Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-35calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest and the total forest (pairs of values in ), but they do in the private forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest and the total forest of the Plateau has decreased. For the private forest the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest and total forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
growing stock (stemwood)
ownership (2 categories) · diameter at breast height (10 classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
ownership (2 categories)	diameter at breast height (10 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
public	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	220	35	146	49	287	45	129	30	1	1351	111	58	343	28	160	36	567	20	600	20	79	48	65	62	194	28	0	.	94	62	167	36	249	29	141	40	24	65	206	32	338	27	267	23	415	23	1145	16	1055	15	625	18	128	42	93	33	150	36	106	46	105	40	67	35	84	42	92	41	131	52	178	33	244	36	110	51	115	36	284	29	213	37	44	44	266	29	192	32	162	41	117	49	89	39	233	32	172	41	62	64	166	40	125	54	52	49	178	43	367	27	134	28	360	25	107	45	141	35	617	20	329	31	296	29	151	43	158	52	191	34	167	37	102	39	84	60	84	72	17	52	200	33	49	51	86	48	195	30	114	51	174	39	71	38	116	55	49	62	8	100	9	73	682	18	637	23	656	20	61	43	93	57	28	196	35	50	120	63	82	45	29	44	140	48	0	.	18528	3
	21-30 cm	306	30	439	41	269	32	290	38	71	49	279	43	753	22	503	24	893	21	1629	18	90	50	250	39	410	30	2	.	204	55	499	36	644	34	315	48	83	65	715	32	834	23	408	24	584	22	1959	15	1842	13	888	18	207	36	269	40	243	33	100	49	139	38	159	42	114	43	136	56	288	44	384	38	597	30	93	65	256	40	437	28	351	44	225	41	816	27	215	36	298	35	149	43	276	41	314	38	382	34	160	43	197	47	136	46	281	51	112	59	544	27	275	28	444	31	324	51	245	48	945	21	668	26	441	30	321	52	89	48	368	33	248	43	137	43	88	62	132	91	95	70	446	32	71	54	85	48	430	32	327	39	432	36	132	50	251	50	120	46	37	98	15	100	1199	18	1040	24	1442	18	218	42	187	43	100	54	152	52	140	62	222	40	171	45	279	40	0	.	35355	3
	31-40 cm	354	32	254	40	489	32	405	29	402	43	454	44	1163	21	994	24	961	20	1982	18	108	56	318	35	541	36	23	.	379	45	564	33	570	30	510	32	125	76	656	38	1173	20	823	22	751	26	2751	14	3025	14	1956	19	227	34	666	42	418	50	146	48	191	38	203	50	245	45	256	33	545	38	382	34	698	28	157	66	383	40	873	26	489	32	310	45	952	24	434	36	655	31	337	37	395	37	439	29	419	35	528	36	122	71	151	41	171	39	225	42	533	27	560	35	262	29	144	38	189	45	602	23	329	29	459	32	309	42	90	59	514	38	312	32	317	51	176	52	181	59	82	90	583	31	126	81	194	44	502	28	280	31	524	31	297	42	243	51	101	62	234	55	7	100	1472	19	1298	19	1633	17	291	42	192	43	258	52	196	53	303	43	225	41	483	43	250	35	0	.	47504	3
	41-50 cm	442	32	213	39	386	39	875	25	399	40	671	44	1510	26	821	23	1587	18	2123	16	158	64	280	48	491	37	38	.	381	50	559	34	514	38	546	36	34	78	1239	24	1519	18	929	22	1117	26	2741	14	3286	13	2348	18	399	33	436	35	245	39	223	48	201	50	288	40	256	41	332	37	295	46	286	37	765	25	303	45	350	42	673	28	203	45	222	43	1177	26	378	33	739	34	421	35	453	34	660	30	440	33	516	32	159	82	233	53	396	39	114	48	548	23	801	34	155	41	66	46	108	66	487	26	331	30	270	37	189	57	101	50	521	41	436	53	395	38	209	45	122	57	140	72	594	29	65	73	181	43	499	29	243	40	671	30	479	31	286	55	242	50	153	69	15	136	1244	18	1342	20	1609	18	216	49	174	46	286	41	58	59	254	37	166	47	343	41	306	35	0	.	50671	3
	51-60 cm	380	40	333	35	283	42	1052	31	337	45	435	48	1153	26	1083	23	1903	20	1472	20	248	49	131	61	583	36	70	.	208	52	439	36	640	35	409	39	0	.	1059	25	1092	25	1064	22	1157	25	2600	17	2329	15	1768	18	395	38	368	44	198	40	136	60	248	73	242	68	199	46	418	40	131	63	236	52	581	34	315	48	138	44	647	35	755	37	414	41	632	30	740	37	244	45	469	54	292	39	575	28	529	40	596	34	45	71	318	53	399	37	294	39	763	28	460	39	228	34	32	128	18	100	439	29	243	40	255	39	136	71	131	59	326	41	366	45	644	40	352	73	139	71	0	.	440	32	77	100	199	50	330	30	535	36	471	39	598	43	230	54	106	59	260	52	114	77	1272	20	1045	23	1965	18	151	53	365	49	225	45	122	85	364	41	347	59	192	46	397	39	0	.	47114	3
	61-70 cm	223	47	195	49	124	51	711	31	154	56	256	63	821	35	620	34	982	27	460	34	154	60	176	68	263	55	0	.	102	74	278	51	368	61	315	40	0	.	470	36	539	32	641	27	758	28	1297	19	971	22	682	29	384	49	277	47	150	52	141	60	112	58	66	100	113	74	328	48	91	72	97	66	359	40	200	46	115	58	328	45	246	45	353	51	500	40	294	44	108	58	211	50	108	59	691	33	129	62	210	55	34	100	74	100	134	51	195	69	284	40	342	40	105	63	27	100	82	100	263	40	74	58	293	60	123	61	128	61	131	60	201	48	211	52	66	71	105	59	0	.	547	46	50	72	146	78	200	39	184	64	53	71	164	49	35	100	163	60	0	.	141	100	627	35	340	35	751	28	43	100	449	44	102	100	44	100	82	72	116	75	104	75	195	61	0	.	24581	5
	71-80 cm	141	59	89	71	209	60	204	58	100	100	44	100	257	47	372	40	603	38	172	50	0	.	90	70	112	100	44	.	0	.	131	71	102	71	154	64	0	.	502	42	444	47	296	38	366	35	790	28	466	30	359	38	56	100	205	65	45	100	129	74	44	100	90	71	197	59	42	100	0	.	147	66	214	51	96	71	86	58	203	73	619	42	331	60	96	71	207	51	0	.	63	100	0	.	465	43	0	.	211	84	0	.	104	100	47	100	271	43	235	51	186	50	29	100	0	.	0	.	172	46	77	58	123	55	101	58	92	100	134	77	68	74	104	58	63	71	0	.	0	.	88	71	45	100	53	100	90	72	83	100	98	71	137	61	0	.	0	.	111	100	123	100	928	28	131	51	523	34	28	100	93	71	0	.	0	.	0	0	0	.	90	71	43	100	0	.	14061	7
	81-90 cm	41	100	53	100	87	71	161	73	0	.	0	.	105	72	251	45	238	51	175	72	0	0	58	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	203	47	44	100	268	47	335	40	624	30	304	42	41	100	0	.	0	.	0	.	0	.	62	100	0	.	0	.	0	.	0	.	0	.	0	.	79	100	0	.	0	.	185	58	0	.	0	.	0	.	52	100	0	.	0	.	66	100	0	.	60	100	0	.	0	.	194	71	0	.	165	60	51	100	87	76	29	100	0	.	56	71	0	.	41	100	0	.	0	.	46	100	170	60	52	100	49	100	37	100	0	.	0	.	51	100	0	.	67	100	0	.	0	.	72	100	0	.	0	.	81	100	0	.	236	47	170	53	155	45	0	.	113	72	50	100	47	100	80	100	100	71	85	71	0	.	0	.	5776	10
	91-100 cm	0	.	0	.	0	.	54	100	0	.	0	.	34	100	79	100	143	100	65	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	144	59	92	100	0	.	61	100	117	72	61	100	0	.	0	.	0	.	0	.	0	.	84	100	72	100	0	.	0	.	0	.	0	.	0	.	0	.	55	100	0	.	0	0	81	100	0	.	0	.	0	.	0	.	0	.	65	100	0	.	41	100	0	.	0	.	0	.	37	100	53	100	0	.	0	.	0	.	0	.	75	100	0	.	0	.	0	.	47	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	100	0	.	0	.	28	100	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1595	21
	>100 cm	0	.	67	100	0	.	86	100	0	.	0	.	0	.	200	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	0	.	0	.	127	71	246	52	52	100	67	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	400	100	163	100	148	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1772	30
	total	2108	23	1790	26	2133	26	3966	21	1465	40	2252	37	6139	19	5082	19	7879	14	8679	14	836	39	1367	33	2594	28	177	.	1369	39	2636	28	3087	25	2390	30	265	65	5245	21	6075	17	4698	18	5671	18	14269	11	13390	10	8734	14	1796	30	2315	33	1448	33	980	40	1185	39	1186	37	1208	37	1604	31	1482	37	1710	32	3458	22	1351	37	1497	32	3481	24	3061	28	1980	37	4439	19	2461	27	2258	25	1768	30	1613	30	3507	24	2071	29	2384	30	722	47	1143	39	2074	33	1589	29	3642	20	2809	25	1671	22	730	39	784	36	3656	16	2051	23	2177	26	1329	39	837	40	2231	31	1967	31	2000	31	1086	45	800	47	333	60	2898	24	533	60	944	36	2313	23	1767	30	2424	25	1948	29	1161	44	841	39	883	55	423	70	7725	13	6051	16	8788	14	1009	37	1667	34	1049	36	655	49	1343	33	1257	38	1497	35	1610	29	0	.	246957	2
private	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	10	253	22	66	0	.	38	48	46	51	96	40	1009	14	548	16	560	25	127	38	8	71	26	72	34	61	0	.	25	78	128	31	190	38	155	66	6	77	41	73	99	32	115	42	272	36	58	53	81	55	126	29	232	38	354	31	376	21	37	58	133	46	8	100	18	71	82	58	88	49	32	.	49	47	63	41	119	72	108	46	47	65	132	33	60	61	17	69	0	.	17	82	38	48	100	54	71	83	79	64	136	35	176	46	210	41	28	71	0	.	19	73	116	65	229	31	561	28	27	82	111	66	221	41	66	47	30	63	57	63	60	52	92	47	194	39	69	54	48	81	60	53	1	100	0	.	47	53	41	80	33	74	42	53	50	78	40	94	0	.	36	84	303	31	327	25	338	35	30	62	55	62	55	58	160	32	42	38	21	83	51	80	25	94	0	.	10284	5
	21-30 cm	85	55	4	400	0	.	222	79	269	47	325	35	1938	14	1323	19	1248	18	408	44	14	100	55	71	62	74	0	.	47	71	411	41	315	41	344	44	48	72	171	45	69	65	344	43	332	34	245	45	186	43	322	30	356	30	406	27	798	24	127	91	207	39	0	.	50	71	251	43	320	40	70	.	191	46	297	38	212	54	238	55	185	62	416	28	206	58	25	100	20	.	63	73	263	53	103	64	95	76	216	56	375	35	202	67	342	39	33	71	0	.	85	67	106	61	492	33	714	23	121	100	217	77	312	33	289	49	175	56	45	69	139	44	84	61	401	60	161	49	48	100	134	46	16	100	0	.	78	59	141	71	47	63	109	61	149	52	95	85	0	.	86	71	473	30	592	26	332	31	99	50	70	83	73	48	512	36	306	34	127	88	32	78	50	71	0	.	21765	5
	31-40 cm	223	37	129	56	0	.	236	60	362	43	745	31	3282	13	1762	16	1476	19	659	31	3	537	63	100	150	61	0	.	82	71	552	36	401	41	482	36	81	71	225	40	452	31	274	42	455	33	372	45	132	55	765	28	746	31	694	32	1056	22	110	73	451	47	37	72	80	77	296	36	312	46	36	.	252	51	392	35	452	42	240	46	209	56	916	26	172	57	129	91	39	.	104	77	230	46	120	48	295	42	184	70	719	35	338	40	418	40	11	100	0	.	37	72	60	66	438	33	600	24	68	100	110	62	320	54	195	53	270	55	31	72	201	53	146	49	291	46	180	61	70	79	215	45	19	71	0	.	148	48	67	62	85	61	207	55	73	51	70	83	0	.	102	58	642	24	387	29	418	33	9	404	237	56	162	46	511	31	721	30	189	54	67	76	0	.	0	.	29743	4
	41-50 cm	488	35	97	53	0	.	440	44	499	39	793	31	4276	13	2264	15	2241	17	742	35	15	100	103	100	138	54	0	.	90	83	556	38	575	41	495	34	145	78	417	42	858	34	243	42	597	34	370	35	92	60	1059	24	828	34	1021	26	1849	22	124	59	479	39	109	78	93	90	638	34	198	40	14	.	271	61	456	42	488	44	154	47	367	52	1497	24	162	56	61	71	52	.	62	71	307	41	244	54	273	47	230	62	427	34	286	47	246	35	31	71	0	.	90	71	8	100	286	30	383	38	21	100	73	50	232	41	184	47	395	55	141	71	241	51	142	55	304	48	189	58	136	59	115	53	136	89	0	.	221	45	49	72	124	92	196	58	133	59	134	81	0	.	132	65	781	35	380	35	460	36	270	57	197	64	295	44	774	26	839	30	213	47	167	75	72	83	0	.	37072	4
	51-60 cm	285	50	113	68	0	.	342	44	431	46	874	37	4089	15	2762	16	2111	19	418	50	21	100	0	.	195	52	0	.	20	100	425	37	371	39	623	31	52	100	191	63	620	29	91	54	673	37	407	35	247	71	594	28	715	47	1023	27	1554	25	84	51	333	46	97	100	63	89	371	38	346	49	0	.	325	55	494	49	153	52	193	47	347	48	1281	25	25	100	153	71	0	.	113	100	127	54	71	80	311	44	101	55	471	41	332	53	209	37	19	100	0	.	28	100	0	.	160	45	208	52	8	100	43	100	144	55	225	45	89	60	16	100	114	71	53	73	300	37	160	55	42	71	119	62	72	72	0	.	35	72	40	100	195	74	184	66	41	71	53	71	0	.	176	58	521	32	351	43	223	47	157	60	151	65	150	46	714	35	459	38	312	58	144	73	125	81	0	.	31084	4
	61-70 cm	202	52	37	100	0	.	293	51	241	55	472	45	1784	20	1675	21	1301	23	125	64	88	100	0	.	141	60	0	.	28	100	157	54	383	49	79	59	35	100	95	58	317	38	122	56	263	38	132	49	53	100	349	38	536	44	792	33	732	32	138	78	87	75	68	100	0	.	356	35	118	51	31	.	164	77	96	71	85	100	247	63	143	82	921	36	69	100	143	71	0	.	39	100	29	100	25	100	100	76	135	58	84	58	144	50	195	76	30	100	0	.	33	100	19	100	128	47	133	48	15	100	0	.	37	80	88	59	52	72	33	100	108	61	166	60	438	55	66	72	0	.	0	.	0	.	0	.	20	100	76	100	0	.	45	100	26	100	87	74	0	.	62	72	359	58	299	35	194	49	180	71	168	100	33	100	477	36	150	61	70	71	0	.	77	100	0	.	17521	6
	71-80 cm	61	100	49	100	0	.	249	52	49	100	85	100	798	25	1167	24	669	32	65	73	60	100	0	.	168	62	0	.	0	.	69	83	167	74	35	100	0	.	43	100	437	33	54	100	109	74	0	.	0	.	161	58	179	83	153	58	360	53	0	.	40	100	0	.	0	.	55	100	25	100	0	.	119	71	236	66	107	71	188	56	84	71	450	37	37	100	59	100	0	.	0	.	0	.	0	.	51	100	0	.	67	71	114	71	127	75	31	100	0	.	9	100	0	.	83	59	15	100	31	100	24	100	42	71	94	61	0	.	37	100	82	73	82	58	0	.	85	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	40	100	109	53	23	100	95	51	72	100	0	.	51	100	82	71	40	100	95	71	0	0	0	.	0	.	8189	8
	81-90 cm	0	.	0	.	0	.	63	100	0	.	0	.	471	44	368	49	256	50	0	.	0	.	0	.	0	.	0	.	0	.	71	100	91	72	0	.	0	.	23	100	111	72	39	100	35	100	66	72	0	.	104	71	0	.	0	.	42	100	56	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	100	0	.	371	80	0	.	66	100	0	.	0	.	0	.	0	.	53	100	32	100	56	100	116	100	65	100	0	.	0	.	84	83	0	.	0	.	27	100	0	.	0	.	27	100	0	.	0	.	0	.	0	.	103	100	0	.	65	100	0	.	0	.	50	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	100	222	63	19	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3237	17
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	44	100	0	.	149	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	100	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	168	100	0	.	0	.	0	.	0	.	0	.	0	0	29	100	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	100	0	.	0	.	0	.	0	.	0	.	0	.	56	100	0	.	0	.	114	71	46	100	0	.	0	.	0	.	46	100	0	.	0	.	0	.	0	.	0	.	0	.	910	29
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	100	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	62	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	166	100	0	.	183	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	698	35
	total	1354	33	451	43	0	.	1883	38	1897	39	3390	28	17690	11	11869	13	10080	14	2545	28	209	79	248	87	888	46	0	.	293	76	2369	31	2542	28	2281	28	367	75	1206	39	2964	25	1283	37	2736	26	1721	30	791	45	3549	22	3645	27	4445	21	6767	20	676	51	1730	33	319	74	304	70	2048	31	1407	36	182	.	1370	48	2033	34	1615	37	1412	35	1381	44	6046	20	730	48	652	58	110	.	398	70	994	41	663	46	1248	39	1019	42	2334	29	1876	38	1811	29	184	71	0	.	386	58	309	61	1816	27	2706	19	457	62	577	52	1538	33	1140	40	1011	51	361	67	945	43	869	42	1928	36	976	52	345	60	643	43	363	71	0	.	550	44	414	60	484	58	784	51	472	52	556	72	0	.	676	58	3524	26	2425	23	2060	27	817	54	878	54	866	37	3229	24	2557	27	1027	46	461	64	350	71	0	.	160505	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	230	35	168	43	287	45	167	26	48	62	207	36	1352	13	708	15	1127	16	727	18	87	44	91	49	229	26	0	.	120	52	295	24	439	23	296	40	30	54	248	29	437	22	382	20	686	20	1203	16	1136	14	752	15	360	28	448	25	525	18	143	37	238	31	75	33	101	36	174	35	219	37	210	32	293	31	173	36	233	40	392	24	259	32	176	27	326	26	209	30	162	41	134	44	127	31	333	28	243	38	141	45	301	27	301	35	262	34	207	38	367	27	153	26	476	25	336	26	703	23	643	19	439	28	516	24	217	33	188	45	248	30	226	30	195	30	278	33	153	47	65	61	260	28	50	50	86	48	241	26	155	43	207	35	113	31	166	45	89	54	8	100	45	68	985	15	964	18	994	17	91	35	148	43	83	76	195	27	162	48	103	40	80	54	164	43	0	.	28812	2
	21-30 cm	392	26	444	40	269	32	511	40	340	38	604	27	2691	12	1826	15	2141	14	2037	16	105	45	306	34	471	27	2	.	251	46	909	27	959	27	659	33	132	49	885	27	904	22	752	23	916	19	2203	14	2028	12	1210	15	563	23	675	23	1041	20	227	55	346	27	159	42	164	37	387	34	608	30	454	35	787	25	390	33	468	33	674	26	536	36	640	23	1022	25	240	34	318	34	212	37	539	33	418	33	476	31	376	37	572	28	338	44	623	31	145	48	544	27	361	26	551	28	816	28	958	21	1066	21	885	27	753	22	610	36	264	40	413	30	387	32	221	35	489	50	293	49	143	57	580	27	87	48	85	48	508	28	468	34	479	33	241	39	400	37	215	45	37	98	101	63	1672	15	1632	18	1774	16	316	33	257	39	174	37	664	30	447	30	349	41	203	40	330	36	0	.	57120	2
	31-40 cm	577	24	383	32	489	32	640	29	764	30	1199	25	4445	11	2756	13	2437	14	2641	15	111	56	381	34	691	31	23	.	461	39	1116	24	971	24	993	24	206	53	881	30	1625	17	1098	19	1206	21	3123	13	3157	14	2721	15	974	25	1361	26	1474	21	255	42	641	34	240	44	325	38	552	24	858	29	417	32	950	24	548	31	835	29	1113	23	698	28	1226	22	1124	22	563	35	693	29	442	34	625	29	558	25	714	27	712	32	841	31	489	30	589	30	236	41	533	27	597	33	322	27	582	27	789	21	670	23	440	27	779	29	504	33	360	44	546	36	514	28	463	38	467	35	361	42	152	60	797	25	145	71	194	44	650	24	347	27	609	28	504	33	316	41	171	50	234	55	108	55	2114	15	1686	16	2051	15	300	43	429	37	420	36	707	27	1024	25	414	33	550	39	250	35	0	.	77247	2
	41-50 cm	930	24	310	31	386	39	1315	22	899	28	1464	26	5786	12	3084	13	3828	12	2865	15	172	59	383	44	629	31	38	.	472	43	1115	26	1089	28	1040	25	178	65	1656	20	2377	17	1173	19	1714	21	3111	13	3378	13	3407	14	1227	25	1457	21	2094	20	347	37	680	31	397	36	348	39	970	25	493	32	300	35	1037	25	758	31	838	31	827	25	569	37	1719	21	1339	24	440	30	791	32	482	32	760	26	904	26	713	27	745	29	586	33	519	35	641	27	145	41	548	23	891	31	163	39	352	26	491	32	508	25	404	26	502	27	373	37	496	45	661	35	676	39	537	31	514	34	311	42	276	46	709	25	201	65	181	43	721	24	292	36	795	29	674	28	418	42	375	43	153	69	147	60	2025	17	1722	17	2069	16	486	38	371	40	582	30	832	25	1093	24	378	33	510	37	378	32	0	.	87743	2
	51-60 cm	664	31	446	31	283	42	1394	26	767	32	1309	29	5242	13	3845	13	4013	14	1891	19	269	46	131	61	778	30	70	.	228	49	864	26	1011	26	1032	24	52	100	1250	23	1712	19	1155	21	1831	21	3007	15	2576	15	2363	15	1110	33	1392	23	1752	23	220	42	580	41	339	56	263	41	789	27	477	39	236	52	905	29	809	35	291	34	840	29	1102	30	1695	21	656	29	893	33	244	45	582	47	419	32	646	27	840	30	697	30	516	38	650	37	608	27	313	37	763	28	487	38	228	34	192	43	226	49	447	29	286	37	399	31	361	38	221	42	342	39	480	38	697	37	652	43	299	44	42	71	560	28	149	62	199	50	365	28	576	34	667	35	782	37	271	47	159	46	260	52	290	46	1793	17	1396	20	2188	17	309	40	516	39	375	32	836	32	823	28	658	41	336	41	523	36	0	.	78198	2
	61-70 cm	425	35	232	44	124	51	1005	26	395	40	728	37	2605	17	2295	18	2284	17	585	30	242	53	176	68	403	41	0	.	130	62	435	38	751	39	394	34	35	100	565	32	856	25	764	25	1022	23	1429	17	1024	22	1030	23	920	33	1069	27	882	28	280	49	199	46	134	71	113	74	684	29	209	42	128	55	523	37	296	38	200	54	575	37	389	41	1274	30	568	37	437	38	108	58	250	45	137	51	716	32	229	48	344	40	118	50	219	47	329	50	225	61	284	40	375	37	124	56	156	43	215	48	278	38	74	58	330	54	210	43	180	48	164	52	309	38	377	39	504	48	171	45	0	.	547	46	50	72	146	78	220	36	260	54	53	71	209	44	61	71	251	47	0	.	203	73	986	31	639	25	946	24	223	60	617	42	136	79	521	34	232	47	186	53	104	75	272	52	0	.	42102	3
	71-80 cm	202	51	138	58	209	60	453	39	149	75	130	74	1055	22	1540	20	1272	25	237	41	60	100	90	70	281	55	44	.	0	.	199	55	269	53	190	56	0	.	545	39	881	29	350	35	475	32	790	28	466	30	520	32	234	67	358	45	405	48	129	74	84	71	90	71	197	59	97	71	25	100	147	66	333	42	331	51	193	47	391	46	703	38	781	33	132	58	265	45	0	.	63	100	0	.	465	43	51	100	211	84	67	71	218	60	175	61	303	40	235	51	195	48	29	100	83	59	15	100	204	42	101	50	165	45	195	42	92	100	171	64	151	52	186	41	63	71	85	71	0	.	88	71	45	100	53	100	90	72	83	100	98	71	137	61	0	.	20	100	111	100	163	79	1036	25	154	45	617	30	100	77	93	71	51	100	82	71	40	100	95	71	90	71	43	100	0	.	22251	5
	81-90 cm	41	100	53	100	87	71	224	60	0	.	0	.	575	38	619	34	495	36	175	72	0	0	58	100	0	.	0	.	0	.	71	100	91	72	0	.	0	.	227	44	154	59	308	43	370	37	690	28	304	42	145	58	0	.	0	.	42	100	56	100	62	100	0	.	0	.	0	.	0	.	0	.	0	.	79	100	0	.	44	100	185	58	371	80	0	.	66	100	52	100	0	.	0	.	66	100	53	100	92	74	56	100	116	100	259	59	0	.	165	60	135	64	87	76	29	100	27	100	56	71	0	.	68	72	0	.	0	.	46	100	170	60	156	74	49	100	102	73	0	.	0	.	101	71	0	.	67	100	0	.	0	.	72	100	0	.	0	.	81	100	42	100	458	39	190	49	155	45	0	.	113	72	50	100	47	100	80	100	100	71	85	71	0	.	0	.	9013	9
	91-100 cm	0	.	0	.	0	.	54	100	0	.	0	.	77	71	79	100	292	61	65	100	0	.	0	.	0	.	0	.	0	.	0	.	48	100	0	.	0	.	144	59	92	100	0	.	61	100	117	72	61	100	69	100	53	100	0	.	0	.	0	.	84	100	72	100	0	.	0	.	0	.	0	.	0	.	0	.	55	100	0	.	0	0	81	100	0	.	0	.	0	.	0	.	0	.	65	100	0	.	41	100	0	.	168	100	0	.	37	100	53	100	0	.	0	.	0	0	29	100	75	100	0	.	20	100	0	.	47	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	100	0	.	0	.	0	.	0	.	0	.	0	.	116	71	0	.	0	.	142	60	94	71	0	.	0	.	0	.	46	100	0	.	0	.	0	.	0	.	0	.	0	.	2506	17
	>100 cm	0	.	67	100	0	.	86	100	0	.	0	.	0	.	200	73	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	100	0	.	51	100	0	.	0	.	127	71	318	46	52	100	67	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	0	.	62	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	100	0	.	0	.	400	100	163	100	148	82	0	.	0	.	0	.	36	100	166	100	0	.	183	59	0	.	0	.	0	.	0	.	38	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2470	23
	total	3462	19	2241	23	2133	26	5849	19	3362	28	5641	22	23829	9	16952	10	17958	10	11223	12	1046	35	1615	31	3482	24	177	.	1662	34	5005	21	5629	18	4671	20	632	51	6452	18	9039	14	5981	16	8408	15	15991	10	14180	10	12284	12	5441	20	6759	18	8215	17	1656	32	2915	24	1505	33	1512	32	3653	22	2890	26	1892	30	4828	21	3385	25	3112	25	4893	20	4442	23	8026	18	5169	18	3113	24	2368	25	2166	28	2607	24	4170	21	3319	23	3403	24	3056	25	3018	28	3885	22	1773	27	3642	20	3195	23	1980	21	2546	22	3490	17	4113	16	2628	21	3715	20	2469	28	1847	33	2591	28	2913	25	2870	25	3015	28	1776	36	679	42	3540	21	896	46	944	36	2863	20	2181	27	2908	23	2732	25	1633	35	1397	37	883	55	1099	45	11249	12	8475	13	10848	12	1826	31	2545	29	1915	26	3884	21	3900	21	2284	29	1958	30	1960	27	0	.	407462	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. 2300834. Birmensdorf, Swiss Federal Research Institute WSL

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