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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NFI5
number of stems
development stage · main tree species
regional demarcation: economic region
unit: n/ha
evaluation area: accessible forest without shrub forest NFI4/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	39	23
	fir	0	.	0	.	0	.	0	.	5	86	27	65	0	.	0	.	27	89	0	.	0	.	4	89	0	.	0	.	4	64
	pine	59	48	0	.	5	87	0	.	5	86	0	.	0	.	0	.	0	.	0	.	0	.	4	90	3	65	0	.	4	48
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	55	17	82	8	89	6	65
	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	.	52	71	0	.	0	.	0	.	14	50
	maple	0	.	0	.	0	.	13	86	0	.	14	65	0	.	17	82	0	.	0	.	175	71	0	.	0	.	0	.	9	74
	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	227	38	16	22	95	30	65	38	78	18
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	38	33	3	96	13	66	56	48	11	55	97	36	74	28	48	34	74	24	18	93	19	77	16	52	66	21	53	47	46	11
	fir	69	73	13	50	2	95	43	33	12	49	9	65	18	39	15	41	3	97	0	.	2	96	0	.	0	.	0	.	14	33
	pine	2	98	0	.	0	.	8	50	1	98	0	.	0	.	1	98	0	.	0	.	0	.	0	.	7	71	11	98	3	42
	larch	0	.	4	96	22	87	0	.	0	.	0	.	0	.	0	.	8	82	0	.	5	95	11	58	17	51	10	40	6	30
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	99	0	.	1	100
	other conifers	0	.	0	.	0	.	5	72	3	97	0	.	2	99	5	98	0	.	0	.	0	.	0	.	5	98	0	.	2	47
	beech	18	40	28	29	13	50	18	35	28	30	2	95	40	35	27	31	20	69	5	94	0	.	0	.	1	99	5	97	16	15
	maple	6	57	14	48	0	.	0	.	9	65	5	95	7	54	16	60	17	63	3	93	11	95	3	97	9	83	0	.	7	24
	ash	0	.	13	70	2	96	0	.	0	.	9	95	0	.	7	71	0	.	0	.	5	95	7	97	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	.	1	97	0	100
	other broadleaves	9	57	4	95	2	96	9	69	19	63	10	95	11	54	2	98	36	57	16	93	9	95	14	79	10	65	46	38	14	20
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	144	43	79	21	67	29	141	23	83	17	132	28	152	16	123	23	157	19	42	46	50	54	50	30	124	15	126	19	113	8
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	90	35	121	41	173	37	230	20	180	19	269	39	259	25	264	26	193	23	275	26	103	42	69	24	282	16	46	38	160	7
	fir	101	30	21	80	27	47	130	28	29	38	40	51	136	43	86	50	4	69	26	64	7	66	7	72	1	76	4	45	39	15
	pine	5	59	0	.	4	98	10	86	7	63	0	.	9	99	2	97	0	.	24	97	0	.	51	49	101	29	0	.	21	22
	larch	0	.	7	98	59	79	2	99	19	48	28	64	5	99	0	.	26	61	0	.	0	.	62	29	41	35	60	25	31	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	86	40	35	0	.	5	36
	other conifers	3	99	2	98	2	98	5	70	3	60	0	.	1	98	0	.	25	79	10	97	0	.	7	61	19	40	0	.	6	27
	beech	137	23	167	22	142	39	112	22	100	20	25	63	70	42	27	36	70	54	2	66	147	78	25	51	5	70	78	26	75	10
	maple	100	28	110	23	65	57	32	41	47	26	19	82	38	27	80	36	143	20	24	59	71	55	59	32	1	99	30	38	51	10
	ash	34	36	65	28	33	45	20	44	28	28	56	57	14	49	55	39	29	51	41	56	0	.	43	37	6	78	29	29	30	12
	oak	40	69	4	80	24	64	8	58	9	40	0	.	2	98	0	.	11	98	0	.	0	.	32	42	0	.	26	46	15	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	104	27	20	28
	other broadleaves	19	30	63	23	68	36	50	46	42	27	124	41	58	36	61	35	80	36	115	62	31	39	131	18	133	25	203	16	102	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	529	9	560	9	597	13	599	9	463	8	561	19	592	12	576	12	581	9	518	17	358	27	488	8	628	7	580	7	554	3
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	104	27	102	45	177	27	237	21	171	17	384	30	382	28	278	27	310	25	155	35	282	31	276	24	307	16	51	35	205	8
	fir	66	25	1	97	41	60	66	35	44	35	54	87	101	40	64	46	56	46	143	46	19	62	0	99	9	61	12	68	39	14
	pine	16	57	31	51	5	72	10	73	6	43	0	.	15	67	0	.	0	.	0	.	0	.	123	35	52	48	7	86	26	23
	larch	0	99	6	58	10	96	2	75	4	61	0	.	0	.	0	.	12	97	0	.	57	67	94	21	39	33	42	42	26	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	60	45	49	2	99	8	38
	other conifers	2	99	27	69	0	.	8	42	27	61	0	.	0	.	5	96	0	.	0	.	8	65	1	99	1	99	0	.	5	36
	beech	168	21	212	25	54	39	68	22	92	25	92	63	71	46	185	49	42	50	170	40	109	51	18	69	12	46	185	22	103	10
	maple	40	26	40	43	21	66	29	27	62	24	54	47	61	64	88	44	106	36	81	38	66	47	0	.	17	61	13	45	36	13
	ash	18	45	7	65	15	50	21	33	19	44	27	44	69	59	49	42	17	67	74	64	23	68	8	48	9	67	34	48	24	18
	oak	26	76	31	74	76	65	7	53	1	56	8	96	1	98	0	.	10	67	0	.	0	.	9	54	0	.	25	34	14	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	82	1	99	104	26	19	26
	other broadleaves	47	45	17	39	37	49	48	33	44	26	48	68	21	55	15	96	39	51	43	53	70	64	44	32	51	60	148	19	62	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	488	9	476	9	436	13	494	10	469	8	668	17	721	14	684	15	591	11	666	16	633	17	598	11	543	9	622	6	567	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	327	32	190	25	216	16	264	21	356	35	109	23	250	19	277	11	116	27	156	6
	fir	86	16	39	51	52	53	74	25	28	37	125	43	43	28	37	23	31	55	112	38	20	60	36	48	4	90	12	61	46	10
	pine	12	53	15	35	15	61	7	68	11	33	0	.	6	97	0	.	0	.	28	82	0	.	39	42	32	38	1	99	12	19
	larch	0	.	1	69	1	98	1	99	1	56	0	.	0	.	0	.	0	.	0	.	0	.	67	32	30	28	69	29	14	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	98	9	47	0	.	1	45
	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	66	30	164	24	119	16	72	33	92	55	88	25	48	64	11	58	97	30	106	6
	maple	36	19	25	31	15	59	5	46	25	22	24	75	18	33	33	29	32	42	38	75	32	32	2	98	2	70	7	45	20	11
	ash	11	31	24	44	90	34	12	44	19	28	26	97	12	49	20	32	25	51	50	71	21	56	8	72	0	.	3	62	16	14
	oak	7	45	11	56	57	43	5	58	8	38	3	97	4	97	1	99	0	.	0	.	0	.	7	59	0	.	7	48	7	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	42	4	45
	other broadleaves	18	26	23	33	26	48	22	40	30	34	6	57	27	37	9	35	20	52	13	64	5	67	17	42	5	54	80	25	24	12
	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	578	18	464	10	439	8	445	14	689	16	274	8	477	9	369	7	432	6	409	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	57	14	24	35	61	27	38	21	51	15	224	14	138	12	163	11	157	14	191	23	175	29	201	12	212	10	125	14	124	4
	fir	102	11	26	35	57	29	79	15	36	19	71	38	123	11	49	18	56	27	84	34	37	38	22	39	7	43	24	42	56	6
	pine	4	44	1	98	4	69	5	41	4	35	0	.	3	100	1	70	0	.	20	82	0	.	1	76	8	35	0	.	3	21
	larch	0	70	1	98	0	.	0	99	2	50	0	.	0	.	1	51	3	70	0	.	4	75	81	21	40	18	28	23	14	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	47	10	38	0	.	2	30
	other conifers	1	99	12	84	0	.	5	53	2	80	0	.	0	.	2	57	0	.	0	.	0	.	0	.	0	.	0	.	1	36
	beech	95	11	119	12	127	19	107	11	89	10	39	40	116	15	73	17	61	29	83	31	60	26	1	99	17	33	46	33	72	5
	maple	25	18	12	50	16	45	17	31	24	23	10	36	10	31	24	27	18	31	20	37	28	45	12	51	3	60	0	.	15	9
	ash	9	28	6	39	19	39	10	31	22	24	1	99	3	53	10	41	15	54	20	49	6	50	3	61	1	71	0	.	8	12
	oak	4	49	12	46	6	68	5	28	10	27	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	.	46	31	4	33
	other broadleaves	12	41	16	57	21	51	22	28	25	38	13	48	8	41	15	48	12	48	15	46	6	73	10	40	7	52	27	33	15	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	310	6	228	10	310	9	289	6	266	6	359	14	401	6	337	6	321	8	434	12	316	15	342	8	306	6	298	7	319	2
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	106	11	32	62	79	41	75	32	48	31	203	22	185	12	129	24	265	11	292	21	214	25	166	11	263	10	123	23	165	5
	fir	120	11	24	89	44	51	131	24	19	36	104	31	106	14	71	26	25	46	84	36	16	58	14	42	4	39	9	43	52	7
	pine	5	36	39	45	11	97	0	.	5	43	0	.	15	91	0	.	12	52	29	62	20	78	31	30	15	36	2	69	12	20
	larch	0	.	0	.	1	97	3	98	4	84	0	.	0	100	7	98	3	71	0	.	2	98	78	16	47	17	80	16	30	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	30	27	42	32	0	100	10	22
	other conifers	3	47	8	96	0	.	4	98	4	73	0	.	1	57	0	.	0	.	14	97	5	99	0	.	3	76	1	70	2	28
	beech	81	11	178	28	68	41	69	27	74	19	75	42	69	17	109	28	48	38	71	45	96	35	25	41	15	49	58	22	59	7
	maple	26	18	55	42	37	54	15	33	32	29	62	32	34	30	34	39	11	33	13	78	36	46	10	40	7	43	5	43	20	10
	ash	7	37	50	46	31	47	31	48	30	26	23	46	6	33	13	49	12	68	5	98	21	47	4	61	12	63	11	27	12	14
	oak	11	53	30	47	11	60	20	49	13	35	2	97	2	77	3	63	0	99	10	71	4	98	6	81	1	100	10	38	7	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	95	1	100	53	25	8	25
	other broadleaves	28	20	25	46	63	45	21	56	67	24	32	46	15	24	54	37	50	35	43	45	46	46	30	20	13	32	78	22	37	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	388	5	442	11	345	17	368	12	295	9	502	11	433	6	420	12	429	8	560	13	460	12	398	6	422	6	430	9	415	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	89	8	56	22	88	19	118	12	95	10	241	12	186	8	172	8	211	8	223	13	164	15	166	8	239	6	88	12	147	3
	fir	98	7	24	29	39	20	87	11	30	14	73	20	100	10	51	14	30	20	73	20	18	25	13	24	4	26	11	23	45	4
	pine	8	24	12	27	6	37	7	33	6	21	0	.	9	57	1	55	4	53	19	41	6	79	40	21	32	19	2	53	12	10
	larch	0	58	3	50	16	64	1	48	6	36	3	70	1	81	2	74	8	40	0	.	10	58	72	10	38	11	56	11	21	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	15	22	27	19	0	79	5	15
	other conifers	3	40	7	48	1	71	5	27	6	37	0	.	1	46	2	40	4	80	5	71	3	63	1	56	4	34	0	70	3	15
	beech	114	6	151	9	99	16	87	9	86	8	51	22	87	10	85	12	52	18	67	22	81	21	20	27	11	23	83	12	73	3
	maple	36	11	42	17	27	33	18	18	34	12	29	22	26	20	37	16	46	16	26	25	41	21	17	24	5	29	12	26	25	5
	ash	12	17	27	20	31	21	15	19	22	13	20	30	11	33	19	19	16	27	28	32	14	30	12	27	5	43	16	22	15	6
	oak	13	32	13	29	28	30	7	23	8	17	2	55	1	51	1	49	3	65	2	68	1	99	10	32	1	72	14	24	8	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	2	67	0	72	69	13	9	13
	other broadleaves	22	15	26	17	36	21	30	19	36	14	33	26	19	17	24	22	40	20	44	36	29	32	44	13	32	20	110	10	41	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	395	3	360	6	372	7	375	5	329	4	452	7	440	4	394	5	413	5	488	8	367	8	413	4	399	4	462	4	405	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. 1356758. Birmensdorf, Swiss Federal Research Institute WSL

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