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 (main diameter classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
ownership (2 categories)	diameter at breast height (main diameter 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	.
	12-15 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	.
	16-23 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	.
	24-35 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	.
	36-51 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	.
	=52 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	.
	12-15 cm	154	29	67	33	15	47	31	32	4	71	28	52	106	24	171	42	259	26	161	27	67	46	52	46	45	49	2	.	18	81	33	39	91	36	65	50	14	75	60	27	119	24	90	28	172	23	469	21	321	22	153	43	68	37	27	39	32	53	39	46	13	47	52	52	29	60	59	58	19	50	100	42	106	39	28	49	30	47	107	41	23	50	13	46	85	35	85	42	50	43	88	45	55	58	80	35	43	45	21	42	14	60	8	75	19	58	53	33	175	32	51	28	157	34	20	48	37	53	237	31	89	31	83	28	13	88	59	44	55	35	66	35	38	44	63	72	17	57	15	64	65	46	14	61	72	47	30	57	28	61	63	47	62	57	24	62	16	73	17	73	21	87	246	20	210	25	266	19	22	43	14	47	30	45	27	51	120	46	37	43	33	45	9	42	0	.	6711	4
	16-23 cm	266	28	202	36	273	39	103	37	20	50	121	51	348	31	410	24	643	17	646	21	135	42	131	42	164	45	0	.	55	44	207	36	333	40	187	39	10	100	277	26	462	23	299	23	527	22	1310	14	1071	15	745	20	137	39	112	38	136	42	61	71	115	36	106	41	127	47	106	41	141	60	228	32	374	36	94	70	166	38	353	34	182	45	118	43	191	30	258	37	152	41	152	33	141	39	265	34	179	38	150	47	132	42	146	53	27	48	218	33	434	25	187	28	377	26	112	45	185	34	597	19	406	27	284	31	175	49	166	46	193	39	218	34	115	38	178	49	50	45	16	59	203	32	107	58	70	50	209	26	126	41	148	43	135	41	73	58	39	56	10	100	33	84	972	19	636	25	788	19	67	44	65	42	37	100	108	43	200	50	99	45	137	70	56	45	0	.	21520	3
	24-35 cm	396	31	528	34	334	33	314	42	242	47	520	43	984	23	694	23	1017	20	1928	19	105	52	177	44	476	33	-5	.	294	50	449	37	916	26	458	45	42	75	403	41	1218	22	965	22	1033	23	2944	13	2715	13	1110	20	318	35	482	43	307	38	113	54	255	36	237	47	164	54	227	59	419	53	483	36	961	29	198	42	215	39	671	32	398	58	501	35	691	28	517	35	391	33	182	41	353	39	428	29	681	30	189	49	467	42	145	46	160	46	127	47	1258	40	440	29	657	30	314	44	450	40	1293	19	784	25	603	29	461	45	102	59	483	37	438	33	227	32	201	47	251	58	80	74	553	32	184	57	152	53	508	29	404	34	418	46	116	47	199	50	157	56	100	72	0	0	1532	17	1593	21	1956	16	322	39	166	43	106	59	130	52	187	54	290	45	147	43	280	40	0	.	48075	3
	36-51 cm	675	28	320	31	637	33	691	27	639	39	827	38	1459	24	1718	23	1983	17	3123	16	142	56	475	40	571	35	38	.	588	43	719	33	1074	29	1234	35	95	89	1717	27	2457	19	1377	19	1658	22	4553	12	5364	12	3104	18	443	35	832	33	437	42	143	51	463	47	391	36	344	48	383	34	698	43	551	29	1230	28	503	43	722	36	1323	23	467	34	477	34	1587	23	647	29	889	30	478	34	695	34	1039	28	769	30	960	30	261	75	207	51	324	42	411	41	1405	32	901	29	327	25	183	37	327	51	998	20	779	29	850	30	364	41	191	47	660	37	619	43	831	41	292	50	275	45	114	92	1027	27	123	91	338	39	782	25	513	30	928	27	511	36	411	47	290	50	254	63	11	100	2408	17	2058	18	2826	15	476	40	454	40	334	42	199	51	324	41	331	37	284	45	352	38	0	.	79262	3
	=52 cm	602	31	616	37	861	39	1724	27	723	50	771	45	2111	28	2536	24	3381	18	2391	20	341	56	404	53	580	50	218	.	423	52	797	40	887	34	957	33	0	.	2642	24	2456	23	2532	22	2801	23	5740	13	4520	14	3209	20	904	35	936	43	413	44	470	58	730	48	465	56	528	48	763	42	271	71	531	45	1161	27	891	42	483	39	1435	29	1362	38	1186	44	1252	29	1475	36	179	70	605	42	463	40	1817	29	723	37	1183	38	135	61	381	53	947	39	958	34	2104	31	795	31	446	32	132	58	240	54	1258	23	498	31	830	37	372	55	419	53	729	44	855	41	1251	35	418	47	464	48	138	100	1177	33	165	76	177	52	652	37	829	44	684	32	717	40	216	53	435	44	162	59	238	100	3559	17	1764	21	3536	18	329	43	846	42	517	42	254	74	448	42	319	43	371	43	453	42	0	.	95668	3
	total	2093	23	1735	26	2121	26	2864	24	1628	41	2267	36	5007	21	5530	18	7283	14	8249	14	790	37	1239	33	1835	32	252	.	1377	39	2204	29	3301	23	2900	29	162	80	5099	20	6712	16	5263	17	6190	17	15017	10	13991	10	8320	15	1871	27	2390	32	1324	36	826	42	1576	36	1251	36	1192	39	1538	33	1548	42	1892	31	3832	22	1714	36	1617	32	3887	22	2432	31	2294	34	3806	21	2982	25	1661	29	1505	28	1707	30	3629	24	2395	27	2503	29	1008	44	887	37	1476	34	1768	29	5376	28	2373	24	1963	21	760	34	1240	32	4384	16	2556	21	2650	24	1385	36	937	38	2120	30	2196	31	2460	30	1153	37	1057	39	363	59	3025	24	592	59	809	34	2181	24	1900	30	2241	26	1540	30	922	41	936	37	544	60	302	87	8716	13	6262	16	9372	13	1217	35	1545	33	1023	33	718	43	1279	31	1075	33	972	32	1150	34	0	.	251235	2
private	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	6	62	15	58	0	.	36	51	5	68	22	48	392	14	186	17	322	29	74	54	2	100	10	87	21	44	0	.	6	100	64	41	44	39	29	38	9	88	4	55	62	35	23	48	59	41	42	84	35	54	61	38	109	46	179	39	136	28	3	73	36	37	5	100	3	175	14	49	36	47	4	.	18	50	33	78	26	88	56	46	7	71	45	30	23	69	2	73	0	.	9	71	-2	1499	67	79	13	62	19	60	11	309	51	40	59	35	5	.	5	72	6	78	21	61	97	58	134	29	16	74	9	71	85	36	21	45	6	85	11	46	10	67	25	63	55	43	11	67	14	72	17	57	5	82	0	.	18	48	6	83	7	74	7	63	5	59	0	0	0	.	14	80	143	32	103	26	90	37	9	75	13	53	15	51	44	39	54	70	2	100	7	74	23	45	0	.	3607	6
	16-23 cm	8	158	15	69	0	.	96	45	78	65	63	73	1061	15	654	16	814	23	211	36	3	100	39	78	67	51	0	.	22	100	256	38	65	217	237	79	52	100	50	49	153	30	150	55	165	36	128	38	140	45	146	30	269	35	347	33	418	22	23	71	126	47	6	100	38	67	200	44	194	52	21	.	81	46	80	62	122	53	117	47	50	65	167	30	73	56	39	72	0	.	23	100	41	50	89	54	36	55	65	56	97	43	108	60	178	36	18	.	0	.	28	70	96	53	214	35	577	27	95	73	57	74	296	36	67	51	49	60	130	53	58	52	45	57	157	50	51	79	36	100	92	62	0	.	0	.	41	57	48	91	16	100	55	51	54	85	4	100	0	.	78	78	402	30	378	25	301	28	21	82	75	54	61	56	159	35	44	42	18	81	0	.	97	47	0	.	11597	5
	24-35 cm	72	75	39	71	0	.	293	56	345	46	514	42	3121	12	1682	19	1512	20	409	39	9	100	87	73	215	46	0	.	54	79	408	52	458	46	417	34	29	77	204	62	313	37	517	47	499	34	421	42	164	46	500	33	558	33	690	26	1155	23	154	91	331	39	17	100	84	76	310	39	568	37	105	.	319	40	457	43	401	47	357	45	383	71	583	31	356	49	98	90	6	.	65	72	307	51	67	60	180	56	112	61	479	40	217	43	447	42	17	.	52	71	121	64	125	62	520	30	852	27	262	72	155	74	445	34	446	50	249	55	101	58	200	46	92	76	527	46	272	56	52	100	284	51	0	.	0	.	115	61	153	66	68	84	120	61	161	52	55	71	0	.	92	75	670	34	696	30	576	38	108	53	75	76	156	50	747	34	424	33	47	100	23	100	257	48	0	.	30403	4
	36-51 cm	499	43	207	48	0	.	511	45	800	39	816	34	6598	12	3079	15	2943	19	752	35	12	100	214	96	379	41	0	.	140	78	909	34	654	33	804	33	122	84	344	43	1196	27	433	39	683	33	734	35	285	49	1525	25	1196	29	1151	25	1756	21	175	63	556	40	108	84	174	63	832	33	475	40	30	.	440	54	840	36	717	44	437	37	638	49	1773	24	263	49	173	71	50	.	159	71	552	37	143	82	371	46	348	58	788	30	486	37	408	38	12	.	43	73	99	71	38	73	542	28	858	26	85	68	124	55	365	38	342	47	603	58	213	59	321	45	244	48	596	39	397	53	207	73	239	47	11	100	0	.	353	43	96	64	152	80	327	52	157	59	60	78	0	.	229	58	1081	32	718	32	808	33	258	58	344	60	351	46	966	27	1191	30	177	57	136	71	87	74	0	.	52511	4
	=52 cm	487	46	234	49	0	.	797	42	894	42	1069	39	7767	13	5684	16	4204	19	744	33	68	73	0	.	401	44	0	.	94	100	848	42	1197	32	905	31	113	100	314	44	1698	28	312	46	1255	35	872	31	496	51	1410	29	1310	33	2137	25	2516	23	212	66	557	40	114	81	71	75	1034	32	517	46	33	.	702	60	1080	47	606	64	765	42	750	43	3129	21	223	57	387	60	0	.	152	100	374	46	136	74	611	48	424	62	774	39	862	54	635	47	37	.	12	100	232	60	21	100	440	38	379	45	237	89	102	81	439	49	393	53	267	54	107	72	261	67	502	54	894	39	450	63	65	100	155	55	236	78	0	.	76	58	132	100	104	81	218	53	102	59	183	71	0	.	384	58	1347	33	1004	29	882	35	423	57	235	59	453	57	1488	32	827	41	415	64	166	75	288	54	0	.	65932	4
	total	1072	40	510	43	0	.	1732	36	2123	39	2483	33	18939	10	11284	13	9796	15	2191	27	94	71	350	84	1083	37	0	.	317	78	2486	33	2419	28	2391	28	323	71	916	41	3421	23	1434	39	2662	28	2196	28	1121	41	3643	23	3442	25	4504	21	5982	19	567	56	1607	32	250	71	370	68	2390	31	1790	32	193	.	1561	47	2489	34	1872	41	1732	33	1827	41	5696	20	937	42	699	59	56	.	408	74	1272	37	502	57	1211	43	968	46	2149	29	1723	35	1728	28	90	.	111	64	486	59	300	55	1813	27	2801	21	696	53	448	53	1630	29	1269	43	1174	52	562	54	851	43	908	42	2229	35	1181	54	374	68	789	42	252	76	0	.	603	43	436	62	348	69	727	51	479	52	302	71	0	.	796	54	3644	26	2898	23	2657	26	819	55	742	53	1037	44	3404	25	2539	28	658	52	332	70	752	42	0	.	164049	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	160	28	83	29	15	47	67	31	9	49	50	36	498	12	358	22	581	20	236	25	69	45	62	40	66	36	2	.	24	65	96	30	135	27	93	36	23	57	64	26	181	20	113	24	231	20	512	20	357	20	214	32	177	31	206	34	168	25	42	43	50	30	57	48	32	57	74	48	55	35	104	40	124	34	60	48	56	48	163	31	30	42	58	26	107	31	87	41	50	43	97	41	54	74	148	41	56	37	40	36	24	141	59	36	78	30	58	31	179	31	57	26	178	31	116	49	172	25	254	29	98	28	168	22	34	43	65	41	66	30	76	32	63	37	118	44	28	44	30	48	82	38	19	50	72	47	47	40	34	52	70	43	69	51	29	52	16	73	17	73	34	61	389	17	313	19	356	17	31	37	27	35	45	34	71	31	174	38	39	42	39	40	32	34	0	.	10318	3
	16-23 cm	274	27	217	34	273	39	199	29	98	53	184	42	1409	14	1063	13	1457	15	857	18	139	41	170	37	231	35	0	.	77	43	464	27	398	49	423	47	61	85	327	23	614	19	449	24	691	19	1438	13	1212	14	890	18	406	27	459	26	553	19	84	55	241	30	113	39	165	39	305	32	334	39	249	30	454	31	174	47	288	31	470	28	231	38	284	25	264	27	297	33	152	41	174	32	182	32	353	29	215	32	215	37	229	30	254	40	205	32	236	31	434	25	216	26	472	23	325	27	763	22	692	19	463	25	579	24	243	38	216	38	323	31	276	29	160	32	336	35	101	46	52	72	295	29	107	58	70	50	250	24	174	39	164	40	190	33	127	49	43	52	10	100	111	60	1374	16	1014	18	1089	16	88	39	140	34	98	51	267	27	244	41	117	40	137	70	153	34	0	.	33117	3
	24-35 cm	469	29	568	32	334	33	608	35	588	33	1034	30	4105	11	2376	15	2529	14	2337	17	114	49	264	38	691	27	-5	.	348	44	857	31	1374	23	875	29	71	54	607	35	1531	19	1482	22	1532	19	3365	13	2879	13	1610	17	876	25	1172	23	1462	20	267	57	586	27	254	44	247	44	536	33	987	31	589	34	1280	24	655	32	616	34	1028	26	781	45	1083	23	1047	25	615	33	397	33	247	36	661	31	495	26	861	26	301	38	946	29	361	31	607	33	144	43	1310	38	560	27	782	27	834	25	1302	22	1555	20	940	24	1048	22	907	33	350	43	583	32	638	27	319	31	728	36	523	40	132	59	838	27	184	57	152	53	623	26	557	31	486	42	236	39	360	36	212	45	100	72	92	75	2202	16	2289	17	2531	15	431	32	241	38	261	38	877	30	611	28	336	41	170	39	537	31	0	.	78478	2
	36-51 cm	1174	24	527	26	637	33	1202	24	1439	28	1643	26	8057	11	4797	13	4927	13	3875	14	153	52	690	40	950	26	38	.	728	38	1628	24	1728	22	2039	25	217	61	2061	24	3653	15	1810	17	2341	18	5287	12	5649	12	4629	14	1639	23	1984	20	2193	19	318	41	1020	30	499	34	518	38	1215	25	1173	30	581	28	1670	25	1342	27	1440	28	1759	19	1105	32	2250	20	1850	21	820	27	938	29	637	31	1247	25	1182	26	1140	25	1308	27	1049	29	692	30	732	28	424	40	1448	31	1000	27	365	24	725	23	1184	23	1083	19	903	26	1216	24	706	31	794	45	873	32	940	32	1074	33	888	31	672	36	321	57	1266	23	134	84	338	39	1135	21	609	27	1081	25	838	30	567	38	350	44	254	63	240	55	3489	15	2775	16	3634	14	734	33	798	34	685	31	1165	24	1515	25	508	31	420	38	439	34	0	.	131773	2
	=52 cm	1089	27	850	30	861	39	2521	23	1618	32	1840	29	9878	11	8220	13	7585	13	3135	17	409	48	404	53	980	34	218	.	517	46	1645	29	2084	23	1861	23	113	100	2956	22	4154	18	2843	20	4056	19	6612	12	5016	14	4619	16	2214	24	3074	22	2929	21	682	45	1288	32	579	48	599	44	1798	25	789	39	564	43	1863	28	1971	32	1089	40	2199	24	2112	29	4315	19	1475	26	1862	31	179	70	757	39	837	30	1953	27	1334	30	1607	32	909	34	1243	41	1582	30	996	33	2116	31	1027	27	466	31	572	32	620	34	1495	24	601	29	1270	29	765	38	687	38	836	40	1116	35	1753	29	1312	31	914	39	203	75	1332	30	402	55	177	52	728	33	961	40	788	30	934	33	318	40	618	37	162	59	622	52	4906	15	2768	17	4418	16	752	37	1081	35	971	35	1742	30	1275	30	733	40	537	37	741	33	0	.	161599	2
	total	3166	20	2245	22	2121	26	4596	20	3751	28	4750	24	23946	9	16814	10	17079	10	10439	12	884	34	1589	32	2918	24	252	.	1694	35	4690	22	5719	17	5291	20	485	54	6015	18	10133	13	6697	16	8852	15	17213	9	15112	10	11963	12	5313	19	6895	17	7305	16	1393	33	3184	23	1501	32	1562	34	3928	22	3339	26	2086	29	5392	21	4202	25	3489	26	5619	18	4259	25	7990	17	4744	18	3681	23	1717	28	1913	27	2980	23	4130	22	3607	23	3470	24	3157	24	2610	26	3204	21	1857	28	5487	28	2859	22	2264	20	2573	21	4041	17	5080	15	3004	19	4280	19	2654	28	2111	34	2682	26	3047	25	3368	25	3382	26	2238	34	738	45	3813	21	845	47	809	34	2784	21	2336	27	2589	24	2267	26	1401	32	1238	33	544	60	1098	46	12360	12	9160	13	12028	12	2036	30	2287	28	2060	28	4122	22	3819	21	1733	29	1304	30	1903	26	0	.	415285	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 2276578. Birmensdorf, Swiss Federal Research Institute WSL

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