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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NFI2
number of stems
development stage · main tree species
regional demarcation: economic region
unit: n/ha
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	40	78	0	.	120	0	86	58	28	62	49	60	21	58	8	92	43	39	73	43	12	89	43	22
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	96	3	92	9	91	0	.	0	.	0	.	0	.	2	61
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	91	0	.	5	95	0	.	0	.	1	76
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	64	2	96	26	57	2	50
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	64	46	0	.	0	.	7	93	22	96	0	.	0	.	31	92	0	.	0	.	55	51	12	39
	maple	35	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	92	0	.	0	.	0	.	0	.	2	76
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	85	4	99
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	92	0	.	10	95	0	.	0	.	2	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	35	71	0	.	0	.	104	5	0	.	120	0	93	53	54	51	53	59	49	36	39	92	62	31	75	41	159	26	68	16
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	27	32	15	51	46	63	48	54	18	34	49	38	77	24	44	33	33	30	45	37	26	43	15	37	66	17	17	45	37	10
	fir	30	44	8	96	0	.	12	60	6	46	2	95	22	42	17	57	8	70	6	97	4	74	6	84	3	59	4	78	9	19
	pine	4	99	0	.	8	68	4	97	8	49	0	.	0	.	0	.	0	.	0	.	0	.	1	99	9	59	0	.	3	32
	larch	1	99	0	.	4	71	0	.	6	57	0	.	0	.	12	97	4	68	1	97	0	.	23	37	20	28	12	39	9	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	72	0	.	0	.	1	73
	other conifers	0	.	0	.	0	.	4	98	5	84	0	.	0	.	0	.	0	.	15	79	0	.	0	.	3	99	0	.	2	47
	beech	9	37	17	46	9	73	22	61	10	35	62	55	4	53	22	35	2	98	5	77	56	49	4	74	2	61	12	37	12	17
	maple	7	50	0	.	3	97	14	71	7	36	15	47	2	72	12	32	10	68	6	97	19	46	9	54	2	71	8	55	7	17
	ash	1	99	0	.	0	.	8	64	12	39	4	96	11	61	6	71	0	.	3	97	8	71	3	71	1	99	1	75	4	22
	oak	1	99	0	.	9	97	3	75	1	59	0	.	0	.	0	.	0	.	0	.	0	.	5	69	0	.	0	.	1	43
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	81	0	82
	other broadleaves	17	58	0	.	0	.	11	39	12	52	38	71	12	52	17	43	4	68	12	75	17	52	25	35	11	43	33	28	16	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	96	18	40	36	78	49	125	28	86	15	170	25	128	17	129	21	61	24	94	30	131	25	96	20	117	12	90	17	102	6
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	258	15	149	27	263	21	430	14	412	11	535	18	411	14	335	17	292	18	276	29	248	23	88	25	203	19	45	21	221	5
	fir	78	20	27	59	72	43	90	25	37	25	68	44	42	30	71	24	9	69	89	34	31	68	18	53	6	65	5	44	34	10
	pine	10	50	42	44	25	55	11	56	17	39	0	.	33	66	3	75	17	69	27	98	41	68	147	18	115	22	0	71	37	12
	larch	5	65	8	53	13	52	10	66	13	52	0	.	0	.	0	.	6	80	0	.	24	61	64	20	45	25	30	20	23	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	8	99	2	99	0	.	0	.	0	.	17	41	20	51	0	.	4	30
	other conifers	11	67	13	74	27	68	7	57	11	52	2	99	1	99	2	69	1	99	2	98	5	98	6	87	17	42	0	100	7	23
	beech	185	14	162	19	58	26	114	20	81	21	47	43	109	29	98	26	62	52	88	35	133	47	35	34	12	58	119	14	95	7
	maple	28	23	33	28	47	52	20	32	50	26	26	49	28	41	57	29	89	26	52	36	70	43	10	27	13	44	11	25	28	9
	ash	40	28	56	33	50	34	33	30	57	19	38	53	26	46	81	28	19	51	50	39	70	57	15	31	14	39	31	18	36	9
	oak	57	32	39	61	123	36	11	41	20	41	2	98	2	60	1	99	27	55	2	98	0	.	16	29	1	81	30	22	25	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	51	0	99	131	13	34	13
	other broadleaves	45	23	46	34	83	25	58	31	55	20	48	43	58	27	91	30	65	33	130	40	93	27	108	16	133	21	191	8	108	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	717	4	575	8	761	7	785	6	753	5	766	11	718	7	741	7	587	9	717	9	715	14	531	6	580	7	593	4	654	2
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	111	11	68	27	147	23	149	17	144	14	441	12	285	14	312	16	316	14	295	24	495	27	238	15	310	8	79	29	213	4
	fir	94	11	24	41	25	35	119	22	30	34	139	20	108	18	71	18	23	43	72	37	15	54	16	37	8	38	18	60	57	7
	pine	19	28	29	33	6	42	5	69	20	27	0	.	7	61	1	99	6	65	62	37	0	.	61	32	56	24	13	83	23	12
	larch	0	.	7	58	10	80	7	67	2	49	0	.	0	100	1	99	4	64	0	.	11	61	107	17	53	16	65	28	20	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	24	55	30	32	0	.	6	28
	other conifers	1	70	10	82	1	99	2	99	2	57	0	.	3	80	1	99	0	.	0	.	3	97	0	.	1	100	0	.	1	37
	beech	218	7	241	11	175	16	169	19	123	12	94	42	135	18	166	20	98	26	119	30	117	31	51	37	16	49	145	24	137	5
	maple	42	11	46	19	20	48	17	51	37	19	49	32	20	22	34	28	38	33	10	52	56	51	13	57	1	70	0	.	27	8
	ash	18	28	41	33	46	23	20	24	47	17	11	46	21	37	34	30	17	45	12	46	24	58	4	61	1	100	3	74	20	10
	oak	16	40	22	29	28	36	23	39	18	42	6	80	6	76	4	79	20	60	2	98	0	.	6	44	0	.	15	52	12	16
	sweet chestnut	0	.	0	.	5	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	111	27	7	29
	other broadleaves	21	18	46	33	43	33	12	31	42	26	22	31	36	39	29	25	52	23	21	44	26	45	38	31	10	39	44	41	30	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	540	3	534	6	507	7	523	7	465	4	762	8	621	6	652	8	573	9	594	10	746	17	559	7	486	5	494	7	553	2
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	114	8	71	22	169	12	122	12	131	9	333	13	212	8	219	9	224	13	280	20	154	19	252	10	278	7	135	15	180	3
	fir	96	9	40	24	37	25	103	13	55	13	62	33	100	13	80	17	40	30	89	35	31	42	17	43	5	39	30	30	60	5
	pine	12	29	13	29	15	48	7	42	20	17	0	.	4	74	3	44	2	99	23	83	0	.	26	30	23	29	11	84	13	11
	larch	0	100	1	76	3	73	1	100	3	45	2	99	0	100	1	61	3	56	0	.	22	57	105	16	32	16	43	25	15	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	36	21	27	0	.	4	22
	other conifers	1	73	2	51	0	99	5	54	0	70	0	.	0	100	2	57	0	.	0	.	0	.	0	.	0	.	0	.	1	30
	beech	99	7	197	11	133	14	114	12	111	10	65	28	103	17	107	16	139	33	111	35	105	33	3	55	12	32	51	26	92	5
	maple	17	16	33	24	10	44	9	39	18	21	14	34	19	21	30	24	30	26	25	36	26	48	5	59	1	50	0	.	15	8
	ash	13	23	29	30	31	37	18	53	20	17	12	54	14	30	19	34	9	54	15	84	15	45	7	65	2	62	2	83	14	11
	oak	7	25	16	28	33	27	7	28	12	21	2	57	1	75	1	50	0	.	8	66	0	.	1	100	3	83	3	74	7	12
	sweet chestnut	0	.	0	.	2	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	41	1	39
	other broadleaves	25	18	23	24	14	56	11	40	17	23	10	43	18	43	16	37	32	34	22	46	32	42	19	43	7	37	18	37	18	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	385	3	426	7	447	5	395	5	389	3	499	10	473	5	477	6	480	9	573	11	386	12	451	6	383	5	312	7	419	2
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	80	10	30	37	96	17	100	11	93	11	274	26	159	9	168	10	218	13	212	17	187	18	142	13	184	8	91	15	133	4
	fir	90	14	9	51	59	22	65	15	37	18	49	30	110	11	65	16	35	42	35	50	19	46	34	42	9	28	7	52	50	6
	pine	6	48	1	98	5	45	5	34	12	22	0	.	2	100	1	57	0	.	0	.	0	.	8	54	11	49	3	67	5	17
	larch	0	100	3	99	3	71	0	70	2	43	0	.	1	84	1	57	6	51	0	.	4	68	74	16	30	21	21	20	11	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	84	10	32	0	.	1	31
	other conifers	0	.	0	.	2	99	3	62	3	70	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	41
	beech	85	11	147	12	89	16	106	8	90	10	55	37	81	17	94	18	25	55	40	60	54	33	14	79	19	35	36	40	69	5
	maple	21	28	16	31	14	55	12	32	11	26	28	37	11	27	13	32	30	54	6	77	34	34	2	99	2	61	3	92	12	11
	ash	2	56	6	44	33	40	9	32	10	28	12	69	4	67	5	44	6	99	6	97	1	98	2	73	0	100	1	99	6	16
	oak	5	46	19	32	21	40	8	27	15	25	2	98	1	72	3	64	0	.	2	97	0	.	1	99	0	.	2	51	5	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	23	3	25
	other broadleaves	11	41	16	53	7	41	9	44	24	26	7	46	4	37	9	54	15	54	19	55	1	98	16	55	3	61	29	21	12	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	300	5	246	10	329	9	317	5	298	5	427	17	374	5	359	6	335	11	320	13	302	13	294	9	268	6	229	8	309	2
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	135	11	10	49	61	23	82	24	42	28	234	14	161	10	218	15	235	13	285	23	198	20	198	10	239	9	98	15	172	4
	fir	95	14	86	57	26	65	117	29	34	55	94	26	131	13	63	21	28	31	51	60	64	31	9	37	8	41	10	40	49	8
	pine	9	35	23	86	3	94	0	.	10	53	2	99	9	52	1	99	0	99	30	65	18	76	19	33	25	28	4	77	11	16
	larch	0	.	0	.	0	.	0	.	6	69	0	.	0	.	3	99	1	70	0	.	3	99	98	15	33	19	58	14	30	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	14	96	0	.	22	21	20	31	0	76	6	18
	other conifers	5	56	0	.	0	.	7	97	17	47	0	.	0	100	2	99	0	.	15	73	0	.	0	100	7	71	0	.	3	32
	beech	81	15	83	48	94	41	106	31	119	20	28	29	75	15	105	17	38	34	42	44	96	26	4	54	13	43	51	19	52	7
	maple	22	18	33	88	32	53	13	62	36	41	22	28	13	24	55	26	20	34	14	85	23	45	5	45	4	55	3	49	15	11
	ash	25	29	17	59	41	49	28	42	41	27	24	47	6	38	51	29	7	61	17	97	10	62	7	41	10	80	6	37	15	14
	oak	2	58	5	86	0	.	18	74	12	40	5	78	0	.	0	.	0	.	5	96	0	.	6	61	1	100	9	51	4	27
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	1	100	61	19	11	20
	other broadleaves	28	26	121	39	42	52	21	46	74	29	32	29	9	36	35	35	50	31	35	59	52	52	19	34	12	28	60	16	33	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	401	6	380	13	298	16	390	10	391	12	441	9	404	5	532	8	380	9	508	16	463	13	393	6	373	6	360	5	400	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	125	6	74	14	157	10	170	9	164	7	335	7	216	5	226	7	233	7	230	11	207	12	162	6	226	4	71	8	172	2
	fir	89	5	28	19	41	17	87	9	38	10	81	13	96	7	66	9	25	17	59	18	29	21	16	21	7	17	10	19	47	3
	pine	12	16	20	22	12	29	6	25	16	12	1	100	8	37	2	31	4	46	26	32	10	53	53	14	38	13	3	42	17	7
	larch	1	58	4	35	7	38	3	45	5	28	0	100	0	59	2	52	4	28	0	100	10	34	82	8	36	8	38	9	19	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	100	0	70	2	100	0	.	16	18	18	16	0	77	4	12
	other conifers	3	42	6	49	5	61	4	29	5	30	0	100	1	49	1	39	0	100	4	52	1	78	2	82	4	35	0	100	2	14
	beech	131	5	182	7	113	9	114	7	93	6	59	18	91	8	105	9	68	18	73	18	92	16	18	22	13	19	83	10	83	2
	maple	25	8	32	13	20	27	13	18	26	12	27	17	16	13	33	13	35	15	20	23	36	20	7	21	4	27	6	21	18	4
	ash	17	14	31	18	37	16	18	19	30	10	17	25	12	19	32	15	10	25	17	29	21	36	8	21	5	37	15	16	17	5
	oak	14	20	21	24	44	21	11	18	14	16	3	42	2	46	2	40	8	42	3	42	0	.	7	23	1	60	16	18	10	8
	sweet chestnut	0	.	0	.	2	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	50	0	83	83	9	10	9
	other broadleaves	24	10	33	17	32	18	20	20	32	11	23	18	20	18	31	17	39	15	40	27	37	21	43	12	26	17	103	7	38	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	442	2	429	4	468	4	447	4	422	3	547	5	464	3	499	4	426	5	473	6	444	7	414	3	377	3	428	3	438	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. 1283233. Birmensdorf, Swiss Federal Research Institute WSL

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