.	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	79	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	147	66
	total	1136	35	1416	39	0	.	1351	36	1344	43	2802	30	10369	13	11778	13	10592	14	2141	27	324	76	328	66	679	59	0	.	609	62	2521	31	1190	34	2894	27	310	66	1036	43	2385	29	586	43	1356	32	947	37	391	52	3217	23	2731	28	5650	19	5541	20	537	56	1312	30	431	71	246	63	1848	31	1134	39	172	.	1161	60	2297	30	1072	45	929	37	955	49	4096	24	398	49	971	59	113	71	240	71	958	39	316	54	1178	43	649	45	1751	31	1893	31	1506	32	0	.	123	86	96	.	214	49	1347	26	1691	22	127	60	103	.	1215	29	447	52	574	65	173	71	451	52	741	44	1038	40	773	58	390	82	371	51	219	.	0	.	564	48	117	.	353	59	254	65	176	66	382	58	0	.	581	73	1256	25	926	30	1341	31	365	65	600	53	1372	40	3269	23	1963	27	1097	38	566	55	709	48	0	.	127770	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	306	25	288	27	190	35	368	29	149	44	389	30	1166	13	1220	14	1583	15	1153	16	55	48	195	37	238	34	23	.	153	43	410	25	544	28	371	31	183	52	392	28	330	20	310	26	358	22	969	15	904	15	1046	19	351	34	548	23	684	20	81	42	282	37	126	38	155	39	204	29	270	33	226	29	353	30	294	37	250	31	382	28	247	39	328	24	537	24	114	38	239	31	200	34	175	30	160	37	289	39	243	35	249	31	221	32	274	30	54	43	205	23	145	32	340	34	509	24	782	20	366	28	425	31	456	22	164	44	112	47	236	37	162	34	109	45	246	44	114	42	55	72	203	30	129	45	84	42	209	27	210	37	306	30	186	37	184	40	171	52	5	80	46	43	874	18	696	17	777	18	80	41	79	47	131	41	228	34	303	28	140	35	175	39	158	36	0	.	30625	2
	21-30 cm	865	24	533	30	284	41	859	21	567	32	1318	30	2844	12	2472	14	3202	13	2077	16	154	45	385	38	531	31	7	.	298	42	816	28	1032	25	1036	23	77	49	967	29	1104	20	763	24	870	25	1893	15	2098	15	2242	15	827	24	954	22	1261	20	175	36	339	32	318	35	246	36	444	31	511	33	322	34	850	25	705	26	722	32	787	22	333	34	1158	23	1018	26	417	42	685	27	257	37	761	28	308	29	750	31	424	31	351	29	385	36	429	30	69	45	296	34	369	36	175	30	489	24	700	23	369	26	392	30	541	27	372	32	197	51	534	32	225	34	286	33	342	34	308	50	76	83	529	26	312	52	322	41	416	26	390	28	535	28	561	31	248	43	186	43	110	51	298	56	1394	16	1185	17	1556	17	149	45	402	45	366	30	732	28	728	25	300	34	419	39	301	35	0	.	62212	2
	31-40 cm	1086	22	780	26	451	37	1836	21	807	30	1330	25	5208	12	4005	14	5221	12	2702	15	297	41	353	44	643	31	0	.	489	41	994	28	1212	24	1857	26	44	82	1221	23	1713	19	799	20	1472	21	2637	16	3434	13	3302	14	1152	22	2288	20	1815	20	439	41	399	30	375	43	206	39	846	28	544	34	549	35	1198	25	933	31	848	34	743	24	664	38	1237	25	1166	23	721	32	724	33	648	32	757	25	502	27	968	26	809	30	638	35	531	33	527	26	121	59	372	37	607	28	150	41	286	32	401	31	223	27	247	39	466	27	367	38	305	58	487	35	347	41	564	39	400	40	504	34	268	81	706	24	275	45	261	37	455	28	280	39	735	28	705	27	272	49	291	44	166	54	451	63	1653	15	1466	17	1639	18	425	41	370	36	876	32	1246	24	1151	23	755	32	538	35	627	31	0	.	87578	2
	41-50 cm	1164	28	977	30	280	36	1784	23	745	32	1306	29	5659	12	4928	13	5354	12	1715	16	276	47	156	45	672	32	40	.	283	47	878	28	1150	25	1119	25	85	95	1201	21	1623	19	1011	21	1953	18	2762	15	2388	14	2666	14	1035	24	1672	20	1801	19	457	38	548	30	350	38	188	44	792	25	385	37	416	35	1087	29	716	32	599	31	1123	27	882	30	1440	22	791	26	1089	28	556	35	956	34	643	29	485	28	1099	28	512	30	752	33	759	32	580	28	305	37	497	26	380	28	81	50	118	34	203	40	311	30	259	44	281	30	335	40	258	43	322	35	409	41	353	33	374	33	504	37	28	100	591	29	170	53	316	44	524	27	330	32	397	35	573	32	177	71	193	43	115	65	355	40	1509	17	1311	21	1820	17	376	38	563	34	856	31	1086	29	1048	29	558	34	579	33	656	32	0	.	83004	2
	51-60 cm	577	33	982	31	206	43	889	28	304	42	719	34	3945	13	3058	15	3096	13	774	23	360	42	159	50	291	40	23	.	210	48	395	42	716	32	408	32	46	100	642	31	1404	21	1047	23	1303	25	1731	16	1615	18	1180	19	1102	29	1084	25	890	29	157	45	211	46	203	51	293	47	591	38	214	39	443	38	790	31	523	34	314	51	939	30	429	39	977	30	444	32	616	39	153	47	315	44	95	84	469	39	509	34	636	40	298	42	443	36	518	34	277	50	462	37	158	45	98	51	102	57	146	67	274	37	138	38	245	48	275	50	130	51	257	49	444	33	350	52	255	42	377	40	0	.	516	43	97	50	294	53	367	33	336	40	166	50	301	45	104	54	122	76	24	100	170	47	1044	19	879	21	1312	19	188	56	237	44	217	36	462	30	500	33	263	40	398	50	269	34	0	.	51988	3
	61-70 cm	378	43	259	47	112	59	171	51	106	78	34	100	1233	20	1273	19	1208	22	221	42	197	61	102	77	55	71	36	.	68	71	145	50	538	34	162	55	0	.	508	43	627	28	319	43	545	30	1168	24	521	27	542	27	249	50	256	49	340	35	87	59	25	100	29	100	83	58	157	50	54	71	321	51	253	50	241	48	345	45	193	38	447	43	352	46	108	58	316	50	71	71	34	100	0	.	297	48	38	100	210	48	146	52	135	61	353	42	75	59	217	63	134	55	41	64	33	73	50	74	139	42	90	59	71	46	80	58	144	86	107	59	120	51	26	100	35	100	240	47	0	.	128	48	62	100	97	59	26	100	181	60	28	100	51	71	0	.	216	63	25	100	0	.	715	31	63	58	587	25	65	71	229	50	94	74	83	59	237	44	128	62	95	59	141	64	0	.	20526	5
	71-80 cm	36	100	43	100	44	100	149	59	82	72	0	.	373	36	1023	25	529	30	184	60	59	100	0	.	0	.	0	.	0	.	145	53	74	71	94	71	0	.	266	47	262	43	226	48	202	45	689	29	262	33	75	73	74	71	38	100	116	70	80	71	88	71	58	100	176	64	81	71	0	.	0	.	89	74	0	.	194	72	84	60	156	52	408	57	46	100	0	.	0	.	0	.	0	.	92	100	47	100	126	50	38	100	39	100	152	50	60	71	251	72	90	78	0	.	37	100	90	62	64	73	46	100	105	54	47	73	30	100	0	.	74	73	72	71	0	.	97	100	0	.	98	100	36	100	0	.	113	59	41	100	0	.	84	100	0	.	35	100	101	100	0	.	673	26	139	46	155	49	42	100	47	100	0	.	80	71	148	58	39	100	41	100	39	100	0	.	9975	7
	81-90 cm	0	.	0	.	68	100	43	100	0	.	0	.	95	71	440	39	255	70	0	.	81	100	57	100	0	.	0	.	0	.	49	100	0	.	125	71	0	.	150	51	42	100	34	100	169	48	116	58	68	72	0	.	66	71	0	.	0	.	0	.	0	.	0	.	0	.	47	100	0	.	0	.	0	.	61	100	0	.	21	100	62	100	51	100	64	100	0	.	49	100	0	.	0	.	0	.	0	.	58	100	0	.	41	100	0	.	0	.	49	72	0	.	0	.	0	.	83	77	79	100	0	.	6	100	0	.	33	100	34	100	27	100	39	100	33	100	0	.	0	.	33	100	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	74	64	71	90	71	0	.	0	.	0	.	0	.	0	.	40	100	0	.	0	.	0	.	3048	14
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	77	100	0	.	0	.	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	38	100	0	.	0	.	99	72	49	100	0	.	44	100	0	.	0	.	0	.	0	.	0	.	74	100	0	.	56	100	0	.	0	.	0	.	0	.	0	.	93	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	72	100	58	100	0	.	0	.	0	.	38	100	32	100	0	.	0	.	47	78	0	.	0	.	36	100	0	.	0	.	0	.	70	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	92	73	37	100	0	.	0	.	38	100	0	.	0	.	68	100	0	.	0	.	0	.	0	.	1239	21
	>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	.	112	77	50	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	100	41	100	0	.	0	.	0	.	0	.	293	74	0	.	133	66	0	.	0	.	0	.	0	.	37	100	65	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	893	32
	total	4411	19	3863	22	1634	28	6099	18	2760	29	5097	23	20601	10	18418	10	20447	9	8878	12	1477	34	1407	30	2429	26	128	.	1500	36	3832	23	5266	21	5206	21	435	51	5385	19	7103	16	4510	18	6971	16	12126	11	11339	11	11098	11	4856	20	6841	17	6908	17	1476	33	1892	23	1533	32	1348	33	3284	22	1977	29	2278	29	4621	23	3473	24	3272	28	4367	20	3220	25	5951	19	4175	19	3274	26	2477	24	2410	28	2431	23	2314	24	3700	24	3054	25	2472	26	2554	26	2905	23	1083	32	2391	23	1883	25	886	25	1612	23	2486	19	2117	21	1598	23	2351	22	1639	30	1209	40	2014	29	1808	28	1836	29	1749	28	2214	33	427	76	2803	23	1133	39	1375	35	2109	21	1768	28	2166	24	2461	25	986	37	1214	38	546	54	1321	46	8055	12	5842	14	7936	13	1324	33	1964	29	2540	27	3917	22	4184	21	2224	28	2246	30	2191	27	0	.	351089	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. 2299764. Birmensdorf, Swiss Federal Research Institute WSL

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