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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NFI3
growing stock (stemwood)
development stage · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	29	74	0	0	80	92	94	71	164	53	262	56	173	43	158	48	184	49	98	82	69	95	68	66	672	33	88	88	2139	17
	fir	48	100	0	.	0	.	22	58	41	74	71	79	29	89	25	84	18	100	0	0	0	.	8	71	49	87	0	0	311	32
	pine	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	14	87
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	100	0	.	0	0	19	71	55	58	48	77	145	38
	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	8	100	6	100	29	61	40	57	65	56	4	100	12	90	3	100	58	100	1	100	0	.	0	.	10	100	30	73	267	30
	maple	0	.	0	.	0	.	0	.	0	.	0	.	23	60	15	75	6	100	1	100	12	100	0	.	0	.	0	.	57	39
	ash	0	.	0	.	0	.	3	100	18	100	12	91	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	64
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	3	100	0	.	0	.	0	.	5	82	0	.	2	100	9	92	0	.	0	.	1	100	3	100	9	100	0	0	32	42
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0
	total	88	86	6	100	108	75	159	52	305	39	350	45	241	35	208	43	290	49	100	81	82	95	99	59	795	31	166	70	2998	14
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	221	40	47	57	138	56	122	65	235	42	78	58	291	35	275	71	271	61	159	56	75	72	236	69	881	24	61	60	3089	14
	fir	218	50	16	73	1	100	45	56	156	58	1	100	126	58	146	70	27	73	2	100	94	54	0	0	49	69	0	.	881	23
	pine	74	60	0	.	13	100	37	100	83	63	0	.	0	.	0	.	0	.	0	.	0	.	49	65	22	98	0	.	279	32
	larch	14	100	0	.	12	100	0	.	36	61	0	.	0	.	0	.	2	100	13	100	56	100	208	41	87	46	73	43	501	23
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	76	3	100	0	.	49	71
	other conifers	1	100	1	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	8	100	5	74	0	.	16	55
	beech	138	38	499	31	105	47	245	33	326	30	46	74	16	52	30	69	48	87	8	100	74	69	21	96	0	0	40	57	1595	14
	maple	21	54	29	78	6	60	11	48	33	47	16	63	12	66	9	69	74	75	0	.	43	95	8	71	1	100	2	71	267	29
	ash	33	77	36	75	22	75	40	87	16	83	12	100	5	100	1	100	2	100	0	.	3	74	1	100	0	.	32	58	202	29
	oak	26	77	53	74	22	71	20	71	37	59	0	.	0	.	0	.	0	.	0	.	0	.	20	71	0	.	1	100	179	31
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	53	61	53
	other broadleaves	27	53	19	49	41	80	5	100	16	55	0	.	17	56	10	61	9	57	2	119	0	.	39	65	18	65	78	41	280	21
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0
	total	773	29	699	29	360	37	524	31	939	23	153	48	467	34	471	64	434	42	185	50	345	43	637	31	1066	22	347	24	7400	9
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	844	24	187	44	587	30	1084	20	2408	17	769	28	692	29	874	27	457	27	121	59	131	39	880	27	1544	18	212	30	10790	7
	fir	399	34	29	74	106	46	225	32	305	30	352	69	97	55	269	42	78	85	17	52	0	.	27	45	47	54	231	53	2185	16
	pine	47	53	48	69	24	58	35	57	195	35	0	.	32	82	2	100	18	72	15	77	0	.	385	31	330	27	0	.	1131	15
	larch	29	80	6	76	62	58	40	71	50	49	0	.	5	100	14	86	32	76	0	.	17	100	405	20	465	28	375	23	1500	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	57	81	36	71	0	.	109	51
	other conifers	50	93	53	74	21	82	17	50	63	51	0	.	0	.	3	100	23	77	2	100	2	100	5	90	12	44	23	100	274	28
	beech	339	33	92	43	117	43	296	30	594	34	15	45	39	56	206	32	200	66	35	73	37	72	187	40	34	94	1359	34	3551	16
	maple	93	39	32	73	89	54	144	32	347	21	62	49	19	80	134	40	94	33	77	51	21	64	69	29	17	61	119	38	1317	11
	ash	99	43	31	58	91	41	156	46	484	26	25	64	27	55	186	40	41	40	61	51	9	71	86	39	35	39	332	28	1661	12
	oak	41	68	45	100	62	62	54	46	161	34	11	100	2	100	0	.	24	69	0	.	0	.	96	42	0	.	176	32	673	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	61	0	.	906	21	930	21
	other broadleaves	82	55	69	72	73	37	145	36	219	24	61	46	35	50	85	47	124	55	89	51	27	68	452	19	341	26	1462	14	3263	8
	indeterminable	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0
	total	2025	17	592	26	1231	21	2197	15	4824	12	1296	28	948	25	1789	19	1091	23	418	34	243	33	2674	14	2862	13	5195	12	27384	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2080	16	728	31	310	37	1205	26	1977	18	1200	30	1363	26	1218	27	504	33	161	45	1335	39	1965	19	1678	21	814	27	16538	7
	fir	1536	18	142	61	231	58	429	31	403	31	381	35	195	43	556	30	97	68	118	52	52	58	240	47	35	70	116	83	4531	10
	pine	434	32	70	42	45	114	59	47	235	41	0	.	27	72	3	100	10	100	64	99	0	.	486	26	462	33	81	100	1977	15
	larch	0	.	12	100	0	.	47	62	159	50	0	.	0	.	0	.	0	.	0	.	64	75	1057	20	296	35	474	30	2110	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	133	48	97	64	7	100	236	38
	other conifers	29	100	43	84	44	100	147	61	31	67	0	.	0	.	9	73	5	100	1	100	1	100	1	100	3	100	0	100	315	36
	beech	3442	13	1094	23	383	45	891	27	622	20	167	43	350	39	739	29	140	47	154	48	310	46	216	44	152	51	1328	20	9987	7
	maple	323	22	72	52	84	55	58	59	210	30	69	51	91	35	207	29	102	47	25	54	170	42	34	51	13	75	90	47	1547	11
	ash	438	26	210	49	379	45	116	41	560	26	142	63	184	74	340	32	49	59	56	90	306	82	66	58	60	43	239	37	3145	14
	oak	515	31	92	62	256	43	143	48	262	63	8	79	3	100	3	100	41	100	1	100	0	.	67	49	0	.	358	29	1748	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	1271	19	1286	19
	other broadleaves	261	30	152	44	70	64	135	37	259	33	16	55	35	57	167	41	41	48	14	63	168	78	288	38	123	37	1303	16	3032	11
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	9059	9	2613	18	1802	24	3230	17	4719	13	1983	23	2249	20	3241	18	989	27	594	37	2406	32	4567	13	2919	15	6081	9	46453	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6753	10	1127	24	1653	26	1985	21	3311	17	3080	17	4261	16	3742	15	2894	19	1468	29	974	28	3715	14	6863	11	2063	21	43888	4
	fir	4240	12	599	30	553	40	1508	23	1567	20	1019	28	1536	22	1717	22	420	33	168	49	319	39	346	37	232	38	377	31	14601	7
	pine	860	25	300	30	242	50	82	69	724	26	0	.	111	61	66	54	45	88	141	54	70	100	388	35	1003	24	154	78	4185	11
	larch	0	.	137	53	124	72	85	52	230	45	44	100	9	100	39	84	33	53	0	.	8	100	1762	17	1201	20	1163	22	4835	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	47	100	0	.	13	100	0	.	0	.	65	51	182	40	0	.	306	31
	other conifers	12	53	29	95	0	.	44	100	9	66	0	.	2	100	3	100	0	.	0	.	0	.	0	100	11	93	0	.	111	49
	beech	6771	8	3321	14	2188	19	1892	19	2177	15	680	33	1127	20	2210	19	1204	37	83	60	891	28	87	73	218	37	686	25	23535	5
	maple	1155	13	329	22	151	45	237	38	424	23	219	30	245	27	476	26	182	29	52	52	148	37	64	51	13	69	22	96	3716	8
	ash	779	24	319	26	322	34	542	53	846	20	140	50	223	41	504	29	190	40	23	60	170	39	7	100	2	100	91	62	4157	11
	oak	250	25	183	37	705	35	256	30	278	34	38	77	43	55	35	63	83	75	42	100	0	.	14	73	79	100	161	51	2167	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	14	100	0	.	436	31	453	30
	other broadleaves	439	22	165	31	199	36	164	46	254	37	36	34	119	62	156	40	164	41	41	61	61	57	203	37	5	55	364	29	2371	11
	indeterminable	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	.	0	0
	total	21259	6	6508	12	6138	15	6795	14	9820	11	5257	16	7723	13	8948	12	5230	16	2018	24	2640	20	6665	11	9808	10	5517	12	104326	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5505	12	741	31	3146	19	5204	14	5202	13	3430	24	9359	11	10314	11	3969	17	2939	19	2648	22	4275	15	11524	10	3845	15	72103	4
	fir	4884	13	494	42	1092	26	3981	15	2796	17	1035	32	6747	11	3348	15	1026	28	730	35	399	46	453	41	673	31	715	32	28373	5
	pine	200	44	120	61	143	59	316	29	1047	21	0	.	156	100	104	71	0	.	39	100	0	.	239	49	517	34	61	75	2943	13
	larch	35	75	12	100	233	48	22	100	167	47	0	.	122	88	166	52	147	52	0	.	252	53	3536	14	2315	16	1598	17	8604	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	174	39	364	33	0	.	538	25
	other conifers	3	75	5	100	114	75	57	65	174	84	0	.	45	91	7	60	0	.	0	.	0	.	0	.	0	.	0	.	405	44
	beech	3418	12	1961	19	1863	21	3699	14	5270	11	761	31	3832	16	3115	15	707	29	620	28	582	32	34	58	358	44	730	27	26952	5
	maple	415	22	249	31	141	43	180	38	519	23	52	53	331	27	395	23	143	36	100	32	100	41	30	52	35	70	16	89	2709	9
	ash	250	32	350	38	654	27	601	28	1105	19	109	61	108	35	494	29	40	70	181	63	51	56	36	73	87	69	0	.	4065	10
	oak	306	39	383	34	717	31	339	36	1032	21	23	71	26	74	117	45	0	.	32	77	0	.	2	100	36	79	118	83	3129	13
	sweet chestnut	0	.	0	.	56	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1043	30	1099	28
	other broadleaves	188	31	396	63	240	54	102	43	327	24	5	71	100	37	142	32	442	47	69	38	53	51	49	47	55	47	127	28	2297	16
	indeterminable	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	15205	9	4711	15	8399	13	14502	9	17639	8	5416	20	20826	8	18202	9	6473	14	4712	17	4086	19	8827	11	15965	8	8254	10	153217	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3468	13	37	73	158	53	673	34	1048	24	2693	18	5116	11	984	24	3954	15	502	34	978	26	2946	13	5656	10	2649	15	30859	5
	fir	3039	14	29	72	167	66	624	37	387	45	847	30	2407	16	406	37	307	41	151	54	205	55	264	35	333	31	247	42	9413	8
	pine	253	32	144	51	120	70	58	63	111	49	0	0	182	42	6	100	73	51	50	57	99	82	472	25	553	26	88	51	2211	12
	larch	6	88	0	.	0	0	25	100	13	71	0	.	0	.	0	.	37	60	0	.	18	76	1950	19	1516	15	1524	14	5089	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	13	100	0	.	475	21	774	20	6	74	1282	15
	other conifers	10	61	88	99	0	.	132	79	61	58	0	.	2	100	1	100	1	100	3	100	-10	100	0	.	26	60	0	.	315	45
	beech	1442	15	294	55	251	42	584	31	838	24	379	44	1354	21	229	35	274	40	37	63	173	41	177	37	234	39	1596	17	7861	8
	maple	340	21	25	62	37	59	34	69	241	31	118	32	242	30	103	44	277	27	7	100	112	54	63	44	88	40	97	54	1785	10
	ash	107	36	83	71	74	47	139	41	436	29	26	61	194	44	216	44	103	54	5	77	44	89	114	50	82	63	157	33	1781	13
	oak	116	56	230	78	24	71	74	42	183	41	0	.	43	75	0	.	7	71	23	82	7	100	25	53	54	68	168	35	953	23
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	83	12	100	1284	20	1355	19
	other broadleaves	163	29	126	49	145	55	145	60	251	34	30	40	200	28	38	48	129	30	31	59	62	59	179	33	230	41	847	16	2576	10
	indeterminable	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	0
	total	8944	9	1056	43	975	29	2488	21	3569	16	4092	17	9739	10	1984	24	5175	13	822	29	1689	22	6724	10	9559	8	8664	8	65481	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18900	5	2866	15	6071	13	10367	9	14345	7	11512	10	21255	6	17564	7	12231	9	5449	13	6210	14	14085	7	28819	5	9732	8	179406	2
	fir	14364	6	1308	22	2150	19	6834	10	5656	11	3707	15	11137	8	6467	10	1974	18	1187	24	1068	24	1338	20	1419	18	1686	18	60295	3
	pine	1869	15	683	21	587	30	588	21	2407	13	0	0	509	37	181	45	146	39	310	36	169	64	2021	14	2887	13	385	41	12739	6
	larch	84	45	167	44	431	34	219	31	656	24	44	100	136	80	219	43	274	32	13	100	415	37	8937	8	5936	9	5256	8	22785	4
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	47	100	16	100	27	60	13	100	0	.	950	16	1456	15	13	63	2522	11
	other conifers	105	53	219	48	181	54	398	38	338	46	0	.	49	84	23	39	30	63	8	55	-6	152	14	64	56	36	23	98	1437	19
	beech	15559	5	7266	9	4936	12	7648	9	9891	7	2052	18	6730	10	6532	10	2631	20	939	21	2068	17	722	21	1007	21	5769	11	73749	2
	maple	2347	8	736	16	508	22	663	19	1774	11	536	17	963	14	1338	13	878	15	262	23	606	20	268	20	169	28	348	24	11398	4
	ash	1706	14	1028	19	1541	18	1597	22	3464	10	466	29	741	25	1741	15	425	24	326	40	583	45	310	26	266	32	851	17	15045	5
	oak	1255	17	985	25	1785	20	885	18	1952	16	80	45	117	38	155	37	155	49	97	53	7	100	224	25	169	54	981	18	8849	7
	sweet chestnut	0	.	0	.	56	82	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	112	49	12	100	5001	10	5184	9
	other broadleaves	1163	13	927	30	768	23	696	20	1331	13	149	23	508	20	607	18	909	26	247	25	372	38	1213	14	780	18	4181	7	13850	5
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	57353	3	16185	7	19013	8	29895	6	41814	4	18547	8	42193	5	34843	5	19683	7	8850	11	11491	11	30193	5	42974	4	34225	3	407258	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. 1230998. Birmensdorf, Swiss Federal Research Institute WSL

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