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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NFI4
growing stock (stemwood)
development stage · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		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	47	100	0	.	0	.	2	100	-18	100	258	69	21	96	106	99	125	73	0	.	0	0	6	100	211	43	205	91	961	32
	fir	0	.	0	.	0	.	0	.	0	.	127	84	0	0	10	100	0	.	0	.	0	.	0	.	0	0	0	.	137	78
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0
	larch	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	62	135	50	83	100	285	39
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	0	0	.	27	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	27	74	0	.	-15	138	0	.	18	100	14	100	11	100	3	100	0	.	0	.	0	.	0	.	24	100	0	.	82	55
	maple	13	100	0	.	0	.	0	.	0	.	5	100	12	100	13	100	0	.	0	.	0	.	0	.	0	.	0	.	44	52
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	oak	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	1	100	0	.	0	.	4	100	0	.	6	100	0	.	3	72	0	.	0	.	0	.	0	.	0	.	0	.	14	53
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	total	88	76	0	.	6	404	6	100	0	100	410	56	44	70	139	86	125	73	0	.	0	0	89	64	370	36	288	94	1564	27
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	69	76	92	80	36	97	245	65	438	38	367	36	542	34	215	61	475	59	143	80	153	73	203	44	800	41	230	51	4008	15
	fir	130	83	0	.	99	66	121	58	179	56	106	93	190	64	103	59	53	71	2	100	69	73	15	100	9	100	1	100	1077	23
	pine	47	100	0	.	0	.	16	100	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	70	79	0	.	138	54
	larch	0	.	0	.	0	.	0	.	72	71	0	.	16	100	0	.	114	74	0	.	0	0	254	51	64	68	86	75	606	30
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	2	100
	other conifers	0	.	0	.	0	.	0	.	3	100	0	.	1	100	0	.	0	.	0	.	0	.	5	100	3	76	0	.	12	54
	beech	120	47	259	60	57	48	445	38	408	28	26	100	46	55	189	46	1	100	22	100	0	.	2	100	14	100	18	73	1607	17
	maple	8	78	7	72	11	75	31	79	91	78	20	71	50	58	36	88	22	100	10	100	67	59	1	1336	1	100	24	85	380	27
	ash	5	70	34	116	25	97	1	100	39	70	28	85	5	77	17	100	8	100	0	.	8	100	14	100	0	.	13	100	197	33
	oak	12	100	0	.	0	.	20	95	126	86	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	6	100	164	68
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	16	100
	other broadleaves	24	51	5	60	99	97	36	58	42	44	27	67	19	63	18	100	17	69	6	100	20	46	26	71	44	68	52	52	435	26
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	414	45	397	53	327	43	914	31	1401	23	575	34	869	31	578	40	692	46	183	64	318	53	521	36	1006	34	447	37	8642	10
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1096	29	468	44	601	37	1197	25	2326	22	693	41	884	35	974	31	825	34	406	50	147	47	814	31	1397	21	441	32	12269	8
	fir	623	39	20	100	108	58	509	35	423	40	51	81	509	37	300	44	73	71	9	100	71	100	111	66	45	71	103	71	2954	15
	pine	68	56	43	100	17	100	89	73	156	45	0	.	5	100	43	100	0	.	9	100	0	.	457	48	403	32	15	100	1304	22
	larch	12	100	9	100	121	59	4	100	51	54	10	100	14	100	0	.	98	59	0	.	0	.	580	27	420	35	563	28	1881	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	98	102	66	0	.	179	56
	other conifers	-2	374	36	77	1	100	56	56	50	61	0	.	0	.	0	.	45	83	0	.	0	.	8	85	30	45	50	100	275	30
	beech	478	36	317	42	233	35	271	35	731	41	29	63	263	48	107	58	9	100	7	100	147	64	183	66	1	100	780	29	3557	14
	maple	194	33	129	43	26	83	68	49	515	32	83	65	67	42	91	50	90	46	77	67	14	59	101	37	54	66	188	34	1697	13
	ash	183	43	118	45	45	65	133	62	270	31	56	65	35	62	213	55	30	69	84	54	18	68	250	43	48	51	206	33	1689	14
	oak	215	60	87	100	63	76	21	94	38	58	0	.	0	.	0	.	25	100	0	.	0	.	36	53	0	.	299	39	785	26
	sweet chestnut	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	79	0	.	1571	23	1605	22
	other broadleaves	140	39	102	41	109	43	194	42	185	32	70	66	76	45	131	49	147	63	155	54	56	67	445	22	613	29	1891	19	4313	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
	total	3004	19	1328	26	1325	28	2543	19	4745	16	991	33	1853	25	1859	25	1343	26	746	34	453	43	3097	18	3114	15	6107	11	32509	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1996	27	590	43	690	47	1236	29	1648	27	1367	38	1468	32	2051	28	669	35	120	45	852	43	1152	37	2116	25	298	46	16251	9
	fir	651	24	263	58	299	65	516	40	221	52	426	58	263	57	914	37	18	68	316	63	43	63	0	.	86	71	171	65	4188	15
	pine	486	50	155	45	126	70	6	100	130	49	0	.	16	100	14	100	0	.	37	100	0	.	614	35	562	47	0	.	2146	21
	larch	10	100	30	81	0	.	15	73	133	62	0	.	0	.	0	.	0	.	0	.	38	77	454	38	578	32	805	37	2062	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	61	288	43	0	.	358	36
	other conifers	0	.	72	82	103	100	133	83	17	75	0	.	0	.	7	81	0	.	0	.	2	100	1	100	1	100	0	.	337	48
	beech	2771	19	794	31	84	54	808	30	773	28	255	61	244	61	706	31	196	57	356	48	180	44	102	58	37	62	1640	26	8946	10
	maple	247	31	88	53	30	71	131	40	367	33	262	45	245	62	354	40	159	53	68	61	190	49	12	100	19	100	68	57	2240	14
	ash	350	39	128	50	288	61	174	37	682	30	176	65	278	73	400	41	18	100	109	78	69	72	10	85	40	90	251	56	2972	15
	oak	316	46	169	61	432	43	186	44	61	52	34	74	6	100	10	100	17	73	10	100	0	.	30	50	0	.	219	33	1491	19
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	26	100	0	.	797	31	829	30
	other broadleaves	270	46	86	51	28	59	227	48	390	41	20	48	11	72	56	95	25	69	47	64	14	73	220	52	116	58	1130	26	2638	15
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0
	total	7097	15	2375	22	2080	29	3433	21	4428	18	2540	28	2531	25	4511	22	1103	31	1063	41	1388	36	2690	22	3843	19	5377	14	44459	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4066	16	872	32	662	51	1456	33	3270	20	2489	26	2175	26	4239	19	1913	30	1976	36	1413	38	2432	24	7647	15	2109	26	36717	6
	fir	3673	18	192	47	504	55	797	37	1308	25	977	39	1030	30	1711	24	320	47	508	49	308	66	446	55	167	69	287	51	12229	9
	pine	389	39	244	50	163	71	174	48	761	27	0	.	170	63	31	100	0	.	197	61	0	.	239	46	543	38	17	100	2928	14
	larch	0	.	15	73	25	100	81	82	280	48	0	.	0	.	77	74	14	100	0	.	102	91	930	37	1447	23	904	34	3876	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	409	36	0	.	409	36
	other conifers	10	89	0	.	0	.	13	100	52	84	0	.	0	.	9	71	0	.	0	.	0	.	0	.	0	.	0	.	84	56
	beech	4408	14	2550	21	1626	29	1411	26	2496	18	392	39	1618	33	2095	22	728	39	205	65	465	41	128	83	263	53	761	35	19146	7
	maple	554	19	276	39	22	72	69	51	425	24	62	62	73	50	507	27	174	40	76	60	101	49	13	73	16	72	2	100	2369	11
	ash	524	37	243	42	506	42	639	71	936	22	68	71	229	70	707	35	228	56	18	58	162	55	0	0	2	100	120	78	4379	15
	oak	187	38	177	57	150	56	308	40	560	29	0	.	28	100	0	0	0	.	19	100	0	.	5	100	59	100	92	75	1586	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	547	55	554	54
	other broadleaves	161	34	240	45	152	66	137	56	209	33	33	69	74	45	64	58	127	73	44	74	123	64	55	56	8	77	203	52	1630	16
	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	13972	10	4808	18	3810	23	5084	20	10296	13	4021	24	5396	20	9441	15	3512	23	3043	29	2673	28	4249	21	10561	13	5042	17	85908	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4704	16	1070	37	2138	27	4460	20	4787	18	3915	24	8463	15	8780	16	3671	24	2351	29	2519	31	4552	20	9154	14	4068	22	64632	5
	fir	4818	17	676	36	964	38	4625	18	2917	20	680	41	5268	16	2231	27	1229	33	708	51	391	61	115	77	701	39	350	65	25672	7
	pine	169	48	28	74	182	74	414	36	363	39	0	.	294	100	25	76	0	.	0	.	0	.	179	86	360	40	0	.	2015	22
	larch	63	75	15	100	49	71	37	100	33	100	0	.	0	.	97	71	68	67	0	.	132	71	3079	21	2752	23	1327	29	7652	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	230	53	119	61	0	.	349	41
	other conifers	3	100	4	100	185	100	237	78	293	100	0	.	71	100	-11	128	0	.	0	.	0	.	0	.	0	.	0	.	782	51
	beech	4056	15	2683	24	1694	29	3759	17	4523	17	558	43	3066	23	2462	25	1024	35	522	46	811	38	27	100	625	47	689	43	26499	7
	maple	511	24	240	45	48	66	85	43	433	33	64	55	280	30	357	33	178	42	113	49	138	59	68	62	36	117	0	.	2550	12
	ash	216	45	230	43	568	44	635	38	1325	25	152	80	208	45	313	52	94	68	218	62	35	112	29	86	38	100	0	.	4060	14
	oak	290	50	93	86	450	56	196	40	966	40	0	.	0	.	42	74	0	.	16	100	0	.	0	0	0	.	14	100	2066	24
	sweet chestnut	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	384	56	385	56
	other broadleaves	67	44	114	57	444	69	137	47	711	63	19	62	97	53	129	44	636	53	39	95	62	70	12	63	31	73	164	64	2661	25
	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	14896	11	5153	20	6722	18	14586	12	16351	11	5387	22	17748	12	14423	13	6901	18	3966	26	4088	26	8291	15	13817	12	6995	16	139323	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5458	12	5	72	677	39	814	36	867	29	1925	25	6903	14	1554	27	5247	19	1323	28	1510	30	5088	15	7098	14	4318	18	42787	5
	fir	4626	16	96	73	165	63	1188	32	399	39	692	47	3181	19	1042	30	759	40	421	41	119	57	624	39	332	37	860	39	14505	9
	pine	309	33	114	65	169	86	0	.	135	59	0	.	147	100	0	.	63	47	42	90	105	72	531	28	667	41	159	53	2443	17
	larch	0	.	0	.	11	100	49	100	61	71	0	.	0	.	30	100	26	83	0	.	3	100	3564	20	1208	19	2161	15	7112	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	554	26	612	27	5	100	1164	19
	other conifers	4	523	181	98	0	.	109	92	21	100	0	.	5	85	0	.	0	.	6	100	-16	100	1	100	11	70	0	.	323	64
	beech	3935	15	418	42	330	43	602	36	946	27	339	58	2443	20	820	34	466	39	300	46	312	48	515	43	339	44	1681	20	13449	8
	maple	775	22	75	68	60	52	25	150	216	62	124	44	620	25	182	59	350	38	32	80	207	51	126	49	281	59	65	65	3137	12
	ash	374	32	309	54	428	40	260	45	351	37	9	86	168	44	361	44	283	52	36	71	116	67	135	56	117	60	158	41	3105	13
	oak	167	60	180	79	61	77	181	62	251	36	9	100	52	73	8	78	9	100	65	71	1	100	133	54	72	100	327	37	1516	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	135	81	18	100	2028	23	2180	22
	other broadleaves	363	39	56	77	277	49	190	89	243	53	33	54	137	31	280	55	220	42	43	56	69	52	230	41	175	37	1021	19	3337	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	16013	9	1433	32	2179	26	3417	25	3492	20	3131	23	13657	11	4277	21	7418	17	2268	26	2425	24	11636	11	10927	11	12786	10	95058	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17435	6	3096	19	4805	17	9409	12	13319	9	11013	12	20455	8	17918	10	12925	12	6318	17	6593	17	14247	9	28423	7	11670	10	177626	2
	fir	14521	8	1246	25	2139	24	7756	13	5447	13	3060	21	10441	10	6310	13	2453	21	1963	26	1002	33	1311	28	1341	24	1771	25	60763	4
	pine	1468	21	584	28	657	37	699	26	1550	18	0	.	632	55	113	51	63	47	285	46	105	72	2020	19	2606	18	192	45	10974	8
	larch	85	59	68	46	219	39	185	49	629	28	10	100	30	71	204	46	320	36	0	.	275	49	8917	11	6604	12	5930	11	23475	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	958	22	1532	18	5	100	2489	13
	other conifers	16	170	292	65	290	73	549	44	436	68	0	.	77	93	5	306	45	83	6	100	-13	118	15	58	45	35	50	100	1813	27
	beech	15794	6	7020	12	4010	17	7298	11	9894	9	1614	23	7692	12	6382	13	2426	20	1412	25	1914	21	958	29	1304	27	5569	12	73286	3
	maple	2301	10	815	21	196	29	408	22	2047	15	620	24	1349	17	1541	16	974	20	376	27	716	24	322	27	406	43	347	25	12416	5
	ash	1651	17	1063	22	1859	22	1841	29	3602	12	489	36	924	31	2014	19	662	31	465	36	407	33	437	31	244	37	748	25	16407	6
	oak	1187	23	707	33	1165	28	911	22	2003	22	43	63	86	55	60	55	51	57	111	48	1	100	205	37	130	71	957	20	7615	9
	sweet chestnut	0	.	0	.	2	100	0	.	6	100	0	.	0	.	0	.	7	100	0	.	0	.	194	59	18	100	5342	12	5569	12
	other broadleaves	1027	20	602	24	1108	33	925	26	1781	28	208	28	413	20	680	27	1173	32	333	31	345	30	987	18	986	21	4460	11	15029	7
	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	0
	total	55484	3	15493	9	16449	10	29982	7	40712	5	17056	10	42099	6	35228	7	21093	9	11269	13	11345	13	30572	7	43639	5	37041	4	407462	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1354525. Birmensdorf, Swiss Federal Research Institute WSL

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