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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NFI1
growing stock (stemwood)
development stage · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
		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	182	40	223	62	172	75	70	62	875	47	561	36	379	39	289	70	316	46	33	100	109	75	248	54	355	44	0	.	3812	16
	fir	144	74	10	100	8	100	84	67	92	81	353	76	458	49	0	.	51	71	0	.	22	100	100	75	17	100	0	.	1340	29
	pine	59	100	25	100	0	.	91	84	53	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	227	47
	larch	0	.	30	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	99	100	123	66	28	75	280	47
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	18	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	192	61	218	45	19	84	123	60	132	62	59	87	52	58	103	84	46	74	0	.	38	100	0	.	15	67	0	.	996	22
	maple	5	246	69	73	-2	100	5	100	14	100	2	100	26	56	59	77	19	72	11	100	0	.	2	100	0	.	0	.	210	35
	ash	109	89	69	84	9	100	73	100	70	60	9	100	6	100	19	75	23	100	0	.	93	100	22	88	0	.	0	.	501	34
	oak	65	73	16	66	0	.	25	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	26	100	137	42
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	71	0	.	0	.	43	71
	other broadleaves	35	66	165	55	0	.	18	62	13	100	8	82	19	62	45	78	1	100	0	.	37	100	27	66	3	100	12	65	384	29
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	790	42	825	37	207	68	489	46	1249	37	992	42	940	36	514	61	456	38	45	79	299	66	546	43	531	37	66	52	7950	13
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	61	72	55	100	105	80	99	49	139	46	255	56	167	49	359	43	588	41	82	95	125	94	158	60	640	40	57	68	2889	16
	fir	46	93	0	.	0	100	178	56	8	75	-4	100	54	100	43	70	102	78	0	.	5	100	19	100	49	71	12	100	512	30
	pine	0	.	45	67	0	.	13	100	131	47	0	.	0	.	0	.	0	.	0	.	0	.	6	79	0	.	0	.	195	36
	larch	2	100	0	100	0	.	3	100	28	100	0	.	0	.	0	.	0	.	0	.	0	.	145	46	107	80	24	72	309	37
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	6	100	0	.	1	100	0	.	0	.	0	.	0	.	2	100	0	.	0	.	4	100	0	.	0	.	13	59
	beech	64	97	74	100	0	.	97	50	101	58	3	100	0	.	43	71	19	100	7	100	0	.	0	.	0	.	18	70	428	30
	maple	0	.	44	100	0	.	20	62	2	100	0	.	8	60	26	82	1	100	0	.	5	100	2	100	32	89	2	100	142	41
	ash	26	100	36	100	0	.	14	71	14	84	1	100	5	100	30	100	0	.	0	.	0	.	7	100	0	.	0	.	132	43
	oak	0	.	64	100	28	100	0	.	1	100	0	.	4	100	0	.	0	.	0	.	0	.	4	58	0	.	0	.	102	69
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	64	19	64
	other broadleaves	5	73	10	100	0	.	4	72	2	92	0	.	12	49	9	100	16	53	0	.	0	.	15	53	27	69	27	50	127	24
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	204	49	334	46	132	71	428	36	425	35	255	56	249	44	509	39	727	37	90	87	135	91	361	34	856	33	160	34	4866	12
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1203	25	480	35	555	43	1023	30	1399	21	742	39	1167	30	819	38	467	41	529	54	409	57	975	27	874	30	241	63	10883	9
	fir	637	28	107	50	115	67	634	42	262	38	103	60	88	41	229	36	15	76	174	50	1	100	30	97	21	100	84	71	2501	15
	pine	103	51	138	47	137	56	64	73	120	43	0	.	66	100	0	.	0	.	32	100	0	.	618	31	143	42	0	.	1421	18
	larch	13	100	67	95	15	100	70	87	84	48	0	.	0	.	0	.	24	100	0	.	30	75	449	35	123	39	171	46	1045	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	55	38	87	0	.	82	50
	other conifers	1	100	39	79	0	.	51	70	22	66	0	.	0	.	3	100	0	.	2	100	-1	100	0	.	0	100	0	.	117	42
	beech	1443	21	902	27	329	47	303	44	716	28	206	55	271	50	266	41	127	66	125	44	81	64	174	65	19	100	818	27	5780	10
	maple	219	33	152	39	35	85	24	100	372	31	23	62	70	38	143	40	105	50	50	54	90	57	17	56	16	100	27	58	1342	14
	ash	261	39	193	42	238	40	86	55	460	27	175	55	131	63	277	37	47	79	55	74	8	100	11	71	126	62	70	36	2139	13
	oak	183	43	46	63	332	39	107	43	110	52	5	71	3	81	3	100	12	82	0	100	0	.	45	61	0	.	249	30	1094	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	73	0	.	1576	18	1616	17
	other broadleaves	99	42	52	56	297	46	129	43	171	37	48	61	77	47	192	47	30	52	75	59	24	53	212	32	253	41	1501	17	3161	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4162	15	2176	21	2054	23	2491	22	3716	16	1303	36	1873	23	1931	24	826	30	1042	33	644	45	2615	18	1614	22	4736	11	31181	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2735	19	1159	34	2059	30	1338	32	2435	22	2413	27	3454	20	2234	28	1198	38	1066	42	1296	36	1740	31	1676	35	716	44	25519	8
	fir	2435	21	365	53	381	58	499	38	855	30	447	48	754	37	1116	29	258	38	139	68	64	58	71	90	0	.	206	64	7589	11
	pine	430	36	490	35	69	85	98	52	397	42	0	.	93	78	34	100	12	72	111	66	0	.	87	47	678	41	0	.	2498	17
	larch	0	.	22	73	0	.	0	.	119	77	0	.	0	.	0	.	47	82	63	100	0	.	492	36	383	42	685	40	1810	21
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	70	73	71	0	.	128	50
	other conifers	3	72	0	.	0	.	97	100	27	62	0	.	0	.	3	100	0	.	2	100	1	100	0	.	0	.	0	.	132	74
	beech	3708	17	1090	25	1239	32	1016	28	1007	25	323	45	930	33	1578	30	304	46	118	75	448	51	85	72	241	55	825	27	12912	8
	maple	537	21	128	44	32	77	56	61	179	32	174	56	207	44	171	42	137	44	10	100	115	78	34	78	6	100	8	100	1793	13
	ash	308	50	91	68	186	49	112	50	424	34	0	.	208	46	308	41	139	58	3	100	13	76	46	75	0	.	42	66	1880	16
	oak	237	52	287	42	121	44	58	58	327	38	6	100	16	100	0	.	5	80	0	.	0	.	0	.	0	.	130	61	1186	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	592	31	592	31
	other broadleaves	265	32	59	44	96	60	14	71	172	49	29	57	58	54	78	62	302	47	0	.	9	100	65	67	15	100	345	36	1507	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	10657	11	3691	21	4182	22	3287	21	5941	16	3392	24	5720	17	5522	20	2402	26	1512	36	1947	31	2674	24	3071	25	3548	18	57546	5
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5164	15	1010	46	3242	26	7535	15	7269	14	3511	27	6927	15	5637	17	4515	22	1978	35	1219	36	2117	23	8111	15	2134	26	60367	5
	fir	3436	17	215	59	705	44	3932	18	2327	20	533	60	3104	18	1727	26	428	51	739	46	600	42	106	82	327	44	293	50	18472	8
	pine	534	33	156	55	683	50	430	40	1511	24	0	.	277	91	90	88	64	100	0	.	0	.	250	64	665	34	97	100	4755	14
	larch	37	71	16	100	116	78	51	100	333	40	0	.	0	.	52	100	174	60	0	.	191	60	916	29	1346	24	289	43	3521	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	100	226	43	0	.	293	40
	other conifers	2	100	0	.	149	100	4	72	137	100	0	.	42	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	336	62
	beech	4142	13	1711	24	618	37	3298	19	3634	16	294	59	3191	22	1090	31	679	40	355	41	686	34	15	100	205	39	304	53	20220	7
	maple	356	27	254	49	18	100	88	51	230	36	20	67	256	38	310	30	114	56	105	45	127	54	2	100	25	63	4	100	1908	13
	ash	224	37	104	54	170	58	271	48	745	24	29	100	189	36	239	55	0	.	116	70	55	108	12	93	42	100	7	100	2202	15
	oak	241	44	143	69	412	52	299	38	625	27	22	100	0	.	0	.	0	.	11	100	0	.	0	.	34	100	46	98	1833	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	294	56	308	54
	other broadleaves	325	34	167	62	62	70	102	39	259	33	16	64	81	57	75	48	104	66	71	74	145	46	4	100	28	53	64	56	1503	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	14461	10	3775	22	6175	19	16008	11	17069	10	4425	25	14068	13	9219	15	6078	19	3375	28	3022	26	3488	20	11022	13	3531	20	115717	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3189	19	484	52	1307	36	3431	23	3836	23	1153	42	3031	25	5526	21	2290	32	756	45	2271	31	1648	31	6251	17	2718	23	37891	7
	fir	2236	22	130	55	299	65	1276	29	1280	31	218	71	2693	23	1366	26	410	55	142	82	9	100	63	100	404	53	278	70	10804	10
	pine	128	60	0	.	110	71	353	56	415	44	0	.	0	.	39	82	0	.	0	.	0	.	44	72	401	46	95	65	1586	22
	larch	34	100	0	.	63	100	47	100	34	73	0	.	32	100	80	71	25	100	0	.	0	.	1272	30	1388	31	949	32	3923	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	100	272	54	0	.	319	49
	other conifers	1	100	0	.	0	.	137	84	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	139	83
	beech	2110	22	1516	32	699	40	2372	24	1664	24	35	100	1656	31	1146	35	264	56	91	63	146	74	68	100	701	52	188	66	12657	10
	maple	300	36	115	55	20	100	62	76	170	37	0	.	19	59	153	41	36	100	2	100	101	63	0	.	31	95	0	.	1009	18
	ash	57	58	82	50	49	61	215	61	163	56	0	.	0	.	70	62	16	100	0	.	31	76	0	.	0	.	0	.	683	26
	oak	0	.	40	67	152	72	116	48	466	38	0	.	0	.	54	92	0	.	18	100	0	.	6	100	34	100	0	.	887	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	683	48	683	48
	other broadleaves	35	48	73	80	133	73	172	57	194	45	3	100	21	70	44	69	67	87	19	93	7	100	133	88	28	75	38	57	969	23
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8091	14	2441	28	2832	29	8180	17	8223	16	1409	45	7453	18	8480	18	3108	29	1027	43	2564	30	3283	21	9510	14	4949	18	71549	5
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4647	14	188	55	204	51	655	32	521	25	1345	30	4267	15	2530	23	2256	21	502	45	640	48	4553	15	6076	13	1868	24	30254	6
	fir	2087	17	30	100	69	100	994	34	318	39	644	38	3067	20	482	31	377	38	68	77	100	79	546	40	171	46	485	51	9437	10
	pine	223	33	16	100	43	74	63	100	99	52	0	.	128	91	0	.	86	78	77	78	0	.	764	26	793	29	51	96	2343	15
	larch	0	.	0	.	0	.	0	.	58	100	0	.	34	100	0	.	95	66	0	.	0	.	2310	16	1425	20	1073	20	4994	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	350	27	585	30	0	.	943	21
	other conifers	4	78	1	100	35	100	0	.	17	90	0	.	0	.	5	100	0	.	0	.	0	.	10	100	2	100	0	.	74	54
	beech	1928	17	454	47	295	50	468	41	813	24	240	49	701	26	825	34	565	35	49	68	208	64	175	42	133	82	945	24	7799	9
	maple	235	27	59	60	0	.	62	61	411	37	91	45	78	38	149	48	135	40	0	.	102	44	37	70	19	78	31	58	1409	15
	ash	324	39	108	71	245	58	71	56	622	31	61	49	126	53	154	52	38	71	0	.	41	53	39	88	0	.	21	84	1852	16
	oak	195	49	18	100	0	.	95	52	319	32	-1	100	0	.	36	100	0	.	22	100	0	.	8	53	0	.	194	57	886	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	628	36	628	36
	other broadleaves	134	35	47	47	61	95	54	45	570	30	31	40	85	51	144	52	36	43	15	74	48	56	72	40	14	54	348	34	1659	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	9778	10	920	37	952	36	2462	24	3749	18	2412	25	8485	13	4323	19	3597	18	732	40	1140	36	8866	11	9219	11	5644	13	62279	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17182	7	3599	19	7643	15	14151	10	16474	9	9979	13	19393	8	17394	10	11629	12	4946	19	6069	17	11439	10	23983	7	7734	12	171616	2
	fir	11020	8	858	29	1576	28	7597	12	5142	13	2294	24	10218	10	4962	13	1640	22	1262	30	801	33	936	28	989	27	1358	27	50654	4
	pine	1478	18	868	23	1041	35	1112	26	2725	16	0	.	564	52	163	56	163	57	219	45	0	.	1769	18	2679	17	243	51	13024	7
	larch	86	52	134	54	194	57	170	54	656	27	0	.	66	71	131	58	365	36	63	100	221	53	5684	11	4895	13	3218	14	15882	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	563	23	1212	21	0	.	1783	16
	other conifers	11	41	46	68	185	83	289	53	204	68	0	.	42	100	12	51	2	100	4	72	0	634	15	77	3	84	0	.	813	32
	beech	13587	7	5965	12	3201	18	7676	12	8067	10	1160	24	6800	13	5050	14	2005	20	744	25	1606	23	518	31	1314	31	3098	13	60791	3
	maple	1653	12	821	21	103	45	317	27	1377	16	311	34	664	21	1010	16	546	22	178	32	540	27	93	41	128	38	71	35	7814	6
	ash	1309	19	684	23	897	24	842	25	2497	13	275	38	664	23	1097	20	263	36	175	52	242	48	136	40	168	53	140	30	9388	7
	oak	921	23	614	28	1045	26	700	21	1848	16	32	72	23	72	93	66	16	63	51	60	0	.	69	42	68	71	645	24	6125	9
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	51	14	100	3791	14	3888	13
	other broadleaves	898	17	574	27	649	29	492	25	1382	17	136	28	353	23	587	23	555	30	180	40	271	30	528	27	370	30	2335	12	9311	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	48144	4	14162	10	16534	10	33345	7	40372	5	14188	12	38787	6	30499	7	17193	10	7823	16	9751	14	21833	7	35822	6	22635	5	351089	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. 1230918. Birmensdorf, Swiss Federal Research Institute WSL

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