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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NFI2
growing stock (stemwood)
development stage · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	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	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	37	100	0	.	16	.	119	71	83	69	104	81	13	96	1	100	160	77	449	45	5	100	986	28
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	0	28	100	11	100	0	100	0	.	0	0	0	.	0	0	40	76
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	1	100	0	.	0	.	4	76
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	13	72	17	100	38	73	68	50
	Arolla pine	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	.
	beech	0	.	0	.	0	.	51	73	0	.	0	.	8	100	20	100	0	.	0	.	8	100	0	.	0	.	17	80	103	44
	maple	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	15	71
	ash	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	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	14	100
	other broadleaves	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	8	100	0	.	2	100	0	.	0	.	9	82
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	0	.	0	.	88	60	0	.	16	.	127	68	131	59	115	76	32	54	8	100	176	71	466	43	75	52	1240	23
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	156	50	62	84	58	55	119	42	177	39	172	58	439	36	142	40	101	50	69	55	74	104	66	49	814	22	112	48	2561	12
	fir	72	44	2	100	0	.	72	60	36	74	8	100	184	52	41	54	51	71	2	100	23	91	17	98	136	52	5	73	652	22
	pine	8	100	0	.	39	104	38	100	61	50	0	.	0	.	0	.	0	.	0	.	0	.	1	100	74	56	0	.	221	35
	larch	14	100	0	.	30	73	0	.	14	59	0	.	0	.	7	100	2	75	11	100	0	.	186	36	203	40	68	45	535	21
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	85	0	.	0	.	16	85
	other conifers	0	.	0	.	0	.	1	100	4	86	0	.	0	.	0	.	0	.	3	71	0	.	0	.	3	100	0	.	10	46
	beech	90	54	78	79	31	77	29	56	151	34	38	75	29	65	111	45	1	100	1	83	92	53	14	96	54	62	97	53	816	17
	maple	8	54	0	.	1	100	35	84	30	42	25	61	11	71	47	47	38	87	2	100	34	69	10	64	14	94	20	73	276	23
	ash	1	100	0	.	0	.	21	69	51	72	2	100	21	64	25	97	0	.	1	100	5	73	2	80	2	100	7	83	136	36
	oak	13	100	0	.	2	100	15	96	91	59	0	.	0	.	0	.	0	.	0	.	0	.	9	66	0	.	0	.	129	44
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	74	26	74
	other broadleaves	17	73	0	.	0	.	6	46	17	55	9	78	9	57	12	55	2	71	4	75	6	69	29	38	23	40	67	38	201	18
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	379	30	142	63	161	59	335	36	632	22	255	45	692	30	385	28	196	37	93	45	234	45	351	26	1321	19	402	27	5578	9
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1520	18	395	34	570	28	1689	20	2623	15	802	29	1770	19	1041	22	631	28	490	32	354	39	665	25	1075	23	800	27	14425	6
	fir	692	25	45	53	258	45	591	29	350	27	139	43	244	37	487	28	21	69	161	41	131	70	99	69	39	69	189	55	3446	11
	pine	135	44	133	36	91	54	47	45	231	34	0	.	91	75	6	83	47	72	37	100	94	73	855	19	342	27	18	95	2128	12
	larch	63	75	36	74	109	77	47	57	149	46	0	.	0	.	0	.	29	61	0	.	25	69	370	21	276	27	554	29	1656	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	33	100	16	100	0	0	0	.	0	.	125	41	69	50	0	.	242	30
	other conifers	32	75	24	64	75	88	41	80	62	49	0	100	1	100	9	75	3	100	1	100	-11	100	11	90	24	51	7	100	278	31
	beech	1253	19	451	27	137	31	567	25	600	28	103	57	439	31	386	30	114	56	90	41	207	56	163	35	78	58	1956	18	6543	8
	maple	147	30	96	42	101	61	97	36	237	33	36	53	103	30	168	31	185	31	94	49	141	49	42	46	47	47	127	33	1620	11
	ash	303	35	150	35	170	44	149	36	500	25	90	70	109	55	338	28	33	73	70	56	129	73	74	36	58	42	357	23	2531	11
	oak	343	32	72	54	316	39	65	58	112	31	3	100	14	71	2	100	29	55	2	100	0	.	66	35	15	71	475	24	1513	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	54	17	100	1845	15	1921	14
	other broadleaves	197	26	140	41	126	34	167	31	286	27	38	48	172	43	221	32	80	41	80	42	83	62	392	20	395	25	2108	10	4484	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4685	11	1543	20	1951	18	3460	15	5149	11	1212	23	2974	15	2673	15	1173	20	1026	24	1153	29	2918	11	2435	14	8433	7	40786	4
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3309	14	600	29	1562	27	1515	21	2258	18	3237	18	3515	17	2637	20	2457	20	1153	32	1301	35	1883	19	5036	13	688	34	31151	5
	fir	2638	13	304	38	262	40	1077	27	472	33	943	25	1089	22	902	23	165	41	244	42	55	54	106	40	164	44	285	49	8705	8
	pine	531	27	322	31	111	41	44	63	355	29	0	.	78	58	2	100	23	59	205	44	0	.	495	37	755	24	104	77	3024	12
	larch	0	.	98	63	128	78	98	53	56	47	0	.	13	100	34	100	25	84	0	.	37	74	1056	20	1097	17	649	30	3289	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	115	55	338	32	0	.	456	27
	other conifers	3	72	55	91	53	100	30	100	5	76	0	.	7	84	2	100	0	.	0	.	1	100	0	.	1	100	0	.	156	51
	beech	5730	10	1807	17	1188	21	1143	23	1399	17	397	40	1355	22	1184	25	429	33	282	39	304	49	235	42	271	46	738	26	16461	6
	maple	785	14	293	25	80	41	107	50	456	23	155	42	136	34	201	31	114	32	23	57	105	45	52	48	7	71	0	.	2513	9
	ash	509	37	304	32	340	29	188	32	936	20	84	50	147	53	297	33	85	46	21	48	71	74	35	63	-4	100	8	81	3021	11
	oak	268	37	181	35	227	42	192	36	326	47	8	81	59	81	31	79	84	58	1	100	0	.	23	51	0	.	110	53	1511	16
	sweet chestnut	0	.	0	.	31	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	611	29	653	28
	other broadleaves	224	23	270	62	186	43	51	36	417	29	34	37	133	36	124	35	188	39	22	85	56	54	185	39	31	42	233	42	2154	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	13997	7	4234	14	4167	15	4445	15	6679	11	4858	16	6532	13	5414	15	3572	17	1950	24	1930	27	4196	13	7695	10	3427	14	73095	3
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7345	10	1479	25	4485	17	4312	15	6741	12	3931	18	6701	12	6049	14	3379	19	1654	28	1346	26	3919	14	8490	10	2113	20	61945	4
	fir	5239	12	712	31	647	30	3076	17	2783	14	712	35	2960	16	2131	20	466	30	449	45	258	51	217	47	144	40	385	35	20178	6
	pine	721	27	314	33	407	48	258	40	1144	19	0	.	186	83	141	46	38	100	76	75	0	.	381	36	793	24	129	77	4590	11
	larch	11	100	17	71	60	72	32	100	247	46	37	100	22	100	38	73	120	58	0	.	193	60	2117	17	1520	16	640	24	5054	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	199	36	484	28	0	.	683	23
	other conifers	3	71	28	83	20	100	111	72	33	73	0	.	34	100	3	58	0	.	0	.	0	.	0	.	0	.	0	.	231	41
	beech	4930	10	3096	14	2232	19	2450	16	3813	12	748	32	1901	20	2256	19	1304	33	468	43	542	38	17	66	87	33	408	27	24250	5
	maple	491	16	275	33	85	41	98	43	491	24	61	44	307	31	423	25	181	35	73	40	122	49	10	61	22	58	0	.	2639	9
	ash	534	23	344	31	387	31	437	58	953	19	127	57	242	29	235	28	77	62	124	76	51	50	38	72	42	76	22	79	3614	11
	oak	383	30	276	36	817	29	260	30	611	21	36	68	14	75	64	56	0	.	39	80	0	.	2	100	102	73	49	91	2653	13
	sweet chestnut	0	.	0	.	8	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	366	48	374	47
	other broadleaves	359	22	202	37	107	51	171	43	406	26	23	55	145	42	119	36	245	41	37	57	86	44	45	41	42	37	94	47	2082	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	20016	7	6743	12	9255	12	11205	11	17222	8	5674	16	12513	10	11459	11	5811	15	2921	24	2597	22	6945	11	11726	9	4207	13	128293	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4076	14	661	39	1697	24	5305	15	5799	14	2504	25	7258	13	5869	15	2775	20	1317	28	1897	24	2532	18	7326	12	2703	18	51720	4
	fir	3067	16	91	50	793	31	1938	19	1791	22	792	36	3935	15	1591	21	541	40	152	62	260	57	377	45	474	33	255	55	16058	7
	pine	262	45	9	100	85	58	260	36	754	26	0	.	65	100	34	64	0	.	0	.	0	.	143	70	488	42	49	60	2149	16
	larch	22	100	10	100	71	71	29	74	214	44	0	.	124	86	63	66	59	59	0	.	97	71	1842	18	1406	22	885	23	4821	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	71	200	36	0	.	216	34
	other conifers	0	.	0	.	48	100	128	59	113	86	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	290	45
	beech	2379	17	1919	21	924	26	3126	16	2961	15	412	44	1967	24	1543	24	273	58	110	49	303	39	45	79	362	46	320	39	16644	7
	maple	177	33	130	38	77	51	102	46	210	31	43	50	140	36	160	33	109	54	13	71	96	50	4	100	13	159	13	71	1285	13
	ash	73	57	59	43	171	49	306	37	392	30	18	71	40	64	116	54	22	100	3	100	11	100	11	77	6	100	4	100	1230	16
	oak	235	49	357	34	360	43	283	29	628	26	22	100	29	73	56	67	0	.	15	100	0	.	1	100	0	.	10	61	1996	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	906	30	906	30
	other broadleaves	104	36	112	54	18	56	190	56	257	32	6	52	33	45	33	54	83	84	33	57	17	100	34	61	22	69	86	29	1029	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	10394	10	3347	18	4244	18	11667	11	13118	10	3797	21	13592	11	9465	13	3861	18	1644	27	2681	22	5006	14	10299	10	5231	13	98345	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2530	13	40	77	247	50	490	32	227	34	1792	21	3699	13	2032	18	2801	20	483	37	913	25	3213	13	3777	13	2208	16	24451	5
	fir	1548	16	50	97	38	62	601	34	244	48	784	30	3118	16	610	28	324	35	77	69	288	36	228	37	249	46	259	50	8418	8
	pine	185	36	18	100	15	100	0	.	80	50	6	100	90	53	5	100	15	100	36	74	75	76	227	34	333	31	65	61	1149	15
	larch	0	.	0	.	0	.	0	.	37	83	0	.	0	.	65	100	43	92	0	.	20	100	2196	17	640	21	1266	15	4267	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	9	100	0	.	341	26	293	30	6	91	653	19
	other conifers	13	53	0	.	0	.	5	100	26	48	0	.	1	100	2	100	0	.	4	72	0	.	1	100	17	74	0	.	68	29
	beech	1030	17	63	64	147	56	252	48	593	26	258	36	852	22	719	22	271	34	75	55	359	34	36	100	92	39	762	23	5510	8
	maple	232	26	25	100	18	52	64	73	156	36	78	37	98	27	281	30	109	42	13	80	60	57	18	49	19	57	26	52	1198	12
	ash	255	30	12	72	131	60	97	55	277	40	68	52	63	60	222	39	32	59	8	100	41	96	73	49	76	73	92	42	1446	15
	oak	5	64	6	100	0	.	121	69	136	43	13	89	0	.	0	.	0	.	12	100	0	0	30	66	38	100	152	45	513	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	100	10	100	1167	22	1239	21
	other broadleaves	128	29	39	65	55	70	39	50	157	40	43	34	36	48	113	36	97	35	20	61	79	56	135	58	51	52	543	22	1535	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5925	11	251	55	651	41	1669	24	1933	21	3043	18	7956	11	4049	16	3696	17	736	31	1836	20	6559	10	5595	11	6547	9	50446	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18935	5	3237	15	8619	11	13467	8	17826	7	12454	9	23501	6	17854	7	12247	9	5179	14	5885	13	12438	7	26967	5	8630	8	187239	2
	fir	13256	6	1204	21	1998	17	7355	10	5676	10	3378	15	11531	8	5790	10	1579	18	1086	24	1015	24	1044	22	1206	19	1378	21	57496	3
	pine	1842	15	796	19	748	29	647	23	2623	12	6	100	511	38	188	37	122	45	358	32	168	53	2103	15	2785	13	365	38	13264	6
	larch	110	50	161	43	397	37	205	34	717	23	37	100	159	69	207	43	278	33	11	100	371	37	7779	8	5158	9	4101	9	19692	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	33	100	16	100	7	71	9	100	0	.	812	17	1385	15	6	91	2267	11
	other conifers	51	50	107	54	196	51	315	37	242	43	0	100	44	80	15	47	3	100	7	48	-10	110	12	85	45	40	7	100	1033	19
	beech	15412	5	7413	9	4658	12	7618	9	9516	7	1956	18	6549	10	6219	10	2392	20	1027	23	1815	18	511	24	943	23	4297	10	70327	2
	maple	1847	9	819	16	361	24	503	21	1580	12	398	21	794	15	1279	13	736	17	224	26	558	22	137	25	123	31	186	25	9546	4
	ash	1674	15	868	18	1199	17	1198	24	3109	10	390	28	621	21	1232	15	249	29	227	46	309	38	233	25	180	38	489	19	11979	5
	oak	1247	17	891	19	1722	19	938	17	1904	14	81	43	116	47	154	38	113	45	69	53	0	0	131	25	154	55	796	19	8315	7
	sweet chestnut	0	.	0	.	39	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	131	54	28	73	4936	10	5133	10
	other broadleaves	1029	11	764	27	491	23	625	23	1540	13	152	20	528	21	621	16	696	21	203	24	327	26	822	16	565	19	3131	8	11494	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	55404	3	16259	7	20428	7	32870	5	44733	4	18853	8	44386	5	33577	6	18423	8	8401	12	10438	10	26153	5	39539	4	28322	3	397785	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. 1230966. Birmensdorf, Swiss Federal Research Institute WSL

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