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
deadwood volume (merchantable wood), lying
higher/lower altitude zone · diameter of lying deadwood (4 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 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
higher/lower altitude zone	diameter of lying deadwood (4 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	.
	7-20 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	.
	20.1-30 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	.
	30.1-40 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	.
	>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	.
	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	.
lower altitude zone	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	.
	7-20 cm	144	23	81	29	29	38	89	27	1	.	26	63	107	34	132	22	308	18	131	31	63	44	84	45	81	40	3	.	54	48	80	38	82	46	62	35	19	86	111	39	18	63	33	54	0	.	91	43	0	.	308	19	48	41	50	27	101	31	56	39	15	82	34	56	27	46	34	58	76	62	0	.	42	44	59	37	61	42	76	44	63	61	89	52	163	30	55	38	98	39	67	31	116	32	30	52	0	.	27	74	67	37	42	32	58	46	1	100	46	43	5	76	83	42	115	44	179	23	119	31	76	41	110	36	8	54	0	0	3	100	71	47	20	61	4	78	48	75	13	67	63	47	21	51	44	58	12	77	22	40	75	37	36	51	46	50	54	41	7	100	8	70	55	45	43	48	199	36	47	46	21	41	58	38	185	44	68	33	41	35	73	53	29	35	0	.	5798	4
	20.1-30 cm	66	48	6	100	0	0	52	52	0	.	45	60	50	41	141	41	92	32	38	61	59	74	38	100	35	79	0	.	23	72	45	59	25	72	55	65	0	.	121	55	14	71	38	51	0	.	46	63	0	.	328	27	41	63	9	100	40	64	14	100	6	100	14	71	0	.	0	.	30	72	0	.	29	58	37	81	14	71	46	87	11	100	39	78	36	52	7	100	79	43	22	100	101	47	0	.	0	.	36	60	25	100	7	100	56	48	0	.	11	100	0	.	36	58	75	39	159	40	82	39	34	60	87	55	0	.	0	.	0	.	23	100	15	71	0	0	25	100	0	.	26	71	10	100	34	73	0	.	33	59	20	74	44	74	13	100	16	100	11	100	0	0	0	0	27	100	100	37	0	.	17	72	29	60	153	50	18	100	0	.	18	73	0	.	0	.	3131	8
	30.1-40 cm	49	58	53	60	0	.	38	71	0	.	20	100	16	100	129	49	70	51	31	71	18	100	0	.	36	71	0	.	0	.	46	76	78	60	50	78	0	.	32	100	0	.	49	71	0	.	0	.	0	.	122	47	48	71	37	100	32	100	0	.	0	.	0	.	0	.	0	.	20	100	0	.	19	100	35	100	0	.	57	58	0	.	20	100	18	100	0	.	16	100	62	59	15	100	0	.	0	.	19	100	14	100	0	.	33	71	0	.	0	0	0	.	0	.	20	100	56	58	0	.	38	72	22	100	0	.	0	.	0	.	22	100	16	100	0	.	0	.	0	.	0	.	0	.	26	100	0	.	0	.	0	.	46	71	0	.	74	62	0	.	0	.	0	.	0	.	45	72	0	.	84	68	0	.	81	50	20	100	0	0	0	.	0	.	0	.	1830	11
	>40 cm	47	100	90	72	0	.	225	59	0	.	24	100	276	83	107	61	54	100	72	100	26	100	0	.	0	.	0	.	0	.	22	100	0	.	69	100	0	.	23	100	0	.	0	.	0	.	0	0	0	.	111	80	25	100	110	59	110	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	122	100	0	.	0	0	0	0	31	100	35	100	0	.	0	.	0	.	0	.	65	100	35	100	0	.	38	100	0	.	0	0	42	100	88	76	0	.	0	.	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	97	100	0	.	0	.	0	.	43	100	0	.	0	.	0	.	144	84	32	100	0	.	0	.	0	.	0	.	0	.	99	71	84	58	31	100	0	.	0	.	0	.	0	.	0	.	2428	18
	total	306	29	230	41	29	38	404	39	1	.	116	51	450	57	509	29	525	18	273	39	166	44	122	55	151	46	3	.	76	47	194	51	184	40	234	59	19	86	286	45	32	47	120	52	0	.	136	45	0	.	868	21	161	38	206	39	283	49	71	46	21	87	47	56	27	46	34	58	126	56	0	.	90	46	131	43	75	46	179	46	74	54	270	60	217	29	62	37	194	36	182	38	266	36	30	52	0	.	82	53	105	57	114	60	183	37	1	100	94	54	5	76	119	38	252	39	482	31	201	29	149	35	267	40	8	54	0	0	3	100	117	48	52	66	4	78	73	83	13	67	186	63	31	61	104	65	12	77	97	63	94	40	127	55	58	58	287	48	50	81	8	70	55	45	69	64	344	35	47	46	221	51	171	38	450	37	107	53	41	35	91	54	29	35	0	.	13187	5
higher altitude zone	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	.
	7-20 cm	0	.	0	.	0	.	0	.	51	44	48	38	498	19	184	27	4	.	157	25	0	.	0	.	1	.	0	.	29	76	55	54	141	32	71	52	0	.	183	36	245	23	87	29	323	22	397	18	504	20	62	42	14	57	0	.	176	32	22	73	54	43	9	71	3	100	40	41	34	47	45	44	49	35	0	.	37	48	126	35	34	49	94	30	0	.	4	85	30	47	0	.	0	.	26	39	143	34	23	45	0	.	0	.	0	.	71	35	44	32	40	52	8	77	0	.	0	.	26	40	14	57	3	71	35	60	39	55	84	39	40	48	37	38	44	39	15	75	0	.	31	49	0	.	12	73	44	45	58	72	19	47	39	72	0	0	0	.	3	.	0	.	430	18	397	22	366	21	19	55	46	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	5969	5
	20.1-30 cm	0	.	0	.	0	.	0	.	19	58	26	58	637	20	62	39	6	.	159	36	0	.	0	.	0	.	0	.	0	.	13	71	208	41	81	63	0	.	177	37	182	30	80	45	298	28	467	24	387	29	42	61	6	100	0	.	289	48	17	72	23	58	0	.	0	.	8	100	74	52	65	42	34	59	0	.	15	100	95	45	31	100	76	40	0	.	0	.	20	72	0	.	0	.	9	100	188	51	33	59	0	.	0	.	0	.	20	71	31	82	19	72	0	.	0	.	0	.	12	100	11	100	0	.	51	56	14	100	88	57	57	55	32	74	48	46	9	100	0	.	48	60	0	.	0	.	0	.	37	82	0	.	19	70	0	.	0	.	0	.	0	.	330	22	297	23	271	29	0	.	15	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5236	7
	30.1-40 cm	0	.	0	.	0	.	0	.	0	.	34	71	679	23	142	47	0	.	64	50	0	.	0	.	0	.	0	.	0	.	64	60	69	60	18	100	0	.	135	43	133	43	14	100	284	38	287	39	235	37	0	.	0	.	0	.	163	61	19	100	18	100	0	.	0	.	0	.	0	.	0	.	106	86	0	.	0	.	46	75	13	100	15	100	0	.	0	.	0	.	0	.	0	.	0	0	20	100	28	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	63	58	47	73	74	53	17	100	0	.	69	100	0	.	0	.	0	.	19	100	0	.	22	100	0	.	0	.	0	.	0	.	336	32	158	39	352	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3756	9
	>40 cm	0	.	0	.	0	.	0	.	48	100	0	.	541	32	99	100	0	.	110	60	0	.	0	.	0	.	0	.	0	.	31	100	442	79	70	73	0	.	232	54	266	82	100	75	198	55	450	44	144	62	0	.	0	0	0	.	215	53	0	.	0	.	0	.	0	.	0	.	42	100	28	100	90	100	0	.	0	.	156	51	0	.	64	100	0	.	0	.	0	.	0	.	0	.	100	100	99	75	32	100	0	.	0	.	0	.	66	100	0	.	124	76	0	.	0	.	0	.	201	58	0	.	0	.	0	.	102	100	29	100	0	.	65	71	133	100	28	100	0	.	70	72	0	.	0	.	0	.	0	.	0	.	55	71	0	.	0	.	103	.	0	.	116	61	157	53	83	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4889	13
	total	0	.	0	.	0	.	0	.	118	64	108	42	2356	17	487	36	10	.	490	30	0	.	0	.	1	.	0	.	29	76	163	42	860	45	240	55	0	.	727	34	826	32	281	36	1104	24	1602	21	1270	21	103	40	20	54	0	.	843	39	59	78	95	40	9	71	3	100	49	43	150	48	138	40	280	54	0	.	51	58	423	33	78	62	250	40	0	.	4	85	50	49	0	.	0	.	135	77	450	35	116	52	0	.	0	.	0	.	157	50	76	42	182	55	8	77	0	.	0	.	239	51	25	54	3	71	87	48	155	79	214	49	159	49	181	39	298	49	68	67	0	.	217	56	0	.	12	73	44	45	113	64	19	47	135	58	0	0	0	.	106	.	0	.	1212	18	1008	20	1072	21	19	55	61	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	19850	5
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	.
	7-20 cm	144	23	81	29	29	38	89	27	52	43	74	33	605	17	316	18	312	18	288	20	63	44	84	45	82	39	3	.	83	41	136	31	222	26	133	32	19	86	294	27	263	22	119	26	323	22	488	16	504	20	370	17	62	34	50	27	277	23	79	35	68	38	42	47	31	42	75	35	110	45	45	44	92	28	59	37	98	32	202	27	97	43	183	29	163	30	59	36	128	32	67	31	116	32	55	33	143	34	50	45	67	37	42	32	58	46	72	35	91	27	45	47	90	39	115	44	179	23	145	27	90	36	113	35	44	49	39	55	87	38	111	35	57	33	47	36	62	60	13	67	93	35	21	51	56	48	56	39	79	53	94	31	76	45	46	50	54	41	10	75	8	70	485	16	439	21	565	19	65	36	67	70	58	38	187	44	68	33	41	35	73	53	29	35	0	.	11767	3
	20.1-30 cm	66	48	6	100	0	0	52	52	19	58	72	43	687	19	203	31	99	31	197	31	59	74	38	100	35	79	0	.	23	72	58	48	232	37	135	46	0	.	298	31	197	28	118	35	298	28	513	22	387	29	369	24	47	57	9	100	329	43	32	60	29	50	14	71	0	.	8	100	104	42	65	42	63	42	37	81	29	62	141	41	42	78	116	37	36	52	7	100	99	38	22	100	101	47	9	100	188	51	68	42	25	100	7	100	56	48	20	71	42	66	19	72	36	58	75	39	159	40	94	36	45	51	87	55	51	56	14	100	88	57	80	49	48	55	48	46	34	78	0	.	74	46	10	100	34	73	0	.	69	51	20	74	63	56	13	100	16	100	11	100	0	0	330	22	324	22	370	23	0	.	32	61	29	60	153	50	18	100	0	.	18	73	0	.	0	.	8367	5
	30.1-40 cm	49	58	53	60	0	.	38	71	0	.	54	58	696	22	271	34	70	51	96	41	18	100	0	.	36	71	0	.	0	.	110	47	146	42	68	63	0	.	166	39	133	43	64	59	284	38	287	39	235	37	122	47	48	71	37	100	195	54	19	100	18	100	0	.	0	.	0	.	20	100	0	.	125	74	35	100	0	.	103	47	13	100	35	71	18	100	0	.	16	100	62	59	15	100	0	0	20	100	47	72	14	100	0	.	33	71	0	.	0	0	0	0	0	.	20	100	56	58	0	.	38	72	22	100	0	.	0	.	13	100	85	50	63	60	74	53	17	100	0	.	69	100	0	.	26	100	0	.	19	100	0	.	68	58	0	.	74	62	0	.	0	.	336	32	158	39	397	27	0	.	84	68	0	.	81	50	20	100	0	0	0	.	0	.	0	.	5586	7
	>40 cm	47	100	90	72	0	.	225	59	48	100	24	100	818	35	205	57	54	100	183	54	26	100	0	.	0	.	0	.	0	.	53	72	442	79	139	62	0	.	255	50	266	82	100	75	198	55	450	44	144	62	111	80	25	100	110	59	325	43	0	.	0	.	0	.	0	.	0	.	42	100	28	100	90	100	0	.	0	.	156	51	0	.	186	74	0	.	0	0	0	0	31	100	35	100	100	100	99	75	32	100	0	.	65	100	35	100	66	100	38	100	124	76	0	0	42	100	88	76	201	58	0	.	48	100	0	.	102	100	29	100	0	.	65	71	133	100	28	100	0	.	167	65	0	.	0	.	0	.	43	100	0	.	55	71	0	.	144	84	135	79	0	.	116	61	157	53	83	74	0	.	99	71	84	58	31	100	0	.	0	.	0	.	0	.	0	.	7317	11
	total	306	29	230	41	29	38	404	39	119	63	224	33	2805	17	996	23	535	18	763	24	166	44	122	55	153	46	3	.	105	40	357	34	1043	38	474	40	19	86	1013	27	858	30	401	30	1104	24	1738	20	1270	21	972	19	181	34	206	39	1126	31	130	44	116	37	56	49	31	42	83	35	276	36	138	40	370	43	131	43	126	36	602	27	152	42	519	37	217	29	66	35	244	30	182	38	266	36	164	64	450	35	198	37	105	57	114	60	183	37	158	50	170	35	187	53	127	36	252	39	482	31	440	31	173	31	270	40	95	44	155	79	217	48	276	35	233	33	302	48	141	54	13	67	403	42	31	61	116	59	56	39	210	45	114	34	262	40	58	58	287	48	157	72	8	70	1267	18	1077	19	1415	18	65	36	282	45	171	38	453	37	107	53	41	35	91	54	29	35	0	.	33037	4

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. 2279078. Birmensdorf, Swiss Federal Research Institute WSL

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