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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NFI4
growing stock (stemwood)
development stage · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
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 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	27	100	0	.	7	100	1	100	-10	100	238	50	93	58	164	58	181	49	0	.	0	0	102	66	394	40	142	77	1340	21
	fir	0	.	0	.	0	.	0	.	5	100	105	68	63	74	5	100	0	.	0	.	0	.	3	100	22	100	0	.	203	43
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	28	100	0	.	0	.	28	100
	larch	0	.	0	.	7	100	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	94	46	78	50	47	100	239	32
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	0	0	.	15	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	15	75	0	.	-5	229	0	0	32	68	10	83	29	63	3	72	0	.	0	.	0	.	0	.	13	100	15	100	112	35
	maple	7	100	0	.	0	.	0	.	5	100	3	100	6	100	9	94	0	.	0	.	0	.	0	.	0	.	0	.	30	46
	ash	0	.	0	.	0	.	14	100	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	11	100	0	.	27	66
	oak	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	1	100	0	.	0	.	2	100	5	100	3	100	0	.	2	71	0	.	0	.	3	100	0	.	10	100	0	0	26	48
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0
	total	51	76	0	.	14	126	17	84	50	58	360	43	191	39	184	54	181	49	0	.	3	100	242	43	528	32	204	77	2025	17
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	78	63	49	80	61	71	179	52	336	31	230	34	416	28	285	37	439	39	91	75	110	65	342	58	1136	24	160	42	3913	12
	fir	115	54	0	.	74	51	112	40	231	47	86	65	334	48	71	49	51	54	1	100	65	59	8	100	105	78	0	100	1255	19
	pine	65	56	4	100	0	.	16	71	58	73	0	.	0	.	0	.	0	.	1	100	0	.	33	98	39	78	0	.	217	34
	larch	0	.	2	100	0	.	0	.	39	71	0	.	10	100	0	.	71	67	0	.	63	98	195	41	71	44	87	46	539	23
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	0	.	0	.	0	.	6	83	0	.	34	85
	other conifers	0	.	0	.	0	.	0	.	1	100	0	.	0	100	0	.	0	.	7	73	0	.	3	100	5	71	0	.	17	42
	beech	136	38	282	43	62	39	341	37	321	25	36	74	53	46	202	34	2	77	79	80	25	100	1	100	11	79	10	72	1562	14
	maple	12	70	7	50	19	54	22	65	66	65	17	58	67	38	38	53	69	79	14	58	37	59	1	1335	1	100	13	84	384	22
	ash	3	71	19	110	14	106	11	96	42	48	18	75	17	60	10	100	5	100	0	.	4	100	10	81	0	.	7	100	160	26
	oak	7	100	10	100	0	.	33	73	106	63	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	3	100	159	45
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	77	30	77
	other broadleaves	14	49	5	46	60	91	16	78	65	47	15	68	20	40	20	61	18	52	7	63	17	37	57	52	35	55	28	53	378	20
	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	431	30	380	39	290	33	731	26	1265	19	402	29	919	25	625	27	685	30	200	47	321	42	651	36	1409	22	340	29	8647	8
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1163	22	396	36	625	26	906	21	1959	19	536	34	1030	25	923	23	843	25	332	39	227	36	915	22	1752	16	412	26	12020	6
	fir	579	28	42	78	109	39	532	27	463	29	28	82	485	28	225	36	41	69	40	69	47	83	102	46	38	58	111	61	2842	11
	pine	82	48	25	100	10	100	60	61	166	34	0	.	41	93	24	100	65	62	5	100	0	.	402	34	426	23	8	100	1313	15
	larch	46	63	5	100	70	56	4	71	73	41	14	74	8	100	4	84	55	57	0	.	20	100	703	18	632	26	488	22	2122	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	0	.	0	.	64	68	66	56	0	100	153	40
	other conifers	1	682	28	62	5	74	34	52	45	47	0	.	0	0	0	.	27	83	0	.	1	100	8	65	29	37	27	100	205	24
	beech	376	28	195	40	221	33	368	27	684	28	25	48	164	44	252	32	18	99	12	51	146	50	222	41	45	85	964	21	3691	10
	maple	206	30	106	42	77	46	211	28	585	23	91	51	87	27	164	40	96	39	91	43	74	61	95	28	64	62	172	25	2118	10
	ash	156	34	96	44	66	46	169	42	439	23	43	52	47	45	143	48	46	60	71	47	23	63	190	33	61	37	315	23	1865	10
	oak	153	51	57	84	64	49	78	40	175	34	0	.	0	.	0	.	28	71	0	.	0	.	92	34	12	100	254	28	913	16
	sweet chestnut	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	63	0	.	1197	20	1223	19
	other broadleaves	145	29	166	61	184	41	157	31	268	19	54	56	88	32	108	36	104	51	135	40	36	59	554	17	541	22	1644	14	4184	8
	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	2907	15	1117	23	1431	19	2518	14	4855	12	791	27	1950	18	1866	18	1325	19	686	26	574	27	3373	12	3664	11	5593	8	32649	4
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1883	19	362	40	733	34	1261	24	2263	18	868	36	1424	23	1358	26	940	27	646	41	862	35	1291	26	2420	18	456	32	16768	7
	fir	885	20	184	50	357	45	431	33	300	34	324	50	242	40	660	32	17	57	176	63	40	55	100	80	53	64	122	52	3891	12
	pine	525	35	91	42	65	77	4	100	227	41	0	.	9	100	8	100	1	100	28	81	0	.	677	28	647	35	78	100	2360	16
	larch	16	75	17	82	36	87	59	62	143	50	0	.	0	.	0	.	20	100	0	.	20	77	687	26	443	26	736	26	2176	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	116	50	252	35	0	.	368	29
	other conifers	77	100	105	69	71	84	81	83	90	57	0	.	0	.	12	71	8	100	0	.	1	100	0	100	1	100	1	100	446	33
	beech	2696	14	765	24	371	34	623	26	864	25	166	56	404	36	589	27	160	49	260	42	211	47	190	47	155	59	1500	20	8952	7
	maple	226	23	93	37	171	57	79	38	249	29	152	45	191	47	265	33	227	32	143	56	138	40	68	46	21	71	108	41	2130	11
	ash	283	34	181	35	379	40	133	36	580	25	103	65	180	68	296	33	47	76	65	79	45	62	20	58	37	68	452	33	2800	12
	oak	440	35	182	45	316	37	127	39	91	37	20	75	22	87	6	100	9	73	5	100	0	.	53	48	0	.	291	32	1562	15
	sweet chestnut	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	821	22	839	21
	other broadleaves	230	34	72	36	30	42	164	40	331	32	6	133	65	49	46	70	87	73	60	53	0	3155	274	36	169	43	1261	17	2794	11
	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	7261	11	2049	18	2529	21	2959	17	5140	13	1638	26	2537	18	3242	19	1516	23	1383	31	1318	29	3490	15	4198	14	5826	10	45086	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3829	12	926	25	649	37	1213	27	2914	15	3129	19	2417	19	4526	15	1901	25	1434	30	1339	30	2458	18	6982	11	2267	21	35983	5
	fir	3199	14	215	34	404	44	914	29	1122	21	1015	26	950	23	1992	20	260	43	289	48	429	40	307	46	288	40	432	35	11816	7
	pine	485	25	210	35	90	70	106	45	649	22	0	.	98	63	38	71	0	0	172	53	73	100	230	37	714	25	193	71	3056	11
	larch	0	.	123	66	23	75	102	54	186	45	0	.	17	100	51	66	17	71	0	.	106	62	970	25	1393	17	1101	23	4089	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	48	100	0	.	0	.	0	.	0	.	40	74	379	30	0	.	466	27
	other conifers	41	87	34	100	73	87	14	71	38	72	0	.	4	100	5	71	0	.	1	100	0	.	0	.	9	90	0	.	218	39
	beech	5237	10	2686	15	1421	23	1379	20	2334	16	459	32	1403	25	1595	19	942	37	133	61	563	31	93	66	215	41	927	23	19387	5
	maple	688	17	257	29	41	44	83	44	348	21	120	36	126	28	392	22	133	33	69	46	88	42	19	67	25	53	19	69	2408	8
	ash	569	28	253	30	353	37	395	65	928	19	71	49	193	52	558	28	128	57	10	58	170	40	5	100	11	93	113	52	3755	12
	oak	224	33	170	38	179	45	265	43	418	27	0	.	15	100	12	100	93	77	11	93	0	.	9	75	109	69	78	55	1584	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	3	100	0	.	440	43	448	42
	other broadleaves	258	30	225	39	127	52	95	50	288	29	23	56	90	40	66	41	126	49	30	62	112	48	117	49	15	68	300	36	1871	12
	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
	total	14529	8	5099	13	3359	18	4563	16	9224	10	4816	17	5361	14	9237	12	3604	19	2150	25	2881	21	4249	15	10139	9	5869	12	85080	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6351	12	997	27	2150	20	3717	16	4781	15	4431	17	8987	11	8368	12	4382	17	2223	23	2494	23	4740	15	11185	10	3998	16	68804	4
	fir	5996	13	892	30	1028	27	4106	14	3237	16	1566	29	5836	13	3117	17	1087	28	606	42	352	50	175	47	685	32	434	46	29118	5
	pine	277	45	171	54	129	65	301	32	582	26	0	.	190	91	88	77	0	.	0	.	0	.	295	50	332	32	33	100	2398	15
	larch	36	75	20	58	244	52	72	59	106	44	46	100	129	100	160	52	49	56	0	.	205	67	3630	15	2696	19	1330	19	8724	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	135	50	178	38	0	.	312	30
	other conifers	3	79	8	77	157	74	150	75	170	104	0	.	48	87	4	287	0	.	0	.	0	.	0	.	0	.	0	.	541	45
	beech	4417	11	2923	16	2453	19	3768	13	5182	12	1077	28	2840	18	3374	16	797	30	425	40	598	32	56	59	405	45	859	27	29174	5
	maple	637	17	276	30	105	39	249	39	447	22	82	39	234	23	266	29	182	33	69	49	142	50	44	56	29	91	59	68	2823	9
	ash	298	32	341	29	715	30	541	31	1030	20	209	48	234	32	446	29	55	64	131	63	50	66	38	63	43	62	12	100	4144	10
	oak	310	35	461	38	679	34	228	28	859	29	43	63	16	72	89	48	0	.	10	100	0	.	0	0	0	.	27	71	2722	15
	sweet chestnut	0	.	0	.	1	100	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	777	37	780	37
	other broadleaves	293	46	405	59	292	61	129	41	535	51	11	62	87	39	118	33	394	51	33	70	64	49	33	47	19	67	196	35	2610	18
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	18617	8	6496	13	7953	13	13261	9	16931	8	7467	15	18602	9	16030	10	6946	14	3497	21	3905	20	9146	11	15571	9	7727	11	152150	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5322	10	22	89	660	28	811	28	1329	24	2442	17	6333	10	1703	19	4552	15	1382	23	1596	21	5018	11	7640	10	3649	14	42460	4
	fir	4261	12	55	73	270	49	925	26	381	34	796	29	3748	13	860	24	596	32	379	33	205	52	829	29	511	29	729	29	14544	6
	pine	287	27	92	54	135	63	29	100	189	40	0	0	221	49	8	77	58	41	123	50	110	61	417	25	660	27	89	53	2418	12
	larch	1	100	0	.	6	100	54	71	48	58	0	.	0	.	17	100	15	77	0	.	20	74	3530	13	1425	14	2289	12	7404	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	164	20	100	0	.	732	19	738	21	15	61	1511	14
	other conifers	11	136	103	98	0	.	74	83	63	52	0	.	3	85	8	67	2	100	4	89	-10	100	1	100	19	69	1	100	278	45
	beech	3886	13	354	35	299	31	663	27	867	21	466	37	2245	15	744	25	535	32	304	39	413	32	365	36	270	35	2039	19	13450	6
	maple	666	17	43	65	123	43	49	73	256	43	182	34	413	22	200	42	315	28	19	81	219	36	118	36	157	58	71	42	2829	10
	ash	522	22	212	48	388	30	244	35	497	24	36	63	156	31	308	33	204	41	42	51	77	61	143	42	73	54	275	32	3176	10
	oak	172	41	206	53	150	43	155	49	306	31	5	100	52	60	26	84	8	73	38	71	15	97	88	47	55	81	304	29	1579	14
	sweet chestnut	0	.	0	.	71	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	80	11	100	2086	16	2240	16
	other broadleaves	361	26	60	48	327	37	125	75	301	30	42	37	103	25	184	48	171	35	29	49	56	42	321	31	177	30	1268	14	3526	9
	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	15489	7	1146	27	2428	19	3129	19	4236	15	3969	16	13274	8	4059	16	6462	13	2340	19	2701	18	11633	8	11735	8	12814	7	95415	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18651	5	2752	15	4885	12	8088	10	13572	7	11873	9	20701	6	17328	7	13239	9	6109	13	6628	12	14867	7	31510	5	11084	8	181288	2
	fir	15035	6	1388	21	2241	17	7020	10	5739	10	3921	15	11659	8	6931	10	2052	18	1492	22	1138	24	1523	20	1701	18	1829	18	63668	3
	pine	1722	15	593	23	429	33	515	23	1870	13	0	0	558	39	166	47	124	38	329	34	183	54	2082	15	2817	13	401	42	11791	6
	larch	98	42	167	50	386	36	290	30	608	21	60	79	164	79	231	39	228	30	0	.	434	38	9809	8	6738	9	6078	8	25291	4
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	48	100	22	100	35	87	20	100	0	.	1102	15	1619	14	15	60	2861	10
	other conifers	132	65	277	47	306	47	352	41	407	47	0	.	55	76	30	53	37	65	12	53	-7	141	12	50	63	31	29	93	1705	19
	beech	16764	5	7205	9	4823	12	7142	9	10283	7	2240	17	7137	10	6759	9	2453	19	1213	21	1956	16	926	21	1114	22	6315	9	76329	2
	maple	2442	8	782	16	536	23	693	18	1956	11	648	18	1124	13	1334	13	1022	15	404	25	698	19	344	19	297	35	442	18	12721	4
	ash	1831	13	1103	16	1914	16	1506	22	3516	10	480	27	827	22	1763	14	486	26	320	33	370	25	405	23	236	25	1173	16	15928	5
	oak	1306	17	1087	21	1393	20	885	18	1954	16	68	46	106	39	134	37	138	54	64	48	15	97	242	24	175	50	957	16	8524	7
	sweet chestnut	0	.	0	.	71	99	2	100	3	100	0	.	0	.	0	.	4	100	0	.	0	.	116	54	11	100	5352	10	5559	9
	other broadleaves	1303	15	932	29	1019	24	687	21	1793	18	154	25	454	15	544	20	901	26	294	24	288	25	1355	13	966	16	4698	7	15389	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	59284	3	16287	7	18004	7	27179	6	41701	4	19443	8	42834	5	35243	5	20720	7	10256	11	11704	10	32783	5	47246	4	38372	3	421054	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. 1231060. Birmensdorf, Swiss Federal Research Institute WSL

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