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 (“±”).

top

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).

top

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).

top

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.

top

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.

top

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

top

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.

top

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.

top

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.

top

NFI5
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 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	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	0	0	0	.	0	.	21	100	94	90	17	100	13	77	68	100	207	80	11	100	0	.	0	0	472	42	68	71	972	30
	fir	0	.	0	.	0	.	0	.	13	100	97	100	0	.	0	.	34	100	0	.	0	.	10	100	0	0	0	.	153	68
	pine	55	77	0	.	16	100	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	8	100	40	80	0	.	135	43
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	100	79	58	45	100	147	46
	Arolla pine	0	.	0	.	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	59	97	0	.	0	.	51	78	3	100	39	100	0	.	19	100	0	.	0	.	2	100	0	.	0	.	0	.	174	47
	maple	0	.	0	.	0	.	4	100	0	.	17	100	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	38	64
	ash	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100
	oak	0	.	0	.	52	100	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	67	80
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	0	6	100
	other broadleaves	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	34	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	114	86	0	.	101	63	87	85	143	81	170	100	13	77	104	74	246	69	11	100	2	100	41	65	590	38	113	58	1736	22
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	348	43	22	100	50	73	287	57	136	57	310	57	434	39	178	41	412	44	37	100	58	59	78	59	1288	35	395	54	4035	16
	fir	156	43	12	59	10	100	75	67	94	81	76	96	99	55	124	55	34	100	0	.	13	100	0	0	0	.	0	.	695	24
	pine	20	100	0	.	0	.	83	61	15	100	0	.	0	.	8	100	0	0	0	.	0	.	0	.	74	74	0	.	201	40
	larch	0	.	1	100	44	70	0	.	0	.	0	.	0	.	0	.	127	78	0	.	1	100	38	95	155	58	128	78	494	35
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	11	91	7	100	0	.	2	100	5	100	0	.	0	.	0	.	0	.	0	.	0	.	25	54
	beech	57	47	157	69	-17	133	193	60	283	48	39	100	509	67	156	42	21	91	25	100	0	0	0	.	19	100	17	100	1460	28
	maple	15	64	53	81	0	.	0	.	63	59	13	100	28	64	27	67	33	88	14	100	19	100	0	.	17	88	0	.	281	27
	ash	0	.	44	97	12	100	0	.	0	.	35	100	0	0	32	88	0	.	0	.	6	100	0	100	0	.	0	.	129	49
	oak	23	100	0	.	100	78	31	71	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	164	51
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	15	77	4	100	70	100	6	79	41	71	3	100	43	66	12	100	32	85	0	.	4	100	0	0	12	100	53	97	296	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	636	31	293	47	269	51	687	43	640	34	477	45	1115	36	552	31	659	37	77	62	101	59	116	51	1565	32	601	51	7789	12
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	553	31	282	42	689	43	787	28	2036	22	438	42	933	30	649	33	758	35	567	52	185	83	468	35	1060	28	338	47	9743	9
	fir	839	36	91	68	246	50	783	31	244	38	71	57	731	36	379	45	45	99	62	81	146	74	53	91	47	81	95	67	3832	14
	pine	74	65	0	.	2	100	25	93	130	54	0	.	50	100	53	100	0	.	0	.	0	.	170	47	262	32	0	.	766	21
	larch	0	.	6	100	170	60	5	100	165	40	25	81	18	100	0	.	88	60	0	.	0	.	508	38	125	44	505	37	1614	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	79	24	94	0	.	61	60
	other conifers	6	100	28	100	6	100	38	83	15	83	0	.	81	100	0	.	10	100	0	.	0	.	13	60	25	50	0	.	221	43
	beech	657	29	382	26	272	42	446	27	892	20	32	95	271	53	171	47	246	64	25	71	215	70	122	79	3	100	840	28	4574	11
	maple	309	28	170	35	110	51	102	46	427	36	26	85	107	35	171	43	247	33	46	70	66	64	106	51	0	.	90	47	1978	12
	ash	157	56	98	36	135	43	110	50	222	32	61	70	46	56	111	66	85	55	62	68	0	.	95	54	14	100	197	43	1393	15
	oak	91	55	66	85	62	75	44	61	62	47	0	.	5	100	0	.	23	100	0	.	0	.	73	49	0	.	186	40	611	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	100	0	.	931	29	956	28
	other broadleaves	59	36	92	47	347	57	151	40	186	31	43	56	89	55	136	53	84	29	18	66	9	52	378	26	407	38	1120	24	3120	13
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0
	total	2744	19	1214	23	2039	25	2491	18	4379	14	696	37	2331	23	1670	28	1587	24	780	42	621	50	2049	19	1966	20	4302	13	28869	6
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1185	36	404	49	545	37	2125	24	2037	23	937	44	1426	34	1047	39	647	39	134	51	648	43	1461	35	2341	26	300	48	15239	9
	fir	804	28	31	100	120	52	642	38	546	32	195	72	481	49	178	51	181	57	308	62	52	59	8	100	155	71	201	70	3902	14
	pine	226	52	141	57	19	82	45	59	155	46	0	.	45	92	0	.	0	.	0	.	0	.	733	36	407	52	78	86	1850	21
	larch	12	100	24	83	57	100	22	72	57	64	0	.	0	.	0	.	52	100	0	.	51	100	688	27	412	38	400	45	1777	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	130	77	209	52	8	100	347	42
	other conifers	7	100	109	67	0	.	121	52	117	58	0	.	0	.	5	100	0	.	0	.	3	83	1	100	2	100	0	.	365	32
	beech	1924	22	553	40	190	45	675	29	946	33	180	73	340	52	580	57	91	70	345	52	159	68	66	61	69	50	2061	24	8179	11
	maple	171	50	64	66	40	71	217	37	505	28	148	52	234	56	242	49	209	47	63	70	223	59	0	.	80	56	98	66	2293	14
	ash	193	42	5	100	87	56	178	39	262	50	151	60	326	83	173	51	97	79	88	67	54	78	46	52	11	78	379	60	2053	21
	oak	236	63	147	88	167	60	96	55	41	59	39	100	6	100	0	.	18	74	0	0	0	.	29	60	0	.	399	38	1177	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	78	5	100	1028	27	1075	26
	other broadleaves	154	55	26	61	105	55	245	42	185	32	16	54	45	53	18	100	52	71	41	63	78	62	176	47	197	61	1192	24	2528	15
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	0
	total	4912	17	1506	29	1331	29	4366	19	4850	16	1666	32	2903	24	2243	31	1347	28	980	40	1267	35	3380	21	3887	20	6146	13	40784	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3473	17	761	32	272	55	1557	33	1877	22	1540	37	2148	30	4102	23	1921	29	1205	52	647	43	2245	31	5543	18	1572	33	28864	8
	fir	2646	21	361	55	583	51	1133	35	747	28	702	46	568	33	841	26	253	57	386	53	223	67	271	55	98	89	286	53	9097	11
	pine	406	55	273	41	134	58	137	62	421	35	0	.	100	100	0	.	0	.	58	86	0	.	327	43	897	36	31	100	2785	17
	larch	0	.	36	80	34	100	20	100	70	62	0	.	0	.	0	.	0	0	0	.	0	.	771	37	850	30	1239	34	3020	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	222	48	0	.	222	48
	other conifers	17	84	0	.	56	100	137	100	93	79	0	.	0	.	7	79	0	.	0	.	0	.	0	.	0	.	0	.	310	54
	beech	4836	14	2007	23	991	35	1264	27	1668	19	411	48	1323	35	1548	25	477	51	244	61	451	43	197	70	199	63	910	35	16526	8
	maple	794	21	313	37	45	55	92	46	365	29	70	67	98	39	400	34	156	45	64	70	123	48	5	100	18	72	21	62	2563	11
	ash	491	36	329	44	717	42	270	50	673	31	161	100	20	110	463	41	170	57	120	96	75	72	62	92	0	.	116	89	3669	15
	oak	267	43	195	76	483	46	112	63	334	43	12	100	58	100	0	.	0	.	0	.	0	.	26	59	0	.	149	54	1635	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	637	44	637	44
	other broadleaves	244	44	237	48	59	58	195	49	319	39	15	75	50	41	85	44	33	54	22	71	36	80	25	45	13	60	672	42	2003	18
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	0
	total	13173	11	4512	18	3375	25	4917	20	6567	15	2911	27	4364	22	7445	17	3009	25	2099	38	1556	32	3928	23	7839	15	5634	17	71329	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4799	16	950	38	1915	34	2534	23	4385	18	4908	19	7037	14	8293	16	4399	23	2551	27	2528	30	5966	17	10770	14	5172	18	66206	5
	fir	6208	15	462	43	1047	34	4276	18	3209	20	1127	43	5954	15	3610	20	1571	32	1037	49	463	55	730	44	780	36	1089	39	31562	7
	pine	251	43	23	100	127	67	381	40	469	38	0	.	229	100	50	85	0	.	116	100	0	.	26	77	475	36	0	.	2147	19
	larch	63	74	15	100	0	.	40	100	246	48	0	.	0	.	155	54	112	71	0	.	122	72	3428	21	3036	20	1481	25	8698	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	249	53	324	45	0	.	574	34
	other conifers	6	100	258	98	0	.	461	62	339	99	0	.	0	.	20	60	0	.	0	.	0	.	0	.	0	.	0	.	1084	47
	beech	4662	13	2187	22	2414	25	3739	16	4971	15	579	47	3064	20	2674	22	1288	31	580	41	676	41	1	100	654	43	1078	33	28567	6
	maple	669	20	127	56	92	45	201	34	642	33	65	52	198	39	676	27	158	41	134	48	131	62	76	56	50	67	0	.	3218	11
	ash	237	37	199	48	410	50	612	40	1352	26	61	100	241	57	578	38	78	53	215	63	62	59	11	64	45	95	0	.	4101	14
	oak	314	46	228	49	176	80	261	30	1177	38	11	100	0	0	0	.	0	.	19	100	0	.	33	100	64	100	34	60	2316	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1404	33	1404	33
	other broadleaves	123	47	150	58	136	68	214	36	308	34	45	54	79	56	128	47	361	80	63	87	55	70	64	78	37	69	205	41	1969	19
	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	17331	10	4599	21	6317	19	12718	12	17098	10	6796	18	16801	11	16183	12	7968	18	4715	24	4036	24	10584	14	16235	11	10463	12	151846	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5012	13	60	61	546	45	497	54	513	38	1731	26	4891	15	1667	24	3798	21	1329	30	1623	33	4253	16	6346	14	3761	32	36029	6
	fir	4381	15	46	82	281	61	975	32	289	47	926	35	3702	17	1204	33	617	40	192	60	113	72	550	44	316	39	334	42	13926	8
	pine	362	32	115	54	159	100	0	.	145	50	0	.	296	67	0	.	54	61	107	68	0	.	627	29	454	36	29	70	2349	17
	larch	0	.	0	.	21	100	50	100	72	85	0	.	1	100	37	100	16	100	0	.	0	.	3093	22	1474	20	1598	21	6363	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	457	29	720	29	0	.	1190	21
	other conifers	35	47	4	100	0	.	23	100	9	81	0	.	9	90	0	.	0	.	7	100	-19	100	0	.	9	73	82	98	159	56
	beech	2513	15	602	38	268	38	772	35	1055	32	530	40	1925	21	1246	27	413	45	275	69	509	39	500	43	346	48	1489	23	12441	8
	maple	876	23	142	48	82	59	97	46	180	37	320	34	616	25	256	48	120	42	1	100	208	52	188	50	178	43	126	53	3390	11
	ash	328	36	186	63	180	57	387	72	338	32	170	80	214	46	245	58	-20	161	0	.	159	62	128	68	149	62	141	33	2604	17
	oak	288	48	101	47	35	73	290	49	259	41	1	100	40	100	59	79	8	100	86	72	0	.	71	100	85	100	171	51	1494	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	18	100	1323	26	1371	25
	other broadleaves	284	28	82	65	170	63	138	63	534	35	43	49	139	29	193	52	223	56	48	62	105	60	263	31	140	38	1058	27	3420	13
	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	14078	9	1338	30	1742	32	3230	24	3394	19	3721	24	11834	11	4906	20	5242	18	2045	30	2697	26	10160	12	10237	11	10113	16	84736	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15371	7	2480	19	4018	20	7809	12	11079	10	9882	13	16882	8	16003	10	12141	12	5836	18	5689	18	14470	10	27821	7	11606	13	161087	3
	fir	15034	8	1003	29	2287	22	7884	12	5142	14	3193	22	11535	10	6336	13	2735	22	1984	30	1010	32	1621	26	1397	24	2006	24	63168	4
	pine	1394	22	552	27	457	43	672	28	1352	19	0	.	721	45	111	61	54	61	281	52	0	.	1891	19	2609	18	138	56	10232	8
	larch	75	64	81	47	326	39	137	50	611	26	25	81	19	94	192	48	396	38	0	.	174	58	8549	12	6131	12	5398	12	22114	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	874	24	1499	20	8	100	2393	15
	other conifers	71	34	399	66	62	91	791	41	580	61	0	.	91	89	36	41	10	100	7	100	-16	122	13	58	35	40	82	98	2163	26
	beech	14708	6	5889	12	4118	17	7138	11	9817	9	1810	23	7432	12	6395	13	2536	21	1494	25	2012	21	886	31	1290	27	6395	12	71921	3
	maple	2833	10	868	20	369	25	713	18	2181	15	659	22	1280	17	1788	16	923	18	322	30	770	26	376	31	342	28	336	30	13760	5
	ash	1407	18	862	25	1541	25	1568	25	2847	15	640	38	847	38	1602	21	410	34	485	39	356	35	342	35	219	47	832	32	13959	7
	oak	1219	23	737	32	1073	28	835	22	1889	25	62	67	108	65	69	69	49	57	105	61	0	.	231	38	149	71	939	21	7465	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	97	52	23	82	5330	13	5456	12
	other broadleaves	878	19	591	27	920	28	950	20	1573	16	166	26	445	19	571	25	785	41	192	36	286	32	906	17	805	26	4300	12	13369	6
	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	52988	4	13463	10	15173	11	28496	7	37072	6	16438	11	39361	6	33103	7	20058	9	10707	15	10280	13	30257	7	42320	6	37372	5	387089	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. 1231077. Birmensdorf, Swiss Federal Research Institute WSL

back