Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI2
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 1993/95
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	1000 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	0	.	0	.	0	.	24	100	0	.	26	100	127	70	52	69	60	73	25	72	10	100	87	50	179	51	9	100	597	25
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	0	7	100	4	100	10	100	0	.	0	0	0	.	0	0	21	62
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	10	100	0	.	0	.	14	77
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	8	71	4	100	21	73	33	51
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	37	71	0	.	0	.	11	100	40	100	0	.	0	.	38	100	0	.	0	.	45	72	170	41
	maple	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	21	77
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	100	54	100
	other broadleaves	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	13	100	0	.	20	100	0	.	0	.	33	72
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	15	100	0	.	0	.	61	58	0	.	26	100	138	65	98	60	64	72	59	55	48	100	125	44	183	50	129	57	944	20
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	204	35	35	59	148	68	204	58	193	36	106	48	487	30	213	39	155	37	155	45	94	49	138	40	870	21	166	47	3168	11
	fir	229	46	20	100	0	.	49	64	64	47	4	100	140	46	80	60	35	73	19	100	14	78	60	86	43	60	39	79	798	20
	pine	34	100	0	.	25	73	17	100	84	50	0	.	0	.	0	.	0	.	0	.	0	.	10	100	114	61	0	.	284	32
	larch	4	100	0	.	11	75	0	.	67	59	0	.	0	.	56	100	20	71	4	100	0	.	216	39	266	30	115	42	760	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	74	0	.	0	.	48	74
	other conifers	0	.	0	.	0	.	18	100	58	85	0	.	0	.	0	.	0	.	51	82	0	.	0	.	41	100	0	.	168	47
	beech	71	40	40	54	27	77	93	65	106	37	135	63	23	56	109	40	10	100	19	81	205	55	35	76	24	62	119	40	1015	18
	maple	50	53	0	.	10	100	58	74	79	38	32	56	13	74	56	38	44	71	21	100	70	52	83	56	26	72	73	57	615	17
	ash	11	100	0	.	0	.	33	67	129	41	9	100	67	64	31	74	0	.	10	100	30	74	27	73	19	100	14	76	379	23
	oak	4	100	0	.	29	100	13	78	15	61	0	.	0	.	0	.	0	.	0	.	0	.	48	70	0	.	0	.	109	43
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	82	19	82
	other broadleaves	130	60	0	.	0	.	48	45	123	53	83	77	74	55	81	48	20	71	41	79	61	57	230	38	139	44	318	31	1348	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	735	24	96	46	251	55	532	36	918	20	369	39	805	25	627	29	285	31	321	39	474	34	895	24	1542	17	863	22	8712	7
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5075	18	1174	31	2235	26	5696	18	8839	14	2798	26	5100	19	3800	21	2084	24	1200	36	1271	30	2212	27	3885	21	2508	22	47876	6
	fir	1529	23	209	61	611	46	1191	28	799	26	357	48	521	32	803	28	61	70	389	40	158	70	447	54	108	66	275	44	7458	10
	pine	190	51	329	46	215	57	146	57	368	40	0	.	405	67	29	76	119	71	117	100	211	71	3683	20	2198	24	19	71	8029	12
	larch	89	66	67	55	114	54	137	67	282	53	0	.	0	.	0	.	42	82	0	.	121	65	1615	21	869	27	1705	20	5039	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	98	100	24	100	0	0	0	.	0	.	429	42	389	52	0	.	940	31
	other conifers	222	68	103	75	226	69	94	58	232	53	9	100	12	100	23	71	10	100	10	100	28	100	154	87	317	44	4	100	1444	23
	beech	3636	17	1273	24	494	30	1513	23	1729	23	247	48	1354	31	1115	29	443	54	384	41	680	51	875	35	235	59	6671	14	20648	7
	maple	555	25	259	32	396	54	260	34	1077	27	134	52	343	43	652	32	635	30	225	42	360	47	259	28	249	45	610	25	6013	10
	ash	795	30	438	37	426	37	437	32	1230	21	198	57	325	48	919	31	139	54	219	44	358	60	384	32	275	40	1761	19	7902	9
	oak	1131	33	307	63	1048	39	148	42	419	42	13	100	28	61	12	100	193	57	10	100	0	.	398	31	18	81	1673	22	5397	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	125	52	4	100	7343	14	7472	13
	other broadleaves	886	25	365	37	704	29	766	33	1171	22	253	47	724	30	1038	33	460	37	564	46	477	33	2719	18	2552	23	10716	9	23394	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	14107	10	4524	17	6467	16	10387	14	16147	10	4009	22	8909	15	8414	15	4185	19	3119	23	3663	24	13299	11	11099	12	33284	6	141613	3
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4266	13	723	30	1590	27	1556	21	2409	17	3919	19	4383	18	3672	20	3447	19	1506	31	1931	35	3398	19	7373	12	913	32	41085	5
	fir	3643	13	257	42	267	38	1247	26	508	35	1235	25	1655	21	837	22	248	45	365	42	60	58	225	39	191	39	212	62	10949	8
	pine	745	28	314	35	61	44	54	71	334	29	0	.	114	62	9	100	61	66	318	42	0	.	868	34	1322	25	149	84	4348	13
	larch	0	.	75	60	113	81	71	69	39	50	0	.	7	100	8	100	38	65	0	.	42	65	1520	21	1258	19	758	30	3929	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	336	57	722	33	0	.	1062	29
	other conifers	41	71	107	84	13	100	18	100	29	58	0	.	40	81	10	100	0	.	0	.	10	100	0	.	20	100	0	.	289	37
	beech	8413	9	2573	17	1896	21	1764	23	2060	16	835	45	2084	21	1949	24	1072	29	608	36	456	38	724	38	383	50	1684	27	26502	6
	maple	1634	13	488	23	220	50	178	52	621	22	435	35	310	24	396	30	412	35	53	55	218	55	182	58	19	71	0	.	5167	8
	ash	714	28	436	35	498	27	208	28	781	20	101	48	321	39	397	33	182	47	63	50	92	62	62	63	29	100	31	75	3916	10
	oak	601	40	230	31	304	39	237	42	294	43	50	82	91	77	46	80	215	61	10	100	0	.	91	46	0	.	178	54	2348	16
	sweet chestnut	0	.	0	.	54	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	1285	30	1358	29
	other broadleaves	796	19	492	36	460	35	124	34	705	28	191	34	553	41	339	28	567	26	106	49	101	51	544	33	240	40	513	43	5731	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	20853	7	5695	14	5475	15	5458	15	7780	11	6766	17	9560	13	7663	15	6246	16	3029	22	2911	28	7968	13	11557	10	5724	14	106685	3
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5326	10	1036	24	2706	16	2367	16	4113	12	3317	19	4935	12	4195	14	2457	18	1366	28	806	26	4545	14	8512	10	1731	19	47412	4
	fir	4493	10	576	26	588	27	2000	16	1730	15	618	36	2326	16	1535	20	437	32	433	40	164	46	302	45	147	39	382	32	15731	6
	pine	562	29	194	31	240	49	139	43	632	19	0	.	95	75	66	45	26	100	114	86	0	.	460	31	699	30	135	84	3361	12
	larch	4	100	14	77	53	74	15	100	109	46	17	100	8	100	17	61	36	58	0	.	114	60	1901	19	974	18	555	27	3818	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	309	38	632	28	0	.	940	23
	other conifers	29	74	33	52	4	100	96	55	8	71	0	.	4	100	31	58	0	.	0	.	0	.	0	.	0	.	0	.	206	30
	beech	4637	9	2877	16	2134	17	2214	16	3487	12	652	31	2411	19	2062	19	1523	35	543	40	550	38	56	56	362	33	655	29	24164	5
	maple	780	17	485	26	159	45	173	40	569	22	135	37	446	23	569	26	330	29	123	41	137	51	89	60	44	51	0	.	4039	8
	ash	610	23	416	32	505	39	346	54	616	19	118	56	333	32	366	35	102	56	73	86	79	48	124	66	55	62	22	84	3764	11
	oak	348	26	239	30	532	29	127	29	370	22	16	59	32	76	21	51	0	.	37	69	0	.	10	100	105	83	34	75	1871	12
	sweet chestnut	0	.	0	.	30	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	261	42	291	39
	other broadleaves	1157	19	342	26	219	57	221	41	541	24	102	45	425	44	301	38	348	36	106	50	168	46	336	44	208	38	231	39	4706	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	17945	7	6211	13	7170	12	7699	11	12176	8	4974	17	11016	10	9163	12	5261	16	2795	23	2020	22	8132	12	11737	9	4006	13	110303	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1667	14	201	41	700	24	1896	15	2114	14	1579	32	3270	13	2214	15	1542	21	623	31	989	27	1674	18	4073	12	1388	18	23930	5
	fir	1869	16	63	54	432	28	1221	18	847	20	283	35	2263	15	861	21	251	45	103	56	103	49	395	44	205	30	106	53	9001	7
	pine	124	49	4	100	40	48	93	35	282	24	0	.	32	100	13	58	0	.	0	.	0	.	94	55	249	50	38	67	969	18
	larch	4	100	20	100	19	73	8	71	56	44	0	.	25	85	16	58	44	53	0	.	23	71	866	20	665	23	314	23	2061	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	85	219	34	0	.	241	32
	other conifers	0	.	0	.	14	100	58	62	64	71	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	145	41
	beech	1756	15	990	20	653	23	2009	13	2060	13	319	42	1671	20	1237	22	175	58	119	65	286	37	163	80	412	36	549	41	12400	6
	maple	426	30	109	35	99	57	224	34	262	28	159	41	234	29	168	34	210	57	17	81	180	38	19	100	44	62	51	93	2201	12
	ash	51	57	40	47	243	43	165	33	231	29	67	71	74	68	63	45	40	100	19	100	7	100	28	74	4	100	10	100	1043	16
	oak	101	47	126	36	154	43	153	29	344	26	12	100	16	73	36	65	0	.	5	100	0	.	9	100	0	.	36	52	992	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	545	25	545	25
	other broadleaves	228	42	109	55	51	44	162	45	548	28	41	50	82	38	113	56	110	56	57	60	7	100	188	57	73	61	445	24	2213	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	6226	11	1662	20	2405	19	5989	11	6807	10	2460	25	7678	11	4720	13	2372	20	942	29	1596	23	3458	15	5944	11	3482	14	55741	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2761	14	8	71	98	42	323	33	267	33	2198	19	3404	13	2296	20	2927	18	795	35	1231	26	5197	13	6063	12	2971	17	30538	5
	fir	1950	17	67	76	42	73	460	36	216	58	878	29	2776	16	662	25	348	33	141	65	396	35	239	38	199	42	314	41	8689	8
	pine	177	36	18	100	5	100	0	.	61	56	21	100	189	53	11	100	4	100	83	71	110	77	507	34	637	29	117	78	1940	16
	larch	0	.	0	.	0	.	0	.	37	71	0	.	0	.	26	100	18	71	0	.	18	100	2585	17	838	21	1772	16	5295	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	38	100	0	.	572	22	512	32	13	77	1140	18
	other conifers	99	57	0	.	0	.	26	100	110	50	0	.	10	100	20	100	0	.	42	78	0	.	10	100	184	71	0	.	502	32
	beech	1657	18	65	70	151	54	418	38	753	27	265	33	1581	18	1103	22	468	36	118	51	596	31	116	55	319	44	1562	20	9172	8
	maple	442	21	26	100	52	63	53	67	226	45	211	32	269	26	578	29	251	37	40	89	145	48	121	46	112	55	78	50	2602	11
	ash	515	31	14	78	65	61	111	47	257	32	222	49	133	39	532	32	87	63	47	100	64	65	196	42	255	81	181	38	2679	14
	oak	34	59	4	100	0	.	70	77	75	44	47	79	0	.	0	.	0	.	14	100	0	0	156	62	34	100	272	52	705	27
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	101	100	17	100	1845	21	1962	20
	other broadleaves	575	28	94	63	67	63	81	51	469	33	299	32	186	37	368	37	626	33	98	65	321	55	509	35	298	29	1816	17	5808	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8210	11	297	52	479	39	1542	24	2472	22	4141	17	8548	11	5595	16	4732	15	1416	31	2882	22	10309	10	9467	10	10942	9	71032	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19298	6	3177	15	7477	11	12065	10	17937	8	13942	10	21706	7	16442	8	12671	9	5670	14	6331	14	17251	7	30953	5	9686	8	194606	2
	fir	13714	6	1192	19	1939	18	6168	10	4164	10	3374	15	9681	7	4785	10	1384	18	1461	20	895	22	1667	21	893	18	1329	19	52647	3
	pine	1832	16	859	23	585	30	449	26	1762	13	21	100	834	37	127	31	210	46	636	33	321	54	5633	14	5219	13	457	42	18945	7
	larch	101	58	175	35	311	38	232	45	590	28	17	100	41	59	124	52	199	28	4	100	318	35	8710	9	4875	9	5239	10	20936	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	98	100	24	100	8	71	38	100	0	.	1717	18	2473	16	13	77	4371	12
	other conifers	390	42	243	49	257	62	310	29	501	30	9	100	77	49	84	39	10	100	104	53	38	78	165	82	562	35	4	100	2754	14
	beech	20171	5	7819	9	5355	11	8048	8	10196	7	2453	19	9136	9	7613	10	3691	19	1790	20	2812	18	1969	22	1735	19	11285	10	94072	2
	maple	3901	8	1367	14	934	28	947	18	2834	13	1106	18	1615	13	2418	14	1882	16	484	24	1111	22	752	21	493	27	812	21	20658	4
	ash	2696	14	1343	19	1736	17	1299	19	3243	10	715	25	1254	19	2308	16	551	26	431	30	631	36	821	21	636	37	2018	16	19683	5
	oak	2217	21	906	25	2067	22	749	19	1516	16	137	43	167	46	115	40	408	42	76	43	0	0	713	23	157	60	2194	18	11422	8
	sweet chestnut	0	.	0	.	84	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	244	50	21	83	11352	10	11702	9
	other broadleaves	3771	10	1403	18	1501	19	1401	20	3557	11	969	19	2044	18	2240	18	2131	16	986	28	1135	23	4546	13	3510	17	14039	7	43235	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	68091	3	18485	7	22246	7	31668	6	46301	4	22743	8	46653	5	36280	6	23145	7	11680	11	13593	11	44187	5	51528	4	58430	3	495030	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. 1356623. Birmensdorf, Swiss Federal Research Institute WSL

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