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 (“±”).

top

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).

top

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).

top

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.

top

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.

top

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

top

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.

top

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.

top

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.

top

NFI5
number of stems
development stage · main tree species
regional demarcation: economic region
unit: n/ha
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	5	86	71	78	14	65	34	31	20	82	56	37	20	0	0	.	0	.	74	29	57	48	41	23
	fir	0	.	0	.	0	.	0	.	5	86	27	65	0	.	0	.	27	89	0	.	0	.	5	85	0	.	0	.	4	64
	pine	59	48	0	.	5	87	0	.	5	86	0	.	0	.	0	.	0	.	0	.	0	.	5	87	3	65	0	.	4	48
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	87	17	82	8	89	6	70
	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	198	54	0	.	0	.	10	50	12	87	45	65	0	.	23	82	0	.	0	.	105	0	0	.	0	.	0	.	14	50
	maple	0	.	0	.	0	.	13	86	0	.	14	65	0	.	17	82	0	.	0	.	0	.	0	.	0	.	0	.	2	58
	ash	0	.	0	.	0	.	5	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99
	oak	0	.	0	.	7	83	0	.	5	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	70
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	89	0	.	0	.	0	.	0	.	0	.	0	99
	other broadleaves	0	.	0	.	5	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	257	52	0	.	16	28	33	70	98	61	99	65	34	31	60	47	88	21	20	0	105	0	15	28	95	30	65	38	74	18
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	38	33	4	96	13	66	56	48	12	54	114	35	76	32	50	35	88	24	29	89	26	75	27	56	99	19	106	42	54	12
	fir	69	73	14	49	2	95	43	33	13	48	10	64	19	43	16	40	4	95	0	.	2	95	0	.	0	.	0	.	18	33
	pine	2	98	0	.	0	.	8	50	1	98	0	.	0	.	1	98	0	.	0	.	0	.	0	.	9	86	0	.	2	46
	larch	0	.	4	96	22	87	0	.	0	.	0	.	0	.	0	.	11	80	0	.	7	93	10	62	8	56	14	50	4	35
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	5	72	3	97	0	.	2	99	5	98	0	.	0	.	0	.	0	.	0	.	0	.	1	49
	beech	18	40	24	33	13	50	18	35	29	29	2	94	48	34	26	34	25	80	8	89	0	.	0	.	2	97	9	93	19	16
	maple	6	57	9	54	0	.	0	.	10	64	6	94	9	53	18	60	11	80	6	89	15	93	0	.	15	82	0	.	8	26
	ash	0	.	14	70	2	96	0	.	0	.	11	94	0	.	7	71	0	.	0	.	6	93	14	94	0	.	0	.	3	38
	oak	2	98	0	.	14	56	2	66	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	1	43
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	93	0	100
	other broadleaves	9	57	5	95	2	96	9	69	11	88	12	94	14	53	2	98	34	72	0	.	12	94	0	.	10	98	40	76	11	26
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	144	43	73	23	67	29	141	23	79	18	156	26	168	16	127	24	172	25	43	55	69	50	51	43	142	16	172	22	123	8
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	92	35	121	41	175	38	230	20	182	19	239	43	276	25	279	27	188	24	365	26	103	42	82	27	352	20	38	52	169	8
	fir	103	30	21	80	28	46	130	28	29	38	51	49	145	43	95	49	5	69	33	86	7	66	8	86	2	75	6	50	47	15
	pine	6	59	0	.	5	98	10	86	7	63	0	.	10	99	2	97	0	.	0	.	0	.	70	50	145	32	0	.	22	25
	larch	0	.	7	98	62	79	2	99	19	48	36	62	5	99	0	.	30	61	0	.	0	.	56	35	11	49	38	38	22	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	86	10	86	0	.	1	68
	other conifers	3	99	2	98	2	98	5	70	3	60	0	.	1	98	0	.	2	98	0	.	0	.	10	60	30	46	0	.	5	28
	beech	140	23	167	22	148	38	112	22	101	20	25	76	76	42	30	35	78	54	5	61	147	78	30	58	4	99	103	29	89	10
	maple	101	28	110	23	68	56	32	41	47	26	24	80	40	27	69	44	158	20	49	53	71	55	37	37	0	.	24	43	54	11
	ash	35	36	65	28	34	44	20	44	27	29	71	55	15	49	44	43	32	51	64	64	0	.	28	49	3	99	40	30	31	12
	oak	41	69	4	80	25	64	8	58	9	40	0	.	2	98	0	.	12	98	0	.	0	.	44	42	0	.	34	52	18	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	152	27	24	28
	other broadleaves	18	32	63	23	72	35	50	46	43	27	58	41	45	46	64	36	54	23	34	39	31	39	150	20	164	33	241	18	96	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	539	9	560	9	619	12	599	9	467	8	504	23	616	12	583	13	558	10	551	20	358	27	520	8	722	8	674	8	577	3
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	96	29	107	46	177	27	244	21	171	17	285	33	384	29	278	27	308	28	115	41	328	29	263	28	293	18	35	47	196	8
	fir	65	25	2	96	41	60	67	35	44	35	69	85	106	40	64	46	62	46	180	43	23	61	0	99	11	61	13	69	42	14
	pine	16	57	34	51	5	72	10	73	6	43	0	.	16	67	0	.	0	.	0	.	0	.	142	34	61	48	8	86	29	23
	larch	0	99	7	58	10	96	2	75	4	61	0	.	0	.	0	.	13	96	0	.	25	96	96	22	42	34	38	47	24	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	63	31	52	2	99	6	39
	other conifers	2	99	29	68	0	.	9	42	27	61	0	.	0	.	5	96	0	.	0	.	9	64	1	99	1	99	0	.	6	36
	beech	172	21	212	27	54	39	70	22	92	25	117	62	74	46	185	49	36	60	214	36	128	50	21	68	14	46	213	22	112	11
	maple	41	26	26	45	21	66	29	27	62	24	69	45	60	67	88	44	120	35	57	44	76	47	0	.	19	61	11	56	37	13
	ash	19	45	4	96	15	50	21	32	19	44	35	41	72	59	49	42	19	67	93	62	27	67	9	48	4	73	37	50	26	19
	oak	27	76	33	74	76	65	5	64	1	56	10	95	1	98	0	.	11	67	0	.	0	.	10	54	0	.	29	34	15	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	82	1	99	118	26	21	27
	other broadleaves	29	42	13	44	37	49	46	35	44	26	17	41	22	55	15	96	44	51	54	50	83	63	51	31	60	60	146	19	59	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	467	8	466	10	436	13	503	10	469	8	602	15	736	15	684	15	613	12	712	17	699	16	620	12	537	10	652	6	571	3
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	104	16	47	27	28	48	88	23	71	20	263	35	190	25	219	16	260	22	356	35	93	27	246	20	287	11	107	29	152	6
	fir	86	16	39	51	52	53	74	25	28	37	133	42	43	28	38	23	33	55	112	38	23	59	38	48	4	90	14	60	47	10
	pine	12	53	15	35	15	61	7	68	11	33	0	.	6	97	0	.	0	.	28	82	0	.	40	42	34	38	1	99	13	19
	larch	0	.	1	69	1	98	1	99	1	56	0	.	0	.	0	.	0	.	0	.	0	.	66	34	26	32	72	30	14	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	47	0	.	1	49
	other conifers	5	81	0	.	4	97	2	99	6	52	0	.	0	.	4	74	0	.	0	.	0	.	0	.	0	.	0	.	2	43
	beech	162	10	180	15	133	26	94	19	93	14	71	29	164	24	124	16	76	33	92	55	104	22	50	64	12	58	106	30	109	6
	maple	36	19	25	31	15	59	5	46	25	22	26	75	18	33	33	31	34	42	38	75	32	35	2	98	2	70	6	50	21	11
	ash	11	31	24	44	90	34	12	44	19	28	28	97	12	49	21	32	26	51	50	71	20	67	9	72	0	.	4	62	17	14
	oak	7	45	11	56	57	43	5	58	8	38	4	97	4	97	0	.	0	.	0	.	0	.	7	59	0	.	8	48	7	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	42	4	45
	other broadleaves	18	26	23	33	26	48	22	40	30	34	7	57	27	37	10	34	21	51	13	64	5	66	17	42	4	63	79	27	23	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	442	6	364	10	421	14	310	9	292	8	531	19	464	10	448	8	450	14	689	16	278	9	475	9	378	7	440	6	410	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	59	14	25	35	65	27	38	21	53	15	212	14	134	12	143	11	157	14	198	23	175	29	214	12	222	10	127	15	124	4
	fir	106	11	27	35	61	28	79	15	37	19	72	38	127	11	51	18	59	27	85	36	37	38	23	39	8	43	25	42	59	6
	pine	4	44	2	98	4	69	5	41	5	35	0	.	3	99	1	70	0	.	17	97	0	.	1	76	8	35	0	.	4	21
	larch	0	70	1	98	0	.	0	99	2	50	0	.	0	.	2	51	3	70	0	.	4	75	74	20	41	18	27	25	13	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	47	11	38	0	.	2	30
	other conifers	1	99	13	84	0	.	5	53	2	80	0	.	0	.	2	56	0	.	0	.	0	.	0	.	0	.	0	.	1	36
	beech	98	11	120	13	136	18	107	11	87	10	40	40	112	15	77	17	64	29	86	31	60	26	1	99	18	33	48	33	74	5
	maple	26	18	12	49	15	50	17	31	25	24	10	36	11	31	26	27	15	32	17	43	28	45	13	51	3	60	0	.	16	10
	ash	8	29	6	39	18	41	10	31	19	23	1	99	3	53	11	41	12	65	21	48	6	50	4	61	1	71	0	.	8	12
	oak	4	49	12	46	7	68	5	28	10	28	1	98	0	.	0	.	0	.	1	98	0	.	1	99	1	99	2	60	3	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	31	4	33
	other broadleaves	13	41	16	57	6	52	22	28	24	42	14	48	8	40	8	34	13	48	12	55	6	73	7	50	7	59	28	33	14	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	320	5	234	9	313	9	289	6	265	6	350	15	398	6	321	6	324	8	437	12	316	15	347	8	320	6	306	7	321	2
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	108	11	32	62	85	41	75	32	46	32	185	24	176	13	123	28	266	12	312	23	255	26	190	11	278	11	140	26	170	5
	fir	124	11	24	89	47	51	131	24	19	35	111	32	114	14	77	27	31	45	89	39	20	61	17	42	5	39	11	47	59	7
	pine	6	36	39	45	12	96	0	.	5	43	0	.	16	91	0	.	12	61	34	68	0	.	36	30	17	39	1	78	13	21
	larch	0	.	0	.	2	96	3	98	4	84	0	.	0	100	8	98	2	99	0	.	0	.	81	18	37	22	57	22	24	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	32	29	48	35	0	.	11	24
	other conifers	3	47	8	96	0	.	4	98	4	73	0	.	1	70	0	.	0	.	18	96	7	98	0	.	4	76	1	70	2	28
	beech	85	11	178	28	73	40	69	27	71	20	82	41	74	17	126	27	58	37	53	64	92	38	30	41	19	49	69	23	66	7
	maple	26	19	55	42	40	54	15	33	25	26	68	31	27	20	36	41	13	34	4	96	39	52	11	42	6	42	6	46	20	10
	ash	8	37	50	46	33	46	31	48	24	24	25	46	6	33	15	49	5	70	0	.	18	52	5	61	15	63	12	31	12	15
	oak	12	53	30	47	12	59	20	49	13	37	2	97	1	100	3	63	1	99	12	70	0	.	6	100	2	100	11	41	7	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	1	100	71	24	9	25
	other broadleaves	28	21	25	46	68	45	21	56	67	25	35	46	11	25	42	44	45	39	49	49	44	52	31	22	16	31	91	23	38	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	399	5	442	11	373	15	368	12	279	8	509	12	427	6	431	14	435	8	570	12	475	14	439	7	446	7	470	11	430	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	89	8	57	22	91	19	119	12	97	10	211	11	185	9	167	9	212	8	238	14	181	15	186	8	256	6	91	13	149	3
	fir	100	7	25	29	40	20	87	11	31	14	80	20	106	10	54	14	34	19	87	21	21	25	16	24	5	26	13	24	50	4
	pine	8	24	12	27	7	37	7	33	6	21	0	.	9	57	1	55	3	62	16	49	0	.	48	21	35	20	2	71	13	11
	larch	0	58	3	50	17	64	1	48	6	36	4	70	1	81	2	74	8	42	0	.	5	71	73	11	32	12	44	14	18	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	17	24	22	24	0	100	4	17
	other conifers	3	40	7	48	1	71	5	27	7	37	0	.	1	52	2	40	0	100	4	99	3	63	2	56	4	40	0	70	3	15
	beech	117	6	151	9	103	15	88	9	86	8	56	22	91	10	91	12	58	19	78	23	87	22	23	27	14	23	100	13	80	3
	maple	37	11	40	17	27	33	18	18	33	12	32	22	24	18	37	16	50	17	25	27	41	23	13	25	6	31	9	28	25	5
	ash	12	17	27	21	32	22	15	19	20	14	22	30	12	33	19	19	15	28	32	34	13	31	10	28	5	52	19	24	16	7
	oak	13	32	13	29	29	30	7	24	8	17	2	55	1	56	1	55	3	65	3	67	0	.	11	33	1	72	16	26	8	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	1	65	0	72	85	13	10	13
	other broadleaves	21	13	27	17	33	22	30	20	36	14	22	24	16	18	20	23	34	19	27	27	30	34	45	14	31	24	116	11	38	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	399	3	362	6	380	7	376	5	329	4	429	7	445	4	393	5	419	5	510	8	383	9	444	4	410	4	496	4	412	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. 1283405. Birmensdorf, Swiss Federal Research Institute WSL

back