Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI3
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 2004/06
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	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	18	73	0	.	28	63	37	52	32	60	104	31	49	31	97	65	76	38	145	72	53	61	39	46	99	24	62	53	63	14
	fir	19	92	0	.	0	.	29	47	6	60	22	72	22	71	19	56	3	96	0	.	0	.	22	73	2	67	0	.	12	29
	pine	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	5	94	0	.	0	.	0	76
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	95	0	.	0	.	4	62	6	50	14	56	2	35
	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	11	92	4	92	19	53	57	64	23	34	6	98	10	76	13	96	50	96	13	87	0	.	0	.	1	97	44	68	16	30
	maple	0	.	0	.	0	.	0	.	0	.	0	.	6	58	4	69	2	96	13	87	8	92	0	.	0	.	0	.	2	37
	ash	0	.	0	.	0	.	12	94	1	97	5	72	2	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	51
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	9	91	0	.	0	.	0	.	10	69	0	.	2	98	11	66	0	.	0	.	9	91	16	94	12	98	0	.	5	39
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	56	70	4	92	47	47	135	38	73	30	137	26	91	25	145	49	133	42	170	74	69	60	88	30	120	25	119	35	102	11
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	41	58	15	51	69	55	12	57	47	37	45	45	63	27	47	40	46	38	64	54	34	49	22	28	70	19	10	50	40	11
	fir	24	29	6	69	3	97	9	46	7	45	5	95	36	34	15	54	3	79	3	96	13	51	0	.	3	68	0	.	9	16
	pine	4	60	0	.	2	97	2	99	3	57	0	.	0	.	0	.	0	.	0	.	0	.	34	76	4	78	0	.	5	56
	larch	1	99	0	.	2	97	0	.	2	59	0	.	0	.	0	.	3	98	1	96	5	98	36	26	6	41	28	41	8	21
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	65	1	99	0	.	1	57
	other conifers	1	99	2	98	3	98	0	.	0	.	0	.	0	.	0	.	0	.	7	96	0	.	4	99	4	58	0	.	2	40
	beech	19	34	34	23	43	43	35	25	28	21	6	70	6	49	11	45	6	62	3	97	26	46	4	72	0	.	15	70	16	13
	maple	9	44	5	68	13	63	7	46	6	37	20	54	3	62	6	57	4	69	0	.	10	57	5	90	1	99	2	69	5	18
	ash	4	85	5	84	6	68	6	52	4	70	11	94	1	99	3	97	4	98	0	.	5	67	1	99	0	.	15	56	4	26
	oak	2	69	3	74	4	76	1	69	2	60	0	.	0	.	0	.	0	.	0	.	0	.	9	75	0	.	1	99	2	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	51	2	53
	other broadleaves	13	40	15	40	13	56	3	99	10	50	0	.	10	61	20	57	14	43	23	67	0	.	30	48	8	42	60	37	17	18
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	119	25	85	19	158	31	75	22	108	17	87	34	118	18	101	27	80	24	101	37	93	24	151	21	97	14	151	19	112	6
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	299	20	217	41	337	22	307	14	326	13	396	22	348	21	370	18	188	21	131	26	124	29	127	24	295	13	26	29	216	6
	fir	87	24	25	68	37	30	60	28	38	34	69	50	27	69	48	44	16	58	39	45	0	.	6	47	4	59	8	51	27	12
	pine	15	71	36	66	8	47	20	64	11	37	0	.	35	70	1	99	26	70	7	81	0	.	82	27	76	25	0	.	27	15
	larch	4	80	5	67	23	49	6	53	6	54	0	.	8	98	9	86	23	48	0	.	18	97	94	21	55	26	45	22	33	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	99	0	.	0	.	0	.	7	64	9	48	0	.	2	37
	other conifers	12	72	14	58	6	90	9	49	14	59	0	.	0	.	3	99	16	82	11	97	3	97	1	74	12	43	0	100	7	24
	beech	110	29	80	35	70	32	89	19	80	23	34	37	31	53	63	33	47	61	31	61	50	84	30	40	3	61	103	18	65	10
	maple	63	25	43	43	45	41	48	25	72	21	46	43	20	35	61	46	71	26	60	44	60	36	16	25	5	59	18	29	37	9
	ash	32	34	78	40	52	40	41	46	70	19	27	58	24	49	74	40	35	37	75	40	22	73	17	35	8	41	34	23	38	10
	oak	25	64	29	98	42	58	9	44	18	37	7	98	2	98	0	.	26	71	0	.	0	.	28	32	0	.	26	31	18	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	75	0	.	115	18	26	19
	other broadleaves	43	37	97	60	55	31	58	33	46	19	98	38	38	38	47	39	61	39	177	47	130	45	147	16	129	22	212	11	115	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	689	8	625	13	676	10	646	6	682	5	677	11	532	12	679	10	510	11	531	17	408	22	558	7	594	6	586	5	609	2
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	131	15	129	37	86	35	226	19	216	14	352	20	377	21	199	19	328	25	123	32	447	26	227	17	276	15	67	24	191	6
	fir	86	14	30	74	56	57	78	32	36	29	103	35	41	31	83	30	31	67	127	40	20	47	29	43	9	64	8	66	45	10
	pine	19	36	10	36	22	79	4	49	12	35	0	.	5	72	5	99	6	97	40	81	0	.	70	24	98	31	4	100	24	15
	larch	0	.	1	99	0	.	10	69	14	54	0	.	0	.	0	.	0	.	0	.	39	62	107	18	37	35	31	25	25	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	45	20	65	0	100	4	39
	other conifers	4	100	11	77	9	98	8	48	2	73	0	.	0	.	3	69	21	97	5	94	2	98	1	99	3	99	0	100	3	32
	beech	218	9	242	16	157	32	147	23	88	16	82	47	149	33	174	22	93	43	173	33	159	36	35	40	36	40	117	19	130	6
	maple	29	20	28	38	30	48	7	57	29	27	39	42	31	30	74	24	94	40	49	50	77	43	8	47	6	61	12	40	26	10
	ash	26	27	60	38	98	30	21	34	41	27	37	54	59	62	64	25	31	48	55	69	103	69	10	41	20	44	32	28	36	12
	oak	37	35	29	71	82	42	9	53	23	49	5	68	2	99	2	98	28	97	5	94	0	.	7	41	0	.	32	27	21	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	99	0	.	125	18	23	19
	other broadleaves	32	22	43	33	49	52	34	30	48	26	22	50	19	40	70	31	65	51	40	49	124	41	39	27	37	31	148	13	62	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	582	4	582	9	588	9	546	7	508	5	639	12	683	12	674	8	696	14	615	17	971	17	551	9	542	7	577	5	591	2
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	116	8	55	21	93	21	108	19	100	12	271	11	235	15	189	12	227	14	416	18	130	23	232	10	319	7	110	17	171	4
	fir	86	9	38	23	50	37	84	20	43	20	81	24	86	20	73	17	36	27	59	37	50	40	18	38	7	37	19	29	54	6
	pine	14	24	14	25	11	46	3	74	19	21	0	.	5	62	2	51	3	88	35	54	17	98	28	32	39	23	8	80	15	11
	larch	0	.	6	54	6	79	3	59	4	38	2	99	0	99	1	74	5	68	0	.	3	99	98	17	35	18	68	22	18	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	6	99	0	.	2	99	0	.	0	.	9	50	10	37	0	.	2	30
	other conifers	4	52	1	77	0	.	1	99	2	60	0	.	1	100	1	100	0	.	0	.	0	.	1	99	6	86	0	.	2	38
	beech	150	7	195	9	153	13	130	13	98	11	64	25	101	17	133	16	130	35	42	68	172	22	11	62	14	30	71	27	106	5
	maple	32	10	31	20	26	57	24	36	24	21	47	28	27	40	41	22	36	27	22	47	42	37	9	54	1	57	2	75	25	8
	ash	18	21	36	33	24	27	23	45	26	16	17	38	13	34	33	24	17	41	10	61	32	34	1	100	1	100	4	45	17	9
	oak	8	36	15	29	41	30	12	29	6	31	2	71	3	60	2	68	10	83	3	98	0	.	0	70	2	100	7	45	8	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	100	0	.	34	28	2	30
	other broadleaves	23	17	18	25	28	34	17	38	23	30	26	37	15	37	16	30	41	29	29	40	14	39	28	31	2	49	51	25	22	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	450	3	411	6	431	9	406	7	346	5	508	7	493	8	490	6	508	11	615	9	460	11	436	6	437	5	375	6	442	2
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	84	10	33	28	111	13	83	11	66	11	252	28	140	8	181	9	186	13	200	15	168	17	157	12	220	7	114	12	135	3
	fir	102	10	35	36	36	21	93	12	38	14	63	27	131	10	63	12	52	25	61	33	29	36	21	40	9	32	20	32	59	5
	pine	3	47	6	51	5	50	5	33	13	19	0	.	2	100	1	65	0	.	8	99	0	.	11	46	9	39	2	70	5	14
	larch	0	70	1	99	7	46	0	100	2	45	0	.	1	79	2	53	4	50	0	.	14	58	93	13	34	15	34	17	15	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	34	13	30	0	.	2	24
	other conifers	1	70	1	99	1	72	3	55	2	59	0	.	1	85	1	61	0	.	0	.	0	.	0	.	0	.	0	.	1	27
	beech	79	8	141	13	93	14	98	9	111	9	72	25	114	12	91	12	62	32	79	23	78	31	3	58	12	31	42	28	76	4
	maple	23	20	32	24	13	37	11	28	17	22	11	41	18	21	17	23	18	32	21	30	21	35	9	48	3	43	1	61	14	8
	ash	9	27	21	24	36	25	16	23	17	18	12	67	6	43	15	29	6	67	15	42	4	55	4	45	3	65	0	.	11	9
	oak	4	39	14	25	24	29	7	36	13	19	4	84	1	76	1	51	0	.	1	76	0	.	0	100	1	82	10	83	6	16
	sweet chestnut	0	.	0	.	9	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	25	3	25
	other broadleaves	21	26	27	48	17	46	9	28	20	27	3	69	6	30	12	39	38	30	17	31	12	56	11	39	6	43	23	23	15	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	327	4	310	8	353	5	325	5	300	5	417	17	419	4	386	5	366	8	401	10	326	12	320	6	309	5	275	7	341	2
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	120	12	4	66	40	28	75	28	101	22	231	14	206	9	205	15	273	9	255	22	268	16	171	10	236	9	91	14	173	4
	fir	115	11	4	64	48	34	110	24	32	44	103	28	82	12	64	32	13	47	63	49	40	50	13	34	7	32	10	37	43	7
	pine	11	36	83	42	13	70	6	71	11	50	0	.	17	60	4	98	7	37	41	52	28	67	39	24	30	23	3	64	18	13
	larch	0	77	0	.	0	.	2	98	1	76	0	.	0	.	0	.	2	62	0	.	5	60	76	15	51	13	60	13	29	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	71	14	97	0	.	38	21	41	20	1	74	12	15
	other conifers	1	61	34	82	0	.	17	52	30	54	0	.	0	100	2	98	1	99	11	97	5	99	0	.	8	54	0	.	4	28
	beech	81	12	137	47	91	32	142	29	117	18	56	52	73	17	60	33	30	34	28	68	92	33	19	40	13	37	81	16	58	7
	maple	23	21	34	42	22	55	15	60	44	22	29	26	25	40	35	34	37	27	8	97	34	41	10	31	8	41	10	28	19	10
	ash	7	31	47	67	29	32	23	34	55	29	5	55	15	35	52	35	11	53	6	67	17	55	12	56	8	61	11	31	14	13
	oak	10	47	68	54	2	66	12	57	18	35	0	.	2	70	0	.	2	71	7	67	1	98	6	46	2	60	7	37	6	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	95	0	100	53	17	10	18
	other broadleaves	17	23	128	28	91	61	26	38	62	24	17	31	28	24	47	36	38	28	35	46	24	39	29	23	25	36	74	12	38	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	385	5	539	24	338	16	427	12	469	12	441	12	448	6	468	10	415	7	467	14	512	10	416	5	429	6	401	5	424	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	122	6	68	19	125	12	132	8	141	7	258	8	192	6	199	6	212	6	211	10	191	11	165	6	242	4	72	8	159	2
	fir	91	5	28	20	38	17	76	9	35	11	76	14	88	7	62	9	26	16	54	19	28	22	15	19	7	17	11	18	44	3
	pine	11	17	15	22	9	28	7	34	12	12	0	.	9	38	2	39	6	40	18	32	9	57	44	13	36	13	3	43	15	7
	larch	0	54	3	40	7	33	3	37	4	27	0	100	1	64	2	52	6	30	0	100	12	35	86	7	39	9	46	9	21	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	100	0	71	2	100	0	.	16	16	19	15	0	66	4	11
	other conifers	3	36	5	39	2	52	5	26	7	34	0	.	0	57	2	39	4	59	4	55	2	65	1	52	5	31	0	71	3	13
	beech	124	5	153	7	104	9	104	7	91	6	55	18	87	8	97	9	61	19	55	18	98	15	18	21	12	17	81	9	77	3
	maple	29	8	28	13	23	23	19	16	32	12	30	16	21	17	34	14	37	14	24	22	37	18	10	17	4	25	9	18	21	4
	ash	16	13	36	18	37	15	21	19	34	11	15	24	14	23	33	15	14	21	22	27	27	38	9	25	6	29	18	15	19	5
	oak	13	21	19	26	32	20	8	18	13	16	2	45	1	35	1	39	8	45	2	42	0	100	8	23	1	51	16	18	9	8
	sweet chestnut	0	.	0	.	3	70	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	2	58	0	100	73	10	10	9
	other broadleaves	24	10	36	20	33	20	22	18	32	11	24	22	16	15	26	16	39	15	45	29	39	25	49	11	31	16	109	7	41	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	434	2	391	5	413	5	397	4	400	3	462	5	429	3	458	4	412	5	438	6	441	8	423	3	402	3	438	3	423	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. 1356692. Birmensdorf, Swiss Federal Research Institute WSL

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