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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total forest.

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
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 2009/17
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	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	37	85	0	.	0	.	50	.	50	.	186	46	20	55	130	79	87	54	0	.	0	.	10	92	62	30	102	50	63	26
	fir	0	.	0	.	0	.	0	.	0	.	49	77	0	.	12	86	0	.	0	.	0	.	0	.	0	.	0	.	5	72
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	5	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	84	9	44	11	83	8	56
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	92	0	.	0	.	2	99
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	41	63	0	.	25	50	0	.	20	.	21	91	14	84	9	91	0	.	0	.	0	.	0	.	2	95	0	.	9	35
	maple	24	81	0	.	0	.	0	.	0	.	19	88	6	84	9	91	0	.	0	.	0	.	0	.	0	.	0	.	4	52
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	91	0	.	0	.	0	.	0	.	0	.	0	.	1	99
	oak	0	.	0	.	5	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	17	81	0	.	0	.	50	.	0	.	4	91	0	.	21	54	0	.	0	.	0	.	0	.	0	.	0	.	5	45
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	119	8	0	.	35	22	100	.	70	.	280	32	39	35	190	63	87	54	0	.	0	.	62	71	73	26	114	48	98	20
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14	49	16	77	6	73	31	45	28	29	73	27	92	23	13	49	69	48	58	61	71	48	46	27	106	27	42	72	45	11
	fir	26	53	0	.	25	44	15	44	9	56	18	76	18	48	30	46	8	67	0	.	11	78	10	96	6	97	9	89	14	19
	pine	3	98	0	.	0	.	1	98	4	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	63	0	.	2	49
	larch	0	.	0	.	0	.	0	.	4	52	0	.	1	98	0	.	8	78	0	.	0	.	36	41	4	71	9	89	4	31
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	1	99	0	.	2	99	0	.	0	.	0	.	0	.	12	97	0	.	0	.	1	70
	beech	27	42	31	39	17	37	44	24	33	24	2	96	15	59	29	43	4	96	8	90	0	.	6	97	0	.	5	90	21	14
	maple	7	70	7	66	18	65	7	68	12	37	17	76	13	64	7	68	7	96	6	89	52	63	6	59	3	97	4	91	11	21
	ash	5	67	15	56	8	66	2	98	11	39	15	56	9	72	2	96	2	96	0	.	10	95	10	96	0	.	0	.	7	22
	oak	1	98	0	.	0	.	2	98	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	90	0	100
	other broadleaves	9	48	10	52	19	65	16	41	14	35	28	60	6	59	4	97	21	65	0	.	50	32	22	60	21	67	19	46	16	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	91	24	79	31	94	24	116	18	117	13	153	22	156	18	85	29	120	27	71	44	194	22	149	27	160	18	94	43	122	6
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	175	30	231	32	333	31	295	20	302	15	357	31	335	31	309	25	201	24	310	24	189	48	112	31	275	22	34	46	209	8
	fir	81	33	6	97	46	43	73	35	71	37	23	63	162	33	101	45	32	68	25	94	6	93	8	74	12	85	3	55	44	15
	pine	13	71	17	97	3	97	29	75	7	42	0	.	18	98	2	97	0	.	0	.	0	.	126	39	118	32	1	100	32	23
	larch	1	99	3	97	84	66	3	99	5	60	33	90	11	98	0	.	47	53	0	.	0	.	68	30	22	38	33	39	24	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	90	8	86	0	.	2	64
	other conifers	6	71	7	67	3	96	13	43	9	51	0	.	0	.	0	.	3	97	0	.	0	.	3	73	29	45	1	99	7	25
	beech	137	28	116	22	106	29	83	24	79	24	45	64	82	47	41	29	4	97	2	94	237	51	33	60	2	99	93	32	73	12
	maple	86	26	52	29	14	79	28	52	86	27	62	59	30	43	44	40	118	32	42	87	26	61	27	39	17	59	20	37	45	12
	ash	34	34	104	44	26	57	29	30	50	29	67	71	18	51	57	42	28	74	102	53	26	61	27	42	5	69	33	30	36	12
	oak	66	51	3	97	32	85	2	75	7	48	0	.	0	.	0	.	18	97	0	.	0	.	27	54	0	.	38	42	20	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	70	0	.	201	21	40	22
	other broadleaves	24	31	63	30	48	45	64	39	48	27	17	95	46	43	66	50	64	35	98	48	163	58	167	19	191	30	257	16	119	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	622	9	603	10	697	12	619	10	664	6	603	20	702	12	620	12	516	11	579	21	647	17	613	9	679	9	714	7	651	3
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	159	24	79	39	126	41	162	23	178	21	397	33	362	20	266	25	371	26	78	34	372	31	231	41	296	19	32	40	194	8
	fir	67	19	72	55	41	53	82	36	21	44	158	48	40	59	72	38	11	57	189	44	26	59	0	.	10	69	15	69	46	14
	pine	31	45	31	54	27	66	2	98	9	54	0	.	4	98	1	98	0	.	39	94	0	.	135	32	86	39	0	.	29	20
	larch	0	99	4	68	0	.	2	75	8	73	0	.	0	.	0	.	0	.	0	.	31	70	71	32	76	29	45	35	23	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	56	38	45	0	.	4	42
	other conifers	0	.	17	73	19	96	6	63	19	84	0	.	0	.	5	67	0	.	0	.	5	95	2	98	1	99	0	.	5	40
	beech	212	12	205	24	42	54	120	19	91	23	113	58	88	53	167	30	167	53	285	28	168	43	41	58	10	56	202	23	136	9
	maple	30	21	20	40	18	67	25	35	63	26	73	37	85	63	73	46	138	40	82	40	79	42	5	99	5	99	10	51	38	14
	ash	22	42	33	38	92	44	30	30	62	29	45	58	57	65	66	37	16	96	71	55	33	77	5	73	14	72	32	60	36	15
	oak	33	47	34	53	144	43	20	46	5	64	16	55	3	98	0	.	14	65	7	93	0	.	16	45	0	.	21	29	20	20
	sweet chestnut	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	6	98	0	.	85	33	14	34
	other broadleaves	41	35	22	38	19	50	37	35	56	29	14	44	7	81	28	42	27	53	51	44	18	73	50	39	35	35	130	22	50	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	596	6	519	9	528	13	486	10	513	8	817	13	647	10	678	11	745	14	803	15	731	19	567	15	573	9	572	8	594	3
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	101	13	45	28	51	43	103	26	90	16	296	21	129	23	192	16	192	24	434	23	182	38	275	15	303	10	179	21	167	5
	fir	104	15	12	51	73	56	48	26	35	27	117	36	90	24	53	22	50	40	111	38	33	52	43	41	4	60	11	48	53	9
	pine	10	37	17	37	9	61	10	55	17	25	0	.	9	63	0	99	0	.	45	67	0	.	36	44	19	37	1	99	12	15
	larch	0	.	1	69	1	98	3	81	6	51	0	.	0	.	2	59	1	98	0	.	1	97	66	33	29	26	70	34	14	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	34	0	.	2	36
	other conifers	4	88	0	.	0	.	3	98	4	51	0	.	0	.	3	56	0	.	0	.	0	.	0	.	0	.	0	.	2	44
	beech	150	12	220	14	187	20	124	20	102	11	74	32	149	24	130	19	100	27	57	60	141	25	37	78	13	50	90	35	110	6
	maple	22	16	27	30	3	72	5	51	23	19	34	45	17	36	36	27	45	29	7	66	38	42	6	72	1	70	1	99	18	10
	ash	14	35	15	39	63	40	27	63	21	19	14	67	19	46	40	31	36	55	17	55	38	57	0	.	1	99	5	68	19	13
	oak	6	36	13	51	24	59	12	34	10	27	0	.	4	98	0	.	0	.	0	.	0	.	2	98	1	99	6	51	6	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	.	57	42	4	44
	other broadleaves	12	27	23	37	27	72	16	44	13	26	16	64	27	35	9	50	43	38	14	67	32	50	25	56	1	99	37	37	17	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	422	5	372	10	439	16	351	9	321	5	551	20	444	9	465	6	466	12	685	9	466	13	490	10	388	6	458	6	423	2
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	73	14	44	35	86	19	64	17	69	14	236	15	141	11	185	10	147	17	172	19	169	27	172	16	201	10	124	18	126	4
	fir	98	12	62	29	41	27	90	14	34	18	45	36	120	12	49	22	57	28	60	44	38	44	8	70	9	43	16	80	57	6
	pine	2	41	2	67	7	63	5	38	5	34	0	.	4	100	1	71	0	.	0	.	0	.	11	86	6	42	0	.	4	26
	larch	0	70	1	98	2	70	0	99	0	100	0	.	0	.	1	72	7	59	0	.	4	75	84	19	44	21	30	31	14	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	47	7	52	0	.	2	36
	other conifers	1	99	2	98	2	98	3	61	3	80	0	.	0	100	2	69	0	.	0	.	0	.	0	.	0	.	0	.	1	34
	beech	86	9	125	14	114	19	94	12	96	12	47	42	102	16	92	19	59	32	84	33	80	30	2	99	24	34	47	44	75	5
	maple	21	20	25	34	14	62	8	35	15	28	11	36	11	29	18	34	15	42	17	48	28	46	16	60	3	63	0	.	13	11
	ash	9	38	11	51	47	36	12	29	23	25	14	85	8	46	6	45	7	83	33	44	6	65	3	72	0	100	0	.	11	13
	oak	4	58	5	59	24	49	4	33	11	31	0	.	0	.	1	70	0	.	1	98	0	.	0	.	0	.	1	99	4	22
	sweet chestnut	0	.	0	.	2	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	45	1	45
	other broadleaves	13	37	4	63	15	82	10	33	23	43	10	64	7	54	13	41	23	48	8	68	12	82	3	73	2	70	29	36	12	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	307	6	280	9	355	10	289	6	279	5	362	11	394	6	368	5	314	9	375	13	336	16	310	8	295	6	261	10	318	2
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	120	11	10	64	124	39	90	29	69	24	148	21	213	12	140	26	271	13	285	24	248	22	206	11	290	10	104	16	176	5
	fir	128	13	10	78	33	55	184	18	27	32	84	41	107	14	68	28	32	40	97	37	23	58	20	38	7	42	17	35	58	8
	pine	4	33	37	51	11	86	0	.	5	50	0	.	14	100	0	.	8	57	13	72	0	.	38	28	16	30	5	78	12	21
	larch	0	.	0	.	1	97	3	98	3	79	0	.	0	.	14	98	3	99	0	.	0	.	92	16	30	20	56	22	26	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	30	28	39	30	1	100	9	21
	other conifers	3	43	28	69	0	.	8	72	5	99	0	.	1	70	0	.	0	.	18	96	7	98	0	100	4	76	0	.	3	27
	beech	123	11	131	25	96	31	102	32	91	16	66	64	99	16	120	33	43	38	48	57	75	47	24	46	20	49	82	20	75	7
	maple	34	20	53	46	32	51	19	52	27	43	35	47	23	18	46	37	23	42	3	97	41	59	6	58	7	39	3	61	20	11
	ash	8	32	78	41	51	31	20	40	33	33	10	78	12	38	44	43	11	45	9	97	16	79	12	74	16	63	8	40	15	15
	oak	6	55	27	58	10	63	13	63	15	34	1	97	1	70	4	67	0	99	12	73	0	.	11	58	2	100	12	40	7	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	1	100	87	21	13	22
	other broadleaves	25	23	26	47	59	37	16	65	44	42	26	41	15	25	55	47	51	39	42	49	40	62	23	26	18	33	68	16	34	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	450	5	401	10	416	12	455	13	320	9	370	15	486	6	491	14	441	9	526	12	450	14	463	6	449	6	442	5	448	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	111	8	70	20	115	18	119	12	122	9	233	11	191	8	188	9	212	9	243	13	203	14	180	9	256	6	85	10	158	3
	fir	98	7	30	28	43	22	79	11	35	16	77	21	99	9	59	13	35	19	85	22	24	25	15	25	7	26	12	26	49	4
	pine	9	24	16	29	9	35	8	46	8	17	0	.	9	60	1	48	3	58	16	49	0	.	54	20	35	19	2	66	14	10
	larch	0	55	2	43	14	65	2	43	4	29	2	99	1	93	3	75	9	40	0	.	6	61	77	10	35	12	45	14	18	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	16	23	20	20	0	100	4	15
	other conifers	3	38	7	43	3	71	5	28	6	37	0	.	1	52	2	37	0	100	4	99	2	76	2	53	5	39	0	100	3	15
	beech	128	6	151	10	101	12	94	8	85	7	57	25	95	10	101	12	54	20	80	23	103	20	23	28	14	24	99	13	83	3
	maple	32	11	29	16	15	28	14	22	36	15	34	21	23	20	35	17	44	19	22	33	42	23	11	26	6	29	8	28	23	5
	ash	14	16	36	23	47	19	19	18	31	13	21	30	15	24	33	18	15	30	36	29	19	32	11	37	7	45	16	26	19	6
	oak	15	29	13	31	35	29	7	23	8	16	2	56	1	52	1	54	4	67	4	57	0	.	10	34	1	72	17	25	9	11
	sweet chestnut	0	.	0	.	0	100	0	.	0	100	0	.	0	.	0	.	0	100	0	.	0	.	1	66	0	100	99	13	12	13
	other broadleaves	21	14	23	19	29	24	25	20	30	15	18	24	15	18	26	24	40	21	33	28	41	30	45	15	35	24	114	11	38	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	429	3	378	6	411	7	372	5	365	4	444	8	450	4	447	5	415	6	522	8	440	9	445	4	422	4	498	4	430	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. 1283355. Birmensdorf, Swiss Federal Research Institute WSL

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