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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
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	31	100	0	0	90	81	91	71	0	0	433	43	166	53	390	87	150	73	28	100	112	73	7	100	94	64	76	91	1668	26
	fir	42	100	0	.	0	.	86	58	0	.	101	100	48	73	74	76	12	100	0	.	0	.	0	.	0	.	0	0	363	38
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	0	0	0	7	100	30	71	43	53
	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	24	100	7	100	64	68	125	95	72	61	30	100	83	82	59	100	185	100	0	.	0	.	0	.	7	100	71	100	726	36
	maple	0	.	0	.	0	.	0	.	0	.	0	.	26	72	12	100	6	100	0	.	18	100	0	.	0	.	0	.	61	47
	ash	0	.	0	.	0	.	34	100	7	100	17	100	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	57
	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	19	100	0	.	0	.	0	.	34	100	0	.	0	.	51	71	0	.	0	.	17	100	0	.	0	.	0	.	122	46
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0
	total	117	85	7	100	154	64	336	59	113	65	582	42	338	50	586	68	360	66	28	100	146	72	7	100	108	57	177	71	3058	20
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	90	45	144	54	182	76	48	65	362	46	115	67	266	48	121	59	188	79	91	71	106	100	162	44	553	36	73	100	2500	16
	fir	132	55	34	100	20	100	66	65	86	52	22	100	221	56	21	72	24	100	0	.	63	66	0	0	35	100	0	.	723	24
	pine	23	100	0	.	12	100	23	100	45	59	0	.	0	.	0	.	0	.	0	.	0	.	381	100	58	100	0	.	541	72
	larch	0	.	0	.	12	100	0	.	8	100	0	.	0	.	0	.	0	.	7	100	0	.	240	44	51	56	33	72	350	32
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	78	17	100	0	.	84	66
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	100	0	.	0	.	65	100
	beech	186	47	293	32	178	73	378	35	322	31	0	.	33	71	53	58	46	72	0	.	53	75	22	100	0	.	196	87	1760	17
	maple	91	52	51	71	72	80	53	65	43	55	22	100	0	.	39	61	0	.	0	.	17	100	7	100	0	.	18	100	413	25
	ash	54	86	49	86	24	100	66	63	58	88	42	100	0	.	17	100	37	100	0	.	17	100	0	.	0	.	0	.	365	31
	oak	17	100	25	76	8	100	7	100	29	61	0	.	0	.	0	.	0	.	0	.	0	.	106	100	0	.	18	100	210	54
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	291	60	291	60
	other broadleaves	97	60	105	57	90	60	34	100	127	62	0	.	6	100	109	69	87	60	0	.	0	.	35	71	83	72	99	84	872	22
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	690	32	702	30	597	49	676	32	1078	26	200	64	526	37	361	45	381	48	98	67	256	55	1084	43	796	30	729	40	8173	11
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3151	34	1140	57	1366	43	3922	23	6727	21	1790	39	1908	38	3307	30	1149	41	367	57	448	51	2738	40	4994	24	617	53	33623	9
	fir	870	40	104	100	236	50	860	37	932	50	374	72	233	76	657	48	110	100	52	100	0	.	112	80	152	59	233	67	4926	17
	pine	20	74	247	70	34	71	419	68	198	38	0	.	121	100	0	.	0	.	0	.	0	.	1696	40	1363	39	0	.	4100	23
	larch	17	100	35	70	169	76	71	60	127	69	0	.	69	100	124	100	307	50	0	.	0	.	1377	32	545	44	1367	37	4208	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	141	100	113	86	0	.	253	67
	other conifers	68	70	101	61	69	100	101	58	171	51	0	.	0	.	0	.	17	100	0	.	0	.	35	100	328	56	16	100	906	26
	beech	1818	37	159	45	419	51	1025	26	1407	28	106	71	274	63	380	42	428	91	7	100	240	100	492	69	37	100	3466	30	10259	15
	maple	597	34	75	71	155	60	442	40	1776	27	167	66	119	52	312	55	562	46	94	92	227	55	216	40	159	66	459	38	5360	13
	ash	352	53	305	64	189	47	226	43	1180	30	167	75	116	61	626	50	129	65	250	60	123	77	317	56	104	58	629	39	4713	14
	oak	485	69	208	100	468	70	41	61	196	55	58	100	22	100	0	.	106	100	0	.	0	.	495	50	0	.	1134	47	3213	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	71	0	.	5226	25	5252	25
	other broadleaves	388	48	110	62	262	49	847	47	475	31	254	71	293	46	496	58	392	58	359	63	646	54	2623	26	2716	34	6418	20	16281	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	0
	total	7766	20	2485	34	3369	29	7955	18	13189	14	2915	32	3155	30	5902	24	3200	29	1129	49	1685	42	10267	18	10511	17	19566	12	93094	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3745	22	1352	48	470	50	2082	29	2015	28	1256	48	3225	33	1425	34	1306	41	113	65	1738	49	2463	28	2451	33	1315	37	24956	10
	fir	2127	24	340	82	259	84	676	42	264	33	478	51	254	47	466	43	49	75	342	59	96	73	359	62	151	80	90	82	5952	14
	pine	545	46	101	45	138	100	58	52	182	48	0	.	36	100	0	.	0	.	0	.	0	.	1351	36	981	48	0	.	3393	22
	larch	0	.	14	100	0	.	24	77	154	60	0	.	0	.	0	.	0	.	0	.	131	78	1567	26	410	55	506	41	2806	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	254	67	58	100	0	.	311	58
	other conifers	0	.	34	100	0	.	122	50	35	100	0	.	0	.	21	100	0	.	0	.	18	100	17	100	0	.	0	.	247	34
	beech	5499	17	2081	29	526	55	1420	27	1192	27	600	58	921	55	1700	34	271	60	432	55	1010	47	496	63	185	67	2731	30	19066	9
	maple	681	30	106	53	34	100	54	65	500	32	225	49	269	41	602	37	389	60	161	58	304	71	64	77	96	78	94	54	3580	14
	ash	693	37	387	57	444	50	198	38	431	31	310	58	583	69	548	38	64	100	175	75	608	82	120	60	175	77	879	45	5615	17
	oak	748	57	94	62	560	52	73	59	72	56	39	71	21	100	21	100	0	.	0	.	0	.	92	54	0	.	632	36	2353	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	100	0	.	2949	27	3004	27
	other broadleaves	703	38	326	48	258	76	363	41	581	37	87	82	123	59	531	56	52	74	86	84	541	65	584	44	419	43	3824	22	8476	13
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0
	total	14741	13	4834	24	2690	31	5069	20	5426	18	2996	32	5432	26	5314	24	2132	36	1310	48	4447	37	7423	18	4926	24	13020	14	79759	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5969	14	576	31	1137	36	1319	29	2274	18	2239	24	2683	24	2936	21	2773	23	1696	38	569	34	3982	21	8135	15	2377	24	38666	6
	fir	4470	15	567	34	658	53	1141	29	853	29	481	43	1379	26	802	30	521	34	271	55	206	58	348	52	99	55	332	43	12128	9
	pine	553	31	192	40	144	56	67	81	379	31	0	.	137	71	31	71	49	100	208	73	0	.	288	44	1089	36	50	100	3185	16
	larch	0	.	31	64	21	58	32	100	98	52	0	.	7	100	14	100	80	93	0	.	0	.	663	31	880	29	1470	32	3297	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	71	308	48	0	.	368	42
	other conifers	195	53	17	100	0	.	20	100	50	74	0	.	0	.	18	100	0	.	0	.	0	.	17	100	0	0	0	.	317	37
	beech	7284	12	2448	20	1564	25	1973	24	2288	17	493	40	1330	29	2352	28	816	32	237	97	913	33	252	76	291	44	1092	37	23333	7
	maple	1425	15	495	29	24	77	221	48	623	29	328	48	364	28	685	36	434	32	55	100	398	47	280	59	15	100	48	100	5395	10
	ash	811	32	383	43	303	38	418	56	648	21	54	100	264	46	658	37	336	47	8	100	176	50	15	100	0	.	113	51	4189	13
	oak	468	52	132	55	250	65	180	42	186	35	21	100	0	.	43	100	0	.	0	.	0	.	8	100	0	.	144	45	1433	23
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	759	36	766	35
	other broadleaves	854	20	227	37	289	57	185	51	491	37	381	50	246	43	266	45	504	44	99	73	69	63	468	46	58	58	757	33	4893	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	22028	9	5070	18	4390	21	5558	18	7890	13	3996	22	6410	18	7806	17	5521	18	2574	32	2331	25	6382	17	10875	14	7142	15	97971	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2322	17	268	41	1425	25	2270	19	2199	15	1619	27	4505	15	4852	16	1862	25	1987	28	1176	33	2727	22	7136	14	2658	20	37005	5
	fir	2758	16	268	61	435	35	2612	18	1184	20	442	45	4276	15	1613	20	805	31	742	44	241	45	251	67	241	43	458	52	16325	7
	pine	87	40	21	74	90	63	185	39	330	29	0	.	114	100	22	59	0	.	118	100	0	.	177	87	211	40	42	100	1398	20
	larch	14	70	0	.	16	100	7	100	48	61	0	.	0	.	31	60	92	52	0	.	32	65	1643	24	1193	22	506	26	3582	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	227	44	310	41	0	.	537	30
	other conifers	35	70	0	.	23	100	30	71	73	80	0	.	7	100	36	71	0	.	0	.	0	.	0	.	0	.	0	.	204	37
	beech	2708	14	958	26	932	28	2582	16	3628	16	374	47	4377	17	2443	18	768	34	924	32	409	36	37	100	548	35	731	39	21419	6
	maple	856	27	202	42	196	54	203	44	546	29	74	52	436	31	467	28	87	54	139	41	190	45	238	60	138	49	16	100	3788	11
	ash	330	36	122	39	515	39	460	30	627	27	108	87	206	62	172	42	68	91	166	54	34	68	49	73	68	84	0	.	2924	13
	oak	184	42	91	45	376	44	211	47	487	26	0	.	0	.	17	71	0	.	22	77	0	.	15	100	28	100	26	76	1456	17
	sweet chestnut	0	.	0	.	66	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	596	41	662	38
	other broadleaves	834	33	70	53	351	58	240	38	794	36	20	100	197	49	262	41	333	43	101	49	117	64	173	48	241	56	360	41	4091	14
	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	10127	11	1999	24	4425	19	8800	13	9915	12	2637	24	14119	11	9916	12	4016	19	4198	22	2197	27	5537	16	10113	11	5394	15	93392	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2666	17	7	100	154	46	239	54	858	35	1525	36	6411	17	1067	37	3409	20	1012	44	1377	37	4863	18	7015	15	2807	23	33411	7
	fir	3516	18	14	71	112	58	511	53	228	47	932	47	2229	20	319	46	295	67	340	65	151	73	318	51	184	49	322	63	9470	11
	pine	266	54	239	60	63	89	7	100	24	77	0	.	446	98	0	.	125	57	62	77	0	.	1336	30	848	34	190	74	3603	20
	larch	7	100	0	.	0	0	0	.	17	100	0	.	0	.	0	.	23	100	0	.	0	.	2064	27	1060	21	1684	25	4856	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	740	38	1574	28	32	100	2356	22
	other conifers	51	74	125	87	0	.	91	73	252	72	0	.	17	100	18	100	0	.	69	100	46	100	0	.	190	65	0	.	860	31
	beech	2291	20	487	60	287	56	387	54	744	30	711	73	2298	26	323	41	482	43	184	72	418	59	850	47	692	47	3178	23	13332	11
	maple	587	26	129	52	127	63	45	100	285	44	329	39	900	53	218	46	533	43	0	.	154	58	137	65	126	48	115	67	3685	17
	ash	120	53	69	100	96	57	52	53	493	51	37	72	242	55	406	48	77	80	37	71	65	100	381	84	487	72	139	50	2701	22
	oak	288	59	227	70	7	100	99	72	76	61	0	.	82	70	0	.	61	71	44	71	0	.	104	74	55	100	372	49	1417	23
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	28	100	2226	27	2262	26
	other broadleaves	367	35	408	45	59	89	98	75	229	48	154	49	608	30	150	49	669	42	129	71	86	67	672	36	331	33	1943	21	5903	11
	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	10159	13	1706	44	905	37	1529	33	3205	26	3689	30	13233	13	2500	27	5684	18	1877	37	2296	31	11473	14	12591	12	13010	12	83857	5
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17974	8	3487	27	4825	17	9971	12	14434	11	8978	14	19164	9	14098	11	10837	11	5293	19	5526	20	16943	11	30377	7	9924	11	171831	3
	fir	13915	8	1326	29	1720	26	5951	13	3546	16	2830	23	8640	10	3952	14	1817	21	1746	27	756	28	1389	27	862	25	1436	26	49886	4
	pine	1493	22	801	30	481	37	758	40	1157	16	0	.	854	56	53	48	174	50	387	51	0	.	5229	19	4551	19	282	55	16220	9
	larch	38	55	80	43	217	60	134	42	452	31	0	.	76	91	170	74	509	35	7	100	162	64	7555	12	4146	13	5597	15	19143	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1488	25	2380	20	32	100	3910	16
	other conifers	349	35	277	47	92	79	364	30	580	37	0	.	24	77	93	44	17	100	69	100	65	77	134	58	518	43	16	100	2600	15
	beech	19809	7	6434	13	3971	15	7890	10	9652	9	2314	29	9315	12	7311	13	2997	20	1784	26	3043	22	2149	30	1760	24	11465	13	89894	3
	maple	4236	10	1058	19	608	29	1019	23	3772	15	1145	22	2114	25	2335	17	2012	22	449	33	1307	26	942	27	535	29	750	27	22282	5
	ash	2361	18	1316	26	1571	21	1454	21	3444	14	734	34	1427	32	2427	20	711	30	636	35	1024	50	882	43	834	46	1761	27	20580	7
	oak	2189	28	777	36	1669	30	612	25	1046	18	118	57	125	52	80	61	167	68	66	54	0	.	821	34	84	74	2326	26	10082	11
	sweet chestnut	0	.	0	.	66	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	89	66	28	100	12047	13	12238	13
	other broadleaves	3262	14	1246	22	1308	27	1768	25	2731	16	896	32	1473	19	1865	24	2037	22	775	35	1476	34	4554	18	3848	25	13402	11	40639	6
	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
	total	65627	4	16803	11	16529	11	29922	7	40814	6	17015	12	43213	6	32384	8	21294	9	11212	14	13358	15	42174	7	49921	6	59038	4	459304	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. 1356632. Birmensdorf, Swiss Federal Research Institute WSL

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