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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NFI3
number of stems
development stage · main tree species
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
		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 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	22	83	0	0	58	70	59	63	101	65	328	39	213	37	236	71	168	48	119	87	64	74	72	56	382	33	72	68	1895	16
	fir	24	100	0	.	0	.	47	59	19	65	70	76	96	74	47	63	7	100	0	0	0	.	40	80	8	71	0	0	359	30
	pine	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	14	76
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	0	8	71	23	55	16	71	51	36
	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	14	100	4	100	39	62	92	73	71	42	18	100	42	79	33	100	112	100	10	100	0	.	0	.	4	100	51	78	489	31
	maple	0	.	0	.	0	.	0	.	0	.	0	.	26	61	10	74	4	100	10	100	10	100	0	.	0	.	0	.	60	38
	ash	0	.	0	.	0	.	19	100	4	100	15	77	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	52
	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	.	0	.	0	.
	other broadleaves	11	100	0	.	0	.	0	.	30	74	0	.	9	100	27	72	0	.	0	.	10	100	30	100	48	100	0	0	165	40
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0
	total	71	80	4	100	97	57	218	51	229	39	431	36	396	33	352	57	296	51	140	89	84	72	160	44	465	33	139	54	3081	14
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	304	60	81	54	267	59	84	59	425	39	93	55	393	32	176	46	241	43	193	60	136	54	199	32	809	23	92	52	3493	12
	fir	181	33	34	72	11	100	65	48	66	47	11	100	220	38	56	58	18	82	10	100	54	56	0	0	32	69	0	.	758	17
	pine	30	62	0	.	6	100	13	100	25	59	0	.	0	.	0	.	0	.	0	.	0	.	299	77	42	79	0	.	415	57
	larch	4	100	0	.	6	100	0	.	15	60	0	.	0	.	0	.	14	100	4	100	20	100	317	30	66	43	254	44	701	22
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	66	10	100	0	.	55	57
	other conifers	10	100	11	100	10	100	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	39	100	51	60	0	.	140	40
	beech	144	38	183	30	165	48	254	30	247	26	12	76	34	52	39	50	30	65	9	100	106	51	36	73	0	0	131	72	1391	13
	maple	70	47	28	71	50	67	48	48	51	40	41	62	19	64	24	61	20	72	0	.	40	61	45	91	10	100	19	71	464	18
	ash	29	86	27	85	24	71	46	54	40	71	23	100	4	100	10	100	20	100	0	.	20	71	10	100	0	.	131	58	385	26
	oak	18	71	14	76	17	79	8	71	16	61	0	.	0	.	0	.	0	.	0	.	0	.	82	76	0	.	10	100	164	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	182	53	182	53
	other broadleaves	93	43	83	44	50	60	19	100	87	52	0	.	60	64	74	62	71	47	68	71	0	.	265	50	94	44	543	39	1509	19
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0
	total	882	29	460	27	607	38	536	27	972	22	180	47	730	25	379	35	415	31	305	45	377	32	1337	26	1114	19	1362	24	9656	8
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3436	24	840	46	2347	27	3553	19	6946	15	1867	30	1916	29	3192	23	1432	27	469	35	373	38	2659	25	6229	16	988	29	36246	6
	fir	998	27	97	71	258	34	693	30	813	35	324	54	148	72	411	47	123	60	140	51	0	.	126	47	87	60	298	52	4517	13
	pine	169	72	138	69	55	50	233	65	235	38	0	.	190	73	10	100	196	72	24	85	0	.	1725	29	1603	26	0	.	4579	16
	larch	44	81	19	71	161	51	66	54	132	54	0	.	42	100	80	88	175	50	0	.	54	100	1972	23	1152	27	1686	23	5581	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	137	65	184	49	0	.	345	37
	other conifers	133	73	54	62	42	92	109	51	306	60	0	.	0	.	30	100	125	83	40	100	10	100	31	75	243	44	9	100	1132	24
	beech	1262	32	311	41	487	36	1030	23	1698	24	160	42	172	57	541	36	355	63	112	65	150	88	633	41	58	62	3886	19	10856	10
	maple	722	27	165	48	311	44	561	27	1546	23	216	48	112	40	528	48	542	30	215	50	180	44	342	27	96	60	663	30	6199	10
	ash	372	37	303	45	365	43	470	48	1491	21	126	61	134	53	635	43	264	40	268	46	67	77	367	36	171	42	1290	24	6321	10
	oak	292	65	112	100	295	60	102	45	385	38	35	100	11	100	0	.	201	72	0	.	0	.	589	33	0	.	980	32	3002	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	76	0	.	4354	19	4406	19
	other broadleaves	493	39	373	64	386	35	671	35	989	21	462	44	207	43	406	42	466	42	633	52	389	52	3091	19	2715	24	8027	12	19307	8
	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
	total	7921	15	2412	26	4707	19	7488	14	14541	10	3190	23	2933	23	5856	18	3879	19	1901	29	1222	34	11725	12	12537	11	22179	8	102491	4
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3311	17	916	40	393	40	1878	24	2749	18	1740	28	2406	27	1506	25	1093	35	236	45	1829	33	3985	20	3193	20	1746	25	26982	7
	fir	2171	16	215	76	253	60	651	36	454	32	508	39	263	36	626	34	104	71	243	50	83	51	507	44	102	65	203	67	6384	10
	pine	474	37	68	39	98	82	33	51	148	37	0	.	34	74	40	100	19	100	76	88	0	.	1223	26	1134	33	106	100	3453	16
	larch	0	.	8	100	0	.	86	71	184	55	0	.	0	.	0	.	0	.	0	.	158	65	1882	21	432	37	804	26	3555	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	270	46	228	66	4	100	502	39
	other conifers	109	100	76	78	40	100	70	50	26	73	0	.	0	.	23	71	69	100	10	100	10	100	10	100	31	100	10	100	484	33
	beech	5500	12	1728	23	713	38	1225	28	1114	20	405	52	952	37	1320	28	309	49	332	45	648	42	615	41	411	42	3033	20	18304	7
	maple	721	21	197	41	137	52	62	59	365	29	194	46	197	35	565	29	313	46	93	59	316	48	134	48	72	63	306	41	3673	11
	ash	653	28	427	41	447	36	171	37	517	29	181	58	377	64	488	30	104	54	105	76	419	72	182	42	235	46	832	30	5137	12
	oak	939	36	209	73	374	47	78	55	291	51	23	71	12	100	12	100	92	100	10	100	0	.	130	42	0	.	841	28	3011	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	0	.	3239	20	3269	20
	other broadleaves	816	23	307	37	222	56	282	33	614	28	107	54	120	43	535	36	218	56	77	58	507	46	689	29	430	33	3854	15	8776	9
	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
	total	14693	9	4151	18	2678	23	4534	17	6462	13	3158	23	4362	21	5113	18	2319	28	1183	36	3970	27	9659	14	6269	15	14977	9	83530	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5987	10	762	24	1154	25	1371	22	2111	15	2712	17	3840	19	3458	15	2691	19	1821	28	812	29	4080	15	8251	11	1878	19	40928	4
	fir	4432	11	525	26	613	39	1060	23	898	22	807	27	1415	23	1344	20	430	30	257	42	313	43	324	40	177	38	327	31	12921	6
	pine	712	25	195	27	137	47	44	75	397	23	0	.	89	63	30	52	31	89	151	58	107	100	496	34	1013	25	143	81	3546	12
	larch	0	.	81	55	78	80	34	60	94	39	17	100	4	100	12	74	62	69	0	.	19	100	1725	20	904	20	1156	24	4187	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	102	100	0	.	18	100	0	.	0	.	162	51	269	38	0	.	551	30
	other conifers	188	52	14	78	0	.	12	100	40	61	0	.	9	100	10	100	0	.	0	.	0	.	10	100	145	86	0	.	428	38
	beech	7717	9	2699	14	1888	18	1653	18	2063	14	641	29	1650	20	2436	19	1539	38	183	72	1074	28	198	63	370	31	1204	29	25316	5
	maple	1675	12	435	23	324	59	304	38	512	23	466	31	436	41	756	24	428	30	94	52	260	41	167	55	29	58	37	76	5922	8
	ash	923	22	503	35	293	30	293	46	551	19	169	41	219	36	608	26	204	43	45	64	198	38	9	100	20	100	70	46	4106	10
	oak	402	36	212	31	506	33	156	31	133	33	19	73	51	61	40	68	123	84	13	100	0	.	8	71	63	100	125	46	1849	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	4	100	0	.	580	30	588	30
	other broadleaves	1164	17	246	27	342	36	220	40	475	31	255	39	241	39	286	31	486	32	126	45	84	42	499	33	43	50	867	27	5336	9
	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
	total	23199	6	5671	13	5337	15	5147	14	7274	10	5086	15	8057	13	8981	12	6015	16	2691	23	2867	20	7682	12	11284	10	6387	12	105678	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2589	13	321	31	1595	17	1982	14	2149	13	1872	32	4657	11	5172	12	2130	18	1795	21	1100	24	3154	15	7106	10	2565	14	38188	4
	fir	3141	12	345	39	525	24	2210	14	1251	16	470	31	4354	12	1794	15	597	28	544	36	189	39	416	41	280	33	442	33	16559	5
	pine	82	47	58	53	75	52	125	34	430	20	0	.	67	100	34	65	0	.	70	100	0	.	219	47	302	39	46	71	1508	15
	larch	8	71	10	100	95	48	4	100	51	45	0	.	28	79	54	53	50	52	0	.	92	61	1880	16	1082	17	755	19	4108	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	194	35	413	31	0	.	607	24
	other conifers	20	71	10	100	21	73	70	56	78	60	0	.	25	85	42	61	0	.	0	.	0	.	0	.	0	.	0	.	265	27
	beech	2442	11	1383	19	1347	18	2324	12	3638	12	538	30	3806	14	2589	14	714	35	704	27	510	35	68	59	395	32	949	29	21407	5
	maple	716	22	315	27	192	38	258	30	559	23	84	44	612	22	498	25	204	34	184	33	141	39	184	49	97	44	23	61	4065	8
	ash	285	28	202	27	520	27	370	25	553	20	86	69	205	44	437	31	65	68	135	44	28	57	87	46	103	65	0	.	3075	10
	oak	131	40	137	28	342	31	174	37	426	20	30	86	21	76	42	51	0	.	13	77	0	.	9	100	19	82	220	83	1564	16
	sweet chestnut	0	.	0	.	135	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	664	27	799	25
	other broadleaves	638	27	268	50	250	47	221	29	666	28	21	71	213	31	337	40	433	33	149	34	78	59	227	41	186	44	506	25	4193	10
	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	10052	9	3049	16	5096	13	7738	10	9802	9	3101	23	13986	8	10997	9	4194	15	3593	17	2138	20	6439	11	9984	9	6172	11	96339	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3381	14	9	72	132	37	456	33	975	26	2490	19	5879	12	1175	24	4776	14	918	32	1642	24	4880	13	9339	11	3657	15	39709	5
	fir	3218	14	8	71	160	41	671	30	310	46	1104	31	2348	15	367	37	235	48	228	54	244	52	370	35	273	33	402	37	9938	8
	pine	302	37	177	51	43	74	39	73	102	52	0	0	481	60	21	100	127	38	147	58	171	69	1118	25	1200	24	129	64	4056	13
	larch	14	78	0	.	0	0	10	100	14	77	0	.	0	.	0	.	31	63	0	.	28	63	2180	17	2033	15	2429	15	6738	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	72	49	100	0	.	1083	23	1612	21	29	74	2782	15
	other conifers	39	61	71	87	0	.	103	55	287	56	0	.	10	100	10	100	20	100	41	100	28	100	0	.	320	55	0	.	931	28
	beech	2264	14	293	55	302	39	865	34	1130	23	601	54	2070	19	344	38	527	35	101	72	563	38	539	41	517	37	3244	17	13360	8
	maple	653	22	72	52	74	60	95	63	421	26	311	29	702	41	201	38	653	29	29	100	209	44	277	32	303	42	387	29	4387	11
	ash	189	32	101	73	97	40	139	38	530	32	55	57	441	36	299	40	194	54	20	71	102	58	346	56	315	62	449	32	3276	13
	oak	272	48	145	61	8	71	71	59	171	38	0	.	46	71	0	.	33	71	24	71	4	100	162	46	64	60	298	37	1299	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	96	17	100	2126	18	2242	18
	other broadleaves	475	24	272	41	301	66	158	42	601	27	181	33	795	25	268	41	661	30	126	51	147	43	818	25	1005	37	2957	14	8764	8
	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
	total	10806	9	1149	38	1117	29	2607	21	4541	18	4743	18	12772	10	2686	21	7265	13	1683	27	3139	20	11872	10	16998	9	16106	8	97483	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19029	6	2930	19	5947	13	9382	10	15458	8	11103	10	19305	7	14914	8	12530	8	5551	13	5957	14	19028	7	35310	5	10997	8	187441	2
	fir	14165	6	1224	21	1820	18	5397	10	3811	12	3294	15	8845	8	4644	10	1514	17	1423	20	884	23	1784	19	960	17	1673	18	51437	3
	pine	1769	17	637	23	415	28	487	34	1340	13	0	0	861	39	135	40	372	41	469	33	278	57	5090	14	5294	13	424	43	17570	7
	larch	69	54	118	41	340	33	200	37	490	27	17	100	74	64	147	52	336	31	4	100	371	36	9966	8	5692	9	7100	9	24922	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	102	100	24	100	27	71	49	100	0	.	1891	16	2717	15	33	66	4843	11
	other conifers	499	36	236	40	113	52	364	26	738	34	0	.	44	57	115	39	214	59	112	55	48	65	90	53	790	31	18	71	3381	13
	beech	19343	5	6600	9	4942	11	7442	9	9962	7	2375	19	8727	9	7303	10	3586	20	1451	20	3051	16	2089	21	1754	18	12498	9	91123	3
	maple	4557	8	1213	15	1087	24	1328	16	3454	12	1311	17	2105	18	2581	15	2164	15	626	23	1154	20	1149	17	608	25	1434	18	24770	4
	ash	2451	13	1561	19	1747	16	1509	19	3685	11	655	25	1388	23	2477	16	851	22	572	28	834	39	1002	25	844	29	2772	15	22346	5
	oak	2053	21	829	27	1541	20	589	19	1421	17	106	45	141	36	93	39	449	45	60	42	4	100	981	23	146	51	2473	18	10889	8
	sweet chestnut	0	.	0	.	135	70	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	186	58	17	100	11145	10	11487	9
	other broadleaves	3689	10	1549	21	1552	20	1571	18	3462	11	1027	23	1644	15	1931	17	2336	16	1179	30	1216	26	5620	12	4520	17	16754	7	48050	4
	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	0	0	0
	total	67625	3	16896	8	19638	7	28267	6	43821	4	19887	8	43235	5	34364	6	24383	7	11497	10	13797	11	48875	5	58652	4	67323	3	498258	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. 1356647. Birmensdorf, Swiss Federal Research Institute WSL

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