Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-35calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

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

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest and the total forest (pairs of values in ), but they do in the private forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest and the total forest of the Plateau has decreased. For the private forest the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest and total forest.

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
number of stems
development stage · main tree species
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	41	100	0	.	0	.	20	.	20	.	270	68	32	71	245	93	122	74	0	.	0	0	25	100	239	42	139	72	1152	30
	fir	0	.	0	.	0	.	0	.	0	.	71	90	0	0	22	100	0	.	0	.	0	.	0	.	0	0	0	.	93	73
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0
	larch	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	92	85	35	53	15	100	151	55
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	100	0	0	0	.	42	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	45	86	0	.	39	70	0	.	8	.	30	100	22	100	17	100	0	.	0	.	0	.	0	.	7	100	0	.	169	38
	maple	27	100	0	.	0	.	0	.	0	.	28	100	9	100	17	100	0	.	0	.	0	.	0	.	0	.	0	.	81	53
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	17	100
	oak	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	19	100	0	.	0	.	20	.	0	.	6	100	0	.	39	71	0	.	0	.	0	.	0	.	0	.	0	.	84	47
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	total	131	58	0	.	55	54	39	.	27	.	405	58	63	61	358	80	122	74	0	.	0	0	160	77	282	40	154	71	1797	25
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	105	53	81	81	26	78	308	48	404	32	413	37	747	31	82	50	345	52	119	66	251	57	312	33	834	30	157	57	4185	12
	fir	192	58	0	.	104	53	145	48	120	58	101	81	171	46	167	53	39	73	37	100	38	84	55	100	35	100	19	100	1223	19
	pine	23	100	0	.	0	.	7	100	59	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	167	52	0	.	255	42
	larch	0	.	0	.	0	.	0	.	55	54	0	.	7	100	0	.	37	83	0	.	0	0	259	41	92	50	62	63	510	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	0	.	36	100
	other conifers	0	.	0	.	0	.	0	.	20	100	0	.	15	100	0	.	0	.	0	.	0	.	69	100	72	79	0	.	176	53
	beech	202	47	160	47	71	47	436	30	561	29	9	100	119	63	165	47	17	100	14	100	0	.	35	100	53	100	61	63	1904	14
	maple	54	74	34	71	74	71	72	71	168	40	91	80	104	68	36	72	32	100	10	100	182	71	48	54	17	100	26	76	948	22
	ash	34	70	74	62	33	72	17	100	147	42	81	62	67	75	9	100	12	100	0	.	35	100	55	100	0	.	35	100	600	22
	oak	7	100	0	.	0	.	50	74	40	83	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	7	100	104	49
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	11	100
	other broadleaves	64	52	53	58	79	71	240	46	198	39	152	65	116	66	24	100	116	62	45	100	176	45	159	53	279	65	434	46	2136	17
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	680	33	402	40	389	38	1274	25	1769	20	847	34	1348	25	484	37	597	37	225	44	683	38	992	31	1586	24	812	33	12089	8
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2785	32	1360	39	2180	36	3745	26	5710	20	1843	42	2818	36	2433	31	1787	30	1317	36	567	52	2438	29	6014	21	1787	32	36786	8
	fir	1245	36	34	100	272	49	932	39	1342	40	61	77	1262	39	765	49	217	72	69	100	14	100	130	70	181	87	121	49	6646	16
	pine	202	72	102	100	17	100	365	77	132	44	0	.	139	100	17	100	0	.	127	100	0	.	2000	41	2135	31	25	100	5261	21
	larch	17	100	17	100	501	71	33	100	93	61	88	100	87	100	0	.	315	57	0	.	0	.	1634	29	1039	35	2352	25	6176	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	178	91	458	57	0	.	636	48
	other conifers	85	72	42	71	20	100	171	46	171	53	0	.	0	.	0	.	232	86	0	.	0	.	52	74	483	44	16	100	1271	26
	beech	2095	31	782	30	634	37	1050	28	1497	27	146	61	636	52	313	35	28	100	7	100	585	64	588	56	35	100	2876	32	11272	12
	maple	1312	29	413	38	86	83	360	54	1630	30	185	60	294	43	489	37	786	39	181	70	185	70	674	32	265	61	999	35	7859	12
	ash	517	37	635	48	155	62	372	34	994	30	182	77	144	55	766	51	186	78	335	55	81	60	766	36	180	52	1012	30	6325	13
	oak	1009	53	18	100	191	88	24	77	137	50	0	.	0	.	0	.	123	100	0	.	0	.	440	54	0	.	1125	42	3067	26
	sweet chestnut	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	71	0	.	6184	22	6209	22
	other broadleaves	498	37	404	36	359	45	812	41	909	30	485	60	467	43	532	52	620	40	946	60	401	69	2828	22	3797	28	9496	17	22554	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	9764	16	3807	25	4415	25	7863	18	12616	14	2990	32	5847	24	5314	23	4295	22	2983	31	1834	38	11752	15	14587	14	25992	10	114061	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2869	27	573	45	602	49	1617	29	2063	27	2370	36	2397	30	2645	28	1573	39	217	50	1447	43	2568	41	4698	24	657	42	26297	9
	fir	1213	24	524	59	194	60	825	41	245	47	793	55	265	63	688	38	48	64	525	57	101	66	0	.	129	71	302	70	5853	15
	pine	555	47	222	58	130	71	17	100	100	56	0	.	24	100	7	100	0	.	107	100	0	.	1359	36	1110	42	0	.	3632	21
	larch	7	100	31	71	0	.	24	77	92	75	0	.	0	.	0	.	0	.	0	.	120	76	812	33	1006	33	1060	34	3153	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	130	48	754	47	0	.	883	40
	other conifers	0	.	123	76	90	100	60	66	224	86	0	.	0	.	42	71	0	.	0	.	18	100	17	100	17	100	0	.	591	40
	beech	3815	18	1489	32	199	60	1205	26	1059	29	566	65	583	58	1411	35	709	60	790	45	654	52	404	61	129	59	4138	26	17150	10
	maple	542	25	148	46	86	72	245	40	735	31	369	45	565	66	656	46	584	49	229	53	306	52	45	100	71	100	210	49	4791	14
	ash	403	44	242	43	439	51	297	35	716	34	226	64	380	69	542	43	68	100	198	65	128	82	54	75	184	74	650	61	4525	16
	oak	591	49	250	57	688	50	201	49	55	66	81	62	21	100	9	100	61	71	18	100	0	.	155	49	0	.	416	32	2548	20
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	55	100	0	.	1709	35	1771	34
	other broadleaves	736	37	162	44	92	56	372	39	649	33	114	50	48	84	231	47	116	60	141	57	71	78	695	42	451	39	2701	24	6580	13
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0
	total	10731	15	3765	23	2520	29	4863	20	5945	19	4519	27	4285	24	6231	21	3159	32	2226	39	2846	36	6296	22	8548	18	11843	14	77775	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3279	16	488	33	359	48	1144	31	2139	19	2209	30	1624	28	3235	20	1876	32	1856	37	871	42	2544	24	7390	15	2300	26	31315	7
	fir	3344	18	125	54	517	60	531	31	816	29	877	42	957	30	855	25	353	45	524	44	138	59	402	45	85	61	144	51	9670	10
	pine	312	38	179	41	67	65	106	58	398	27	0	.	91	66	7	100	0	.	222	64	0	.	331	48	455	39	14	100	2183	16
	larch	0	.	14	70	7	100	34	82	135	52	0	.	0	.	29	61	6	100	0	.	158	96	862	39	855	26	906	38	3006	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	372	36	0	.	372	36
	other conifers	139	88	0	.	0	.	34	100	89	52	0	.	0	.	54	57	0	.	0	.	0	.	0	.	0	.	0	.	316	44
	beech	4873	15	2374	22	1317	29	1382	26	2470	15	549	38	1834	30	2072	24	711	34	250	63	597	37	340	81	319	51	1154	38	20242	7
	maple	721	18	289	34	22	75	61	53	595	22	252	50	180	40	585	30	320	36	57	60	160	49	55	74	33	71	18	100	3347	10
	ash	452	36	160	43	443	45	297	65	533	22	107	71	203	49	631	34	255	59	71	62	161	62	0	0	18	100	70	71	3402	14
	oak	194	37	136	54	172	62	136	38	255	29	0	.	38	100	0	0	0	.	8	100	0	.	18	100	35	100	76	54	1068	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	727	45	734	45
	other broadleaves	401	28	251	41	189	75	183	47	315	28	117	68	291	39	248	46	305	43	84	59	135	56	256	55	54	74	476	41	3305	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
	total	13715	11	4016	20	3093	27	3910	19	7745	12	4111	29	5219	20	7717	15	3832	24	3072	29	2220	28	4809	21	9616	13	5885	17	78960	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2242	17	387	40	910	25	1521	20	1791	17	2024	24	3822	15	3976	16	1715	23	1000	31	1012	36	2932	20	5173	15	1995	22	30499	5
	fir	3043	16	541	35	437	32	2140	18	935	20	353	41	3153	16	918	25	616	32	347	49	225	49	131	71	231	44	245	81	13315	7
	pine	70	42	15	71	77	65	130	40	127	35	0	.	117	100	15	72	0	.	0	.	0	.	187	87	141	44	0	.	879	26
	larch	14	70	7	100	20	73	7	100	7	100	0	.	0	.	23	73	72	62	0	.	25	78	1426	23	1125	24	576	31	3301	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	192	49	169	53	0	.	361	36
	other conifers	35	100	17	100	23	100	63	62	71	80	0	.	7	100	36	71	0	.	0	.	0	.	0	.	0	.	0	.	253	34
	beech	2687	13	1348	25	1207	26	2243	16	2550	15	362	47	2689	19	1783	22	638	36	491	40	477	37	37	100	604	36	734	46	17851	6
	maple	643	22	261	35	146	65	187	36	379	30	89	41	302	31	342	36	252	38	98	53	166	51	261	61	83	64	0	.	3208	11
	ash	284	40	142	49	513	39	282	31	679	26	108	87	212	47	121	46	125	64	189	50	34	68	49	73	12	100	0	.	2750	13
	oak	124	59	45	62	258	52	87	34	275	33	0	.	0	.	17	71	0	.	6	100	0	.	0	0	0	.	8	100	819	22
	sweet chestnut	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	235	48	252	45
	other broadleaves	412	37	89	66	195	72	246	35	702	40	76	66	172	56	236	43	361	44	46	72	71	84	95	60	73	60	448	38	3220	14
	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	9553	11	2851	21	3802	20	6906	12	7517	11	3012	22	10473	12	7466	13	3778	18	2177	27	2009	28	5310	16	7612	12	4241	17	76708	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5333	13	37	71	725	46	685	36	743	29	1458	27	8081	15	1914	29	5627	18	1906	31	1871	28	7662	14	10665	13	4956	16	51663	5
	fir	5591	15	38	84	194	61	1409	28	293	36	693	45	3628	17	726	33	590	42	572	41	148	60	707	39	217	43	659	36	15465	8
	pine	175	34	144	59	62	90	0	.	58	53	0	.	475	100	0	.	180	49	80	64	285	87	1384	29	578	30	210	73	3631	20
	larch	0	.	0	.	7	100	21	100	31	81	0	.	0	.	153	100	71	74	0	.	35	100	3683	17	1489	19	3322	18	8811	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1191	28	1700	26	49	100	2949	19
	other conifers	142	44	108	75	0	.	61	75	50	100	0	.	35	70	0	.	0	.	87	100	46	100	18	100	139	68	0	.	685	27
	beech	5265	14	505	39	566	39	779	38	972	23	548	67	3353	19	1292	37	842	38	429	47	653	45	871	46	631	50	3433	21	20140	8
	maple	1483	21	205	55	187	56	149	57	294	46	292	52	1042	30	494	41	461	42	76	85	316	52	353	47	461	54	145	53	5957	11
	ash	327	33	302	51	299	39	153	45	355	37	83	82	412	39	474	47	389	54	123	73	289	55	481	68	524	64	444	37	4655	15
	oak	252	56	106	65	61	67	99	66	159	38	8	100	84	63	42	70	9	100	56	78	17	100	460	50	55	100	642	34	2050	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	247	97	28	100	3513	22	3788	21
	other broadleaves	1150	23	101	55	344	44	122	68	475	45	219	46	546	26	909	48	1118	39	251	49	355	55	891	26	605	34	3028	17	10113	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
	total	19718	9	1547	31	2444	27	3479	25	3429	19	3301	24	17655	11	6004	22	9296	15	3579	26	4017	24	17946	11	17091	11	20401	9	129907	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16654	8	2927	21	4803	19	9040	13	12869	10	10586	14	19522	9	14531	10	13044	11	6415	16	6020	17	18481	10	35015	7	11990	10	181897	3
	fir	14628	8	1262	29	1718	23	5981	12	3751	17	2950	22	9437	10	4141	14	1863	21	2074	23	664	27	1426	25	878	27	1491	25	52265	4
	pine	1337	24	662	30	353	36	625	47	875	18	0	.	846	60	46	48	180	49	536	42	285	87	5261	20	4586	19	249	63	15841	10
	larch	38	55	70	44	543	66	119	44	413	29	88	100	94	93	204	76	501	39	0	.	338	54	8767	11	5641	12	8293	11	25108	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1734	22	3488	18	49	100	5280	14
	other conifers	401	38	290	44	133	71	390	29	624	37	0	.	57	52	131	37	232	86	87	100	65	77	156	53	712	34	16	100	3293	15
	beech	18982	7	6657	12	4033	15	7095	10	9117	8	2211	26	9237	11	7054	13	2945	21	1981	24	2966	22	2274	28	1779	24	12396	13	88727	3
	maple	4782	11	1351	18	600	29	1074	23	3800	15	1305	22	2497	21	2620	17	2434	20	651	30	1315	24	1437	22	929	33	1398	26	26191	5
	ash	2016	17	1556	24	1881	20	1418	19	3424	13	786	32	1418	25	2561	21	1035	30	916	29	729	31	1405	31	918	41	2211	24	22274	6
	oak	2176	29	554	31	1378	31	597	23	921	17	89	57	143	48	67	49	194	68	89	54	17	100	1073	31	91	72	2274	23	9664	11
	sweet chestnut	0	.	0	.	17	100	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	328	75	28	100	12379	13	12765	13
	other broadleaves	3279	14	1059	19	1258	24	1995	20	3248	15	1169	29	1640	18	2219	25	2636	21	1515	39	1209	30	4924	15	5258	21	16583	10	47992	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	0
	total	64293	4	16387	10	16718	11	28334	7	39049	6	19184	11	44891	6	33575	7	25080	9	14263	13	13608	13	47265	6	59321	5	69328	4	491296	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. 1283821. Birmensdorf, Swiss Federal Research Institute WSL

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