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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NFI4
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 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	.	9	93	56	31	81	56	60	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	.	33	77	8	45	9	87	8	53
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	93	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	.	4	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	.	57	68	67	28	90	54	93	20
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13	49	16	77	6	73	30	45	28	28	66	27	79	24	14	44	57	46	34	58	59	50	29	28	69	25	19	53	39	11
	fir	25	53	0	.	25	44	14	44	8	56	16	77	18	43	28	47	6	69	10	94	9	79	5	98	3	99	2	97	11	18
	pine	3	98	0	.	0	.	1	99	4	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	49	0	.	2	42
	larch	0	.	0	.	0	.	0	.	4	52	0	.	1	98	0	.	6	79	0	.	0	.	24	37	8	47	7	60	5	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	98	0	.	0	100
	other conifers	0	.	0	.	0	.	0	.	1	99	0	.	2	99	0	.	0	.	0	.	0	.	7	98	6	77	0	.	2	53
	beech	26	42	31	39	17	37	42	24	38	25	1	97	13	60	28	41	3	97	4	95	0	.	3	98	4	99	7	60	18	14
	maple	7	71	7	66	18	65	7	68	11	38	15	76	11	65	6	68	5	97	3	95	43	65	5	51	1	99	3	73	9	21
	ash	4	67	15	56	8	66	2	98	10	39	13	57	7	73	1	96	2	97	0	.	8	96	5	98	0	.	4	98	6	22
	oak	1	98	0	.	0	.	5	72	3	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	1	48
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	100
	other broadleaves	8	48	10	52	19	65	23	42	13	36	25	61	12	62	4	97	19	57	13	95	42	34	15	50	23	63	51	41	20	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	87	25	79	31	94	24	124	18	121	14	136	23	143	17	82	28	99	27	63	31	162	25	94	26	132	18	96	26	112	6
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	178	29	221	32	307	29	295	20	297	16	449	30	331	29	282	24	221	22	247	25	180	40	113	27	252	17	45	31	200	7
	fir	80	33	6	98	38	44	73	35	70	37	15	68	148	33	89	46	27	69	13	97	4	94	6	69	8	86	3	48	36	15
	pine	13	71	17	98	2	98	29	75	7	42	0	.	16	98	2	97	0	.	24	96	0	.	93	39	89	29	1	100	29	21
	larch	1	99	3	97	71	67	3	99	5	60	21	93	10	98	0	.	39	54	0	.	0	.	76	26	43	33	59	24	34	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	90	19	55	0	.	3	48
	other conifers	5	71	7	67	3	97	13	43	9	51	0	.	0	.	0	.	29	83	0	.	0	.	2	73	20	43	0	100	7	25
	beech	134	28	127	21	89	31	83	24	78	25	36	55	75	48	36	29	3	98	1	97	185	54	27	54	1	99	72	31	61	12
	maple	84	26	67	30	12	80	28	52	85	27	45	55	35	38	57	31	97	33	34	66	59	62	31	30	11	59	25	34	43	11
	ash	33	34	103	43	22	58	29	30	52	28	44	73	17	52	89	47	23	75	63	49	26	51	35	34	8	51	25	29	34	12
	oak	64	51	3	97	27	86	2	75	7	48	0	.	0	.	0	.	15	98	0	.	0	.	20	52	0	.	28	42	17	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	70	0	.	154	21	34	22
	other broadleaves	32	34	66	28	51	39	64	39	47	28	118	53	55	39	62	48	77	35	177	55	127	61	131	18	159	25	237	14	122	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	624	9	618	10	622	14	619	10	656	6	729	15	686	12	616	12	532	9	560	18	581	18	545	9	611	8	649	6	619	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	391	29	362	20	287	22	371	26	78	34	372	31	213	37	328	18	30	40	198	8
	fir	67	19	72	55	41	53	82	36	21	44	131	49	40	59	75	33	11	57	189	44	26	59	0	.	9	69	14	69	44	14
	pine	31	45	31	54	27	66	2	98	9	54	0	.	4	98	1	98	0	.	39	94	0	.	113	32	78	39	0	.	27	20
	larch	0	99	4	68	0	.	2	75	8	73	0	.	0	.	0	.	0	.	0	.	31	70	67	28	70	29	48	32	24	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	45	53	44	0	.	7	40
	other conifers	0	.	17	73	19	96	6	63	19	84	0	.	0	.	5	67	0	.	0	.	5	95	1	99	1	99	0	.	4	40
	beech	212	12	205	24	42	54	120	19	91	23	93	59	88	53	153	29	167	53	285	28	168	43	34	59	9	56	187	23	129	9
	maple	30	21	20	40	18	67	25	35	63	26	61	39	85	63	71	42	138	40	82	40	79	42	4	99	5	99	9	47	36	14
	ash	22	42	33	38	92	44	30	30	62	29	37	59	57	65	59	38	16	96	71	55	33	77	5	73	13	73	29	60	34	15
	oak	33	47	34	53	144	43	20	46	5	64	13	56	3	98	1	99	14	65	7	93	0	.	13	46	0	.	19	29	19	20
	sweet chestnut	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	5	98	0	.	77	33	13	34
	other broadleaves	41	35	22	38	19	50	37	35	56	29	19	44	7	81	25	42	27	53	51	44	18	73	58	39	31	36	122	21	49	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	745	13	647	10	677	10	745	14	803	15	731	19	523	14	597	9	535	8	585	3
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	100	13	45	28	51	43	103	26	89	16	296	21	143	22	194	15	239	24	405	24	178	34	264	15	288	10	179	21	168	5
	fir	102	15	12	51	73	56	48	26	34	27	117	36	84	24	51	21	45	40	114	34	28	53	42	41	3	60	11	48	52	9
	pine	10	37	17	37	9	61	10	55	17	25	0	.	8	63	0	99	0	.	48	57	0	.	34	44	18	37	1	99	12	15
	larch	0	.	1	69	1	98	3	81	6	51	0	.	0	.	2	59	1	98	0	.	32	92	90	35	33	23	70	34	16	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	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	149	11	220	14	187	20	124	20	103	10	74	32	162	24	124	19	91	27	55	58	122	27	35	78	12	50	90	35	109	6
	maple	22	16	27	30	3	72	5	51	25	19	34	45	16	37	35	27	41	30	13	54	33	43	6	72	1	70	1	99	18	10
	ash	14	35	15	39	63	40	27	63	22	19	14	67	18	46	38	31	32	55	15	56	33	57	0	.	1	99	5	68	18	13
	oak	6	36	13	51	24	59	12	34	11	27	0	.	3	98	0	.	0	.	2	96	0	.	2	98	1	99	6	51	6	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	57	42	4	44
	other broadleaves	12	27	23	37	27	72	16	44	13	26	16	64	26	35	15	43	39	38	18	52	28	51	27	51	2	73	37	37	18	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	420	5	372	10	439	16	351	9	323	5	551	20	460	10	463	6	488	12	670	9	455	12	500	10	375	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	70	14	41	35	83	19	64	17	66	14	250	15	144	11	202	11	143	15	172	19	160	28	164	15	195	10	122	18	126	4
	fir	95	12	57	29	40	27	90	14	34	17	44	35	118	12	47	22	52	28	60	44	35	45	7	70	9	43	15	80	55	6
	pine	2	41	2	68	7	63	5	38	5	34	0	.	4	100	1	71	0	.	0	.	0	.	10	86	5	42	0	.	4	26
	larch	0	70	1	98	2	70	0	99	0	100	0	.	0	.	1	72	6	59	0	.	4	75	80	19	42	21	35	28	14	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	47	6	52	0	.	1	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	84	9	142	16	111	19	94	12	94	12	45	42	101	16	90	19	53	33	84	33	75	30	2	99	23	34	45	45	74	5
	maple	20	20	27	29	13	62	8	35	14	28	11	36	11	29	17	34	21	34	17	48	26	46	15	60	3	63	0	.	13	11
	ash	9	39	15	45	47	35	12	29	25	24	13	85	8	46	6	45	10	62	33	44	5	65	3	72	0	100	0	.	11	13
	oak	4	59	5	59	24	49	4	33	10	31	0	.	0	.	1	70	0	.	1	98	0	.	0	.	0	.	0	99	3	22
	sweet chestnut	0	.	0	.	2	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	45	1	45
	other broadleaves	13	36	9	63	18	70	10	33	26	38	9	64	6	54	12	41	30	41	8	68	11	82	5	58	3	59	27	36	13	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	300	6	300	10	349	10	289	6	277	5	373	10	394	6	379	5	316	9	375	13	317	17	297	9	286	6	259	10	316	2
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	118	11	10	64	124	39	90	29	67	24	161	21	220	11	161	22	261	12	288	20	215	20	184	11	256	10	97	15	171	4
	fir	123	13	10	78	33	55	184	18	26	32	76	41	99	14	61	29	27	40	87	34	17	56	17	38	5	42	13	35	51	8
	pine	4	33	37	51	11	86	0	.	5	50	0	.	13	100	0	.	8	48	12	59	33	85	33	28	14	29	4	73	12	20
	larch	0	.	0	.	1	97	3	98	3	79	0	.	0	.	13	98	3	73	0	.	4	98	89	15	36	17	65	17	29	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	29	26	41	25	1	100	10	18
	other conifers	3	43	28	69	0	.	8	72	4	99	0	.	1	70	0	.	0	.	13	97	5	99	0	100	3	68	0	.	2	27
	beech	116	12	131	25	96	31	102	32	88	16	60	64	91	16	109	33	39	36	65	42	75	41	21	45	15	49	68	19	67	7
	maple	33	20	53	46	32	51	19	52	26	43	32	47	28	28	42	37	21	40	11	82	36	48	8	47	11	53	3	52	20	11
	ash	7	32	78	41	51	31	20	40	32	33	9	79	11	38	40	44	18	53	19	70	33	51	12	68	13	63	9	36	15	14
	oak	6	55	27	58	10	63	13	63	14	34	1	97	2	62	3	68	0	99	8	74	2	98	11	49	1	100	13	33	7	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	96	1	100	69	21	13	21
	other broadleaves	25	22	26	47	59	37	16	65	43	42	24	42	15	24	77	45	52	36	38	44	41	51	21	25	15	32	60	15	33	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	435	5	401	10	416	12	455	13	309	10	364	15	481	6	505	13	431	9	542	13	462	13	432	6	410	6	401	5	429	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	109	8	69	20	116	17	119	12	119	9	249	11	194	8	197	8	213	9	224	12	189	13	159	9	236	6	79	10	155	2
	fir	96	7	30	28	41	22	79	11	35	16	69	21	94	9	56	13	30	20	72	20	21	25	12	25	6	27	10	25	44	4
	pine	9	24	16	29	9	35	8	46	8	17	0	.	8	60	1	48	3	49	19	40	9	86	45	20	31	18	2	63	13	10
	larch	0	55	2	43	13	65	2	43	4	29	2	100	1	93	3	75	8	38	0	.	11	53	76	10	38	11	54	11	21	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	15	22	24	18	0	100	4	14
	other conifers	3	38	7	43	3	71	5	28	6	37	0	.	1	52	2	37	4	85	3	99	2	76	1	53	5	34	0	100	3	15
	beech	125	6	156	9	97	12	93	8	84	7	52	25	92	10	95	12	48	20	69	22	93	19	20	27	12	23	81	13	75	3
	maple	31	11	32	16	14	28	14	22	35	15	31	21	25	20	35	16	40	18	23	29	41	22	12	22	6	33	9	26	22	5
	ash	13	16	36	22	45	19	19	18	32	12	19	30	14	24	35	20	17	29	32	27	23	29	12	31	6	40	15	24	19	6
	oak	14	29	13	31	33	29	8	23	9	16	2	56	1	47	1	48	3	67	3	53	1	99	9	31	1	72	15	23	8	11
	sweet chestnut	0	.	0	.	0	100	0	.	0	100	0	.	0	.	0	.	0	100	0	.	0	.	3	75	0	100	81	13	11	13
	other broadleaves	22	14	25	18	30	22	26	19	30	15	28	28	16	17	30	24	43	19	53	38	38	28	42	14	35	21	109	10	41	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	422	3	384	6	404	7	372	5	361	4	452	8	445	4	454	5	410	5	498	8	427	8	407	4	400	4	455	3	418	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. 1356727. Birmensdorf, Swiss Federal Research Institute WSL

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