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
total wood volume (stemwood)
ownership (2 categories) · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
ownership (2 categories)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	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	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	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	.
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	.
	spruce	2380	22	1344	41	1011	39	9582	12	668	39	5066	19	1	100	3265	25	28876	7	1964	27	2491	23	2367	23	868	54	3519	22	5770	18	1342	31	1786	26	4327	19	933	33	7263	21	3013	24	9200	12	14410	11	590	42	2743	20	114780	3
	fir	1409	28	297	65	1039	43	6552	13	415	51	1814	28	0	.	512	48	2174	23	2837	24	1165	29	2898	21	179	80	845	31	1837	25	184	59	1117	24	1802	27	599	59	1348	29	824	37	5839	14	1161	30	209	52	391	43	37445	5
	pine	116	68	0	.	0	.	1218	33	249	45	243	74	67	100	0	.	2647	18	438	39	170	65	148	60	0	.	231	80	41	100	322	42	368	31	80	90	278	57	159	56	178	71	200	53	1707	20	0	.	462	30	9322	9
	larch	236	47	0	.	0	.	152	51	15	100	63	92	0	.	50	100	7631	11	22	100	24	68	41	100	0	.	20	100	182	55	23	73	20	100	57	100	14	100	3918	15	0	.	444	32	6086	11	40	100	177	39	19215	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1543	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	848	26	0	.	0	.	2399	15
	other conifers	198	50	0	.	0	.	225	69	252	100	0	.	0	.	3	100	37	43	7	100	350	71	8	100	0	.	8	100	6	89	0	.	57	58	4	100	2	100	81	100	10	100	108	56	10	65	0	.	15	83	1380	30
	beech	2994	15	52	51	136	65	7073	11	1722	22	1388	26	0	.	843	33	1262	24	2610	18	1056	33	606	21	146	81	1198	34	2532	21	1837	23	3384	16	1512	30	733	36	4489	14	345	40	4753	14	700	33	160	58	1875	19	43406	4
	maple	460	23	0	.	118	60	1271	16	339	34	252	32	0	.	169	35	302	27	359	30	210	29	435	35	73	55	170	65	719	28	99	54	553	23	171	36	191	38	210	43	75	57	824	17	229	44	76	57	757	26	8062	6
	ash	637	36	0	.	125	88	447	32	256	51	748	37	0	.	71	50	109	33	392	36	60	56	71	57	331	82	109	81	394	33	162	49	262	31	256	49	186	44	483	49	70	56	427	30	279	42	78	75	877	30	6829	10
	oak	352	50	0	.	0	.	341	29	198	52	102	49	81	70	0	.	65	99	169	44	91	69	80	70	0	.	0	.	91	60	143	49	215	43	0	.	246	54	402	34	9	100	577	31	84	47	0	.	475	30	3721	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	443	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1874	22	0	.	6	100	66	61	0	.	0	.	2388	19
	other broadleaves	516	24	0	.	16	71	709	22	194	36	426	46	6	100	92	55	1551	22	297	41	100	48	62	52	37	55	56	49	335	39	37	51	286	38	128	58	220	53	2138	16	73	52	1095	35	694	19	11	61	447	32	9525	8
	indeterminable	0	.	0	.	1	100	13	60	0	.	1	100	0	.	0	.	55	44	14	89	2	100	15	100	35	72	10	87	30	61	0	.	31	86	0	.	1	100	4	71	22	100	33	54	71	44	0	.	21	100	359	20
	total	9299	12	1693	40	2445	36	27583	8	4308	20	10102	14	155	83	5004	21	46693	6	9110	15	5718	18	6730	16	1668	41	6166	18	11936	14	4150	20	8078	13	8337	16	3403	22	22376	9	4620	21	23506	8	26345	8	1163	36	8240	13	258830	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	.
	spruce	384	47	1310	48	1368	39	20855	9	143	80	3589	23	3	100	234	58	7112	17	615	37	5308	17	2383	22	812	47	888	52	4826	19	317	51	50	70	1439	39	1858	25	230	45	1552	42	3413	20	2384	22	184	100	3489	20	64747	5
	fir	191	48	313	50	608	61	10794	11	194	46	694	48	4	100	2	100	198	68	1378	33	5732	16	1784	30	232	75	507	69	2098	25	0	.	337	40	361	56	786	35	34	100	198	59	1663	30	616	49	119	71	1783	21	30626	6
	pine	26	100	0	.	0	.	323	40	102	57	104	78	14	100	0	.	323	39	3	100	76	98	0	.	4	100	0	.	53	100	37	80	35	100	0	.	143	47	0	.	51	100	34	71	592	37	0	.	488	30	2408	15
	larch	49	71	0	.	0	.	129	75	12	100	42	83	0	.	0	.	707	37	0	.	5	100	0	.	0	.	0	.	55	81	14	100	0	.	0	.	64	72	152	97	0	.	83	86	3086	28	0	.	108	76	4506	20
	Arolla pine	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	49	0	.	0	.	130	43
	other conifers	0	.	0	.	14	74	304	55	0	.	0	.	0	.	0	.	11	100	4	100	2	71	0	.	0	.	0	.	338	100	66	100	0	.	0	.	3	100	1	100	0	.	0	.	15	100	0	.	59	58	817	47
	beech	1903	34	273	56	397	41	6771	14	870	41	2121	26	0	.	164	70	682	49	911	39	2753	20	758	28	357	50	227	63	3039	22	377	55	1293	35	394	44	1003	23	1571	29	665	46	2243	22	268	64	166	63	2515	20	31719	6
	maple	190	47	25	75	170	57	1123	18	108	47	316	35	0	.	223	61	131	52	190	36	358	29	143	44	77	66	29	71	1057	22	3	100	175	54	169	47	299	57	54	53	96	60	384	30	163	41	24	100	539	25	6044	8
	ash	225	59	0	.	191	61	994	29	88	70	659	40	10	100	287	49	234	52	633	42	606	47	28	78	238	74	14	59	1077	31	80	91	229	45	361	55	654	34	334	36	143	83	939	30	95	47	37	100	520	29	8676	10
	oak	339	49	0	.	0	.	314	32	4	100	139	62	319	71	0	.	85	100	188	74	219	61	18	100	5	100	0	.	28	100	70	100	29	71	19	100	717	58	654	26	86	72	627	37	151	53	0	.	97	41	4109	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	127	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4097	16	0	.	0	.	36	100	0	.	0	.	4261	16
	other broadleaves	220	54	0	.	66	79	749	23	45	60	174	36	1	100	125	66	411	38	187	58	218	35	26	60	44	91	5	100	247	35	2	100	98	87	142	55	269	44	1863	22	66	50	265	49	392	30	0	.	279	40	5894	10
	indeterminable	0	.	0	.	10	100	133	37	16	81	7	100	0	.	0	.	43	58	0	.	16	60	8	100	0	.	0	.	3	100	0	.	0	.	0	.	0	.	2	100	0	.	11	76	21	92	0	.	8	86	278	23
	total	3527	24	1921	42	2824	31	42501	7	1581	34	7845	17	351	71	1034	39	10069	14	4109	23	15294	12	5148	19	1769	35	1670	47	12821	14	966	42	2246	30	2886	28	5795	17	8993	12	2857	31	9661	15	7931	16	530	62	9885	14	164214	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	.
	spruce	2764	20	2654	31	2378	28	30437	7	811	35	8655	14	4	75	3498	24	35988	6	2579	22	7800	14	4750	15	1680	36	4407	21	10597	13	1660	27	1835	26	5767	17	2791	20	7493	20	4565	21	12614	10	16793	9	773	40	6232	14	179526	2
	fir	1600	26	610	41	1647	35	17346	8	608	38	2508	24	4	100	514	48	2373	22	4215	19	6897	14	4682	17	411	55	1352	32	3935	18	184	59	1454	21	2163	24	1385	32	1381	29	1022	32	7502	13	1777	26	328	42	2174	19	68071	4
	pine	143	58	0	.	0	.	1541	27	351	36	347	57	81	84	0	.	2970	17	441	39	246	54	148	60	4	100	231	80	95	71	359	38	403	30	80	90	420	41	159	56	230	59	233	46	2299	18	0	.	950	21	11730	8
	larch	286	41	0	.	0	.	281	44	27	71	105	64	0	.	50	100	8338	10	22	100	28	59	41	100	0	.	20	100	237	46	36	59	20	100	57	100	78	61	4070	15	0	.	526	30	9172	12	40	100	285	37	23720	6
	Arolla pine	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	1548	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	961	24	0	.	0	.	2529	15
	other conifers	198	50	0	.	14	74	529	43	252	100	0	.	0	.	3	100	48	41	11	74	352	71	8	100	0	.	8	100	344	98	66	100	57	58	4	100	5	72	82	98	10	100	108	56	25	66	0	.	75	49	2197	26
	beech	4896	16	325	48	534	34	13844	9	2592	20	3509	19	0	.	1006	29	1943	23	3521	17	3809	17	1363	18	503	42	1425	30	5570	15	2215	21	4678	15	1906	25	1736	20	6060	12	1010	33	6996	12	967	30	326	43	4390	14	75125	3
	maple	650	21	25	75	288	41	2394	12	447	28	568	24	0	.	393	37	432	24	549	23	567	21	578	28	149	43	199	57	1775	18	102	53	728	22	340	30	490	38	263	36	171	42	1208	15	392	31	100	50	1296	18	14106	5
	ash	862	31	0	.	316	51	1441	22	344	42	1407	27	10	100	358	41	343	37	1025	29	665	43	99	46	569	57	122	73	1470	24	242	45	491	27	617	38	840	28	817	32	213	59	1366	23	374	33	115	60	1397	21	15505	7
	oak	692	35	0	.	0	.	655	21	203	50	241	42	400	58	0	.	150	71	357	44	310	47	98	60	5	100	0	.	119	52	213	47	244	39	19	100	963	45	1056	20	95	65	1204	24	236	38	0	.	572	26	7829	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	570	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5971	12	0	.	6	100	102	53	0	.	0	.	6649	11
	other broadleaves	736	23	0	.	83	65	1457	16	238	31	600	35	8	86	216	44	1962	19	484	34	319	28	87	40	81	55	61	46	582	26	39	48	383	36	270	40	489	34	4001	13	139	36	1360	30	1086	16	11	61	726	25	15419	6
	indeterminable	0	.	0	.	11	93	146	34	16	81	8	88	0	.	0	.	97	35	14	89	18	55	22	74	35	72	10	87	33	56	0	.	31	86	0	.	1	100	6	58	22	100	44	45	92	40	0	.	29	76	638	15
	total	12825	11	3614	29	5270	23	70085	5	5889	17	17948	11	506	55	6038	19	56763	5	13219	12	21012	10	11877	11	3437	27	7836	17	24757	10	5116	18	10324	12	11223	14	9198	13	31370	6	7477	17	33167	7	34276	7	1693	31	18125	9	423045	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. 1308163. Birmensdorf, Swiss Federal Research Institute WSL

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