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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NFI5
number of stems
development stage · main tree species
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	0	0	.	0	.	7	100	97	93	9	100	22	75	21	100	101	58	7	100	0	0	0	0	380	38	99	66	742	26
	fir	0	.	0	.	0	.	0	.	7	100	18	100	0	.	0	.	49	100	0	.	0	.	7	100	0	0	0	.	81	65
	pine	41	85	0	.	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	14	71	0	.	76	50
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	89	86	14	100	118	67
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	136	88	0	.	0	.	14	71	17	100	30	100	0	.	25	100	0	.	0	.	36	100	0	.	0	.	0	.	257	52
	maple	0	.	0	.	0	.	17	100	0	.	9	100	0	.	18	100	0	.	0	.	121	100	0	.	0	.	0	.	165	75
	ash	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	oak	0	.	0	.	9	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	16	72
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	0	7	100
	other broadleaves	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	177	87	0	.	23	58	45	86	134	79	65	100	22	75	64	74	157	50	7	100	157	80	29	50	483	39	113	59	1477	22
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	276	39	14	100	58	72	326	54	75	59	427	47	674	34	348	41	421	35	49	100	72	83	104	57	791	27	310	53	3947	13
	fir	503	76	55	58	8	100	249	41	83	53	38	71	168	44	106	47	15	100	0	.	6	100	0	0	0	0	0	.	1229	33
	pine	14	100	0	.	0	.	46	55	7	100	0	.	0	.	7	100	0	0	0	.	0	.	0	.	82	73	64	100	219	42
	larch	0	.	17	100	94	92	0	.	0	.	0	.	0	.	0	.	44	86	0	.	18	100	69	62	199	54	59	47	501	31
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	126	100	0	.	126	100
	other conifers	0	.	0	.	0	.	27	77	20	100	0	.	15	100	36	100	0	.	0	.	0	.	0	.	55	100	0	.	152	48
	beech	129	45	119	41	56	58	105	42	187	37	9	100	363	40	192	38	114	73	14	100	0	0	0	.	14	100	26	100	1328	16
	maple	46	61	60	56	0	.	0	.	62	69	22	100	64	58	118	65	95	67	10	100	42	100	17	100	105	85	0	.	641	25
	ash	0	.	55	75	8	100	0	.	0	.	42	100	0	0	49	74	0	.	0	.	18	100	43	100	0	.	0	.	215	39
	oak	14	100	0	.	58	63	14	71	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	95	44
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	69	61	19	100	8	100	53	73	128	68	44	100	102	58	15	100	201	61	45	100	35	100	87	82	115	67	266	46	1187	21
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	0
	total	1050	48	340	35	289	41	820	34	561	28	580	41	1385	25	880	32	891	31	118	58	191	61	321	38	1488	23	734	31	9649	10
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1450	38	981	46	1514	42	3298	25	4222	22	1329	47	2866	31	2111	34	1898	30	1570	36	290	54	1474	27	6528	20	1698	39	31230	8
	fir	1619	33	170	83	237	50	1863	32	670	39	197	59	1509	47	691	53	42	72	149	69	21	74	154	73	24	77	167	46	7513	16
	pine	88	61	0	.	39	100	137	87	166	64	0	.	104	100	17	100	0	.	135	100	0	.	1089	50	2330	31	0	.	4105	23
	larch	0	.	57	100	515	82	33	100	444	50	140	71	52	100	0	.	260	64	0	.	0	.	1337	31	957	37	2215	27	6012	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	87	935	38	0	.	984	36
	other conifers	51	100	14	100	20	100	69	71	71	61	0	.	7	100	0	.	249	81	58	100	0	.	156	62	451	42	0	.	1145	27
	beech	2202	27	1344	30	1239	44	1615	27	2343	23	123	67	779	46	218	41	684	58	14	71	415	86	534	53	105	71	2908	28	14523	11
	maple	1599	31	886	31	566	60	463	44	1097	29	91	85	415	33	642	42	1405	27	139	64	200	65	1269	34	20	100	1126	39	9918	11
	ash	543	39	523	35	286	49	284	47	667	30	276	62	154	52	440	45	281	55	235	62	0	.	916	39	137	79	1058	31	5800	12
	oak	638	70	36	83	212	67	121	60	205	42	0	.	22	100	0	.	106	100	0	.	0	.	687	43	0	.	953	47	2980	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	3858	28	3873	28
	other broadleaves	300	34	511	31	599	41	720	49	986	29	612	48	642	40	489	41	783	40	659	66	88	53	2810	22	3080	28	7513	18	19793	10
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	0
	total	8490	16	4522	22	5226	23	8605	17	10872	14	2769	32	6551	22	4607	25	5708	21	2958	30	1014	45	10490	15	14569	14	21496	11	107877	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1677	31	490	52	867	37	2844	27	2398	23	1915	39	3181	35	1205	39	1880	35	542	48	1299	41	4417	28	4708	22	1285	37	28709	9
	fir	1070	28	7	100	200	65	787	39	614	38	270	91	845	44	277	55	338	52	500	56	88	68	7	100	144	63	299	69	5447	15
	pine	257	58	151	58	23	76	115	75	78	45	0	.	124	71	0	.	0	.	0	.	0	.	1964	37	799	50	181	87	3692	24
	larch	7	100	31	64	48	100	24	77	49	63	0	.	0	.	0	.	70	100	0	.	261	73	1497	26	594	36	1050	43	3632	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	325	61	692	52	49	100	1066	39
	other conifers	34	100	130	74	0	.	99	45	376	63	0	.	0	.	21	100	0	.	0	.	36	71	17	100	17	100	0	.	731	36
	beech	2724	25	1026	36	266	46	814	27	1293	29	458	69	593	51	801	56	252	55	595	51	502	58	291	70	179	49	4666	25	14461	12
	maple	644	29	193	50	103	71	343	32	861	28	272	52	509	66	381	53	640	44	282	50	306	54	0	.	255	63	324	47	5114	13
	ash	298	47	36	71	75	56	247	36	260	46	136	51	577	62	212	50	102	72	257	71	105	73	128	50	139	68	864	49	3437	19
	oak	425	77	149	79	373	70	83	55	21	57	40	100	6	100	0	.	61	71	0	0	0	.	140	56	0	.	633	36	1930	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	84	18	100	2624	28	2709	27
	other broadleaves	753	47	84	47	179	56	575	37	615	30	239	73	176	59	63	100	236	57	150	62	324	69	704	35	786	62	3748	22	8631	13
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	0
	total	7889	17	2296	28	2134	29	5929	19	6565	17	3329	30	6011	25	2962	32	3580	27	2327	36	2922	32	9559	18	8331	18	15723	13	79558	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3355	19	536	32	181	53	1041	28	1229	24	1854	40	1641	32	3151	21	1816	30	984	51	466	35	2368	27	5661	17	1895	30	26178	8
	fir	2762	18	443	54	331	58	876	30	478	39	708	48	375	34	543	27	214	59	309	52	84	65	342	52	77	91	204	62	7747	11
	pine	392	54	169	38	96	66	82	70	186	35	0	.	50	100	0	.	0	.	76	90	0	.	367	46	652	40	14	100	2085	19
	larch	0	.	14	70	7	100	7	100	20	58	0	.	0	.	0	.	0	0	0	.	0	.	636	37	606	31	1129	32	2419	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	181	49	0	.	206	45
	other conifers	174	81	0	.	28	100	20	100	95	54	0	.	0	.	59	76	0	.	0	.	0	.	0	.	0	.	0	.	376	43
	beech	5232	14	2067	22	851	35	1114	25	1613	19	377	39	1419	32	1732	22	497	39	254	63	376	37	453	67	228	59	1583	34	17796	7
	maple	1147	22	292	35	93	63	64	49	432	25	138	79	156	38	476	33	223	47	105	81	139	42	18	100	33	71	114	47	3431	11
	ash	368	32	272	47	579	41	140	47	331	31	149	100	103	54	294	36	171	56	138	78	90	62	80	74	0	.	56	64	2771	15
	oak	239	46	121	59	364	49	57	60	142	40	19	100	36	100	7	100	0	.	0	.	0	.	68	62	0	.	119	51	1172	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	647	45	647	45
	other broadleaves	579	28	261	37	169	54	259	43	526	37	36	61	236	42	133	38	135	56	37	71	20	73	156	46	99	56	1310	29	3956	13
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	0
	total	14248	11	4174	19	2698	27	3661	19	5052	16	3280	29	4016	23	6397	17	3055	26	1904	39	1174	28	4512	21	7539	15	7071	15	68783	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2009	16	226	39	661	32	864	24	1681	17	2811	21	3850	15	4096	15	2004	21	1391	32	1175	37	4234	17	6774	14	3057	18	34833	5
	fir	3578	14	244	39	617	33	1796	18	1197	21	896	41	3423	15	1229	21	717	31	611	40	245	43	458	41	232	44	597	43	15839	7
	pine	129	45	14	100	40	71	117	43	148	36	0	.	90	100	13	71	0	.	144	85	0	.	25	77	250	37	0	.	970	21
	larch	14	70	7	100	0	.	7	100	73	50	0	.	0	.	36	52	35	72	0	.	25	78	1711	25	1263	20	688	25	3860	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	189	49	318	39	0	.	507	30
	other conifers	35	100	115	86	0	.	103	54	72	80	0	.	0	.	54	57	0	.	0	.	0	.	0	.	0	.	0	.	378	36
	beech	3335	14	1121	22	1375	25	2443	16	2957	14	490	43	3225	18	1835	20	774	32	604	37	401	33	18	100	542	34	1137	34	20258	6
	maple	880	20	112	53	169	49	385	33	801	25	126	39	286	33	604	29	226	35	143	43	190	49	261	52	85	61	0	.	4268	10
	ash	302	29	56	43	203	42	231	33	732	26	16	100	89	54	261	42	196	56	143	52	42	55	71	62	30	72	0	.	2371	13
	oak	131	49	112	49	69	70	109	30	334	28	8	100	0	0	0	.	0	.	6	100	0	.	16	100	35	100	46	61	866	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1121	34	1121	34
	other broadleaves	436	42	147	60	223	54	512	31	832	40	166	51	215	42	366	50	157	50	109	51	40	76	219	42	231	53	668	35	4321	13
	indeterminable	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	0
	total	10850	10	2155	21	3356	19	6567	13	8827	11	4513	21	11177	12	8494	12	4108	17	3151	24	2118	27	7202	15	9761	12	7313	13	89593	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4760	13	137	68	381	49	602	38	576	35	1878	29	6501	15	1749	29	4808	18	1838	31	1913	31	6618	15	10646	13	5127	24	47533	6
	fir	5397	14	101	93	212	57	1046	31	228	39	962	36	3727	17	960	30	458	48	528	43	142	61	563	43	157	40	369	44	14851	8
	pine	239	37	164	53	55	100	0	.	63	46	0	.	515	92	0	.	215	53	183	66	179	80	1218	31	617	37	79	70	3527	20
	larch	0	.	0	.	7	100	21	100	45	86	0	.	15	100	98	100	60	72	0	.	22	100	3105	18	1887	20	3353	18	8614	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1203	28	1695	34	16	100	2924	23
	other conifers	125	48	35	100	0	.	31	100	52	75	0	.	53	58	0	.	0	.	87	100	46	100	0	.	121	77	44	70	593	28
	beech	3663	14	750	40	328	49	551	34	896	25	695	46	2430	19	1469	32	870	40	450	50	858	40	1017	42	600	50	2404	23	16980	8
	maple	1178	20	229	51	177	60	118	39	382	33	575	37	1185	31	460	42	205	36	81	81	319	50	393	41	278	44	217	44	5797	11
	ash	333	38	210	54	149	53	245	53	358	31	213	51	207	35	173	52	226	70	31	100	190	51	167	62	472	64	454	28	3429	14
	oak	502	54	127	55	55	65	162	53	161	39	21	100	61	78	36	65	9	100	61	75	35	100	231	82	55	100	430	39	1945	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	156	95	28	100	2190	26	2373	25
	other broadleaves	1253	22	106	54	306	52	164	60	807	29	292	51	516	26	736	41	904	38	269	50	408	50	1183	22	507	33	3234	23	10685	10
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	17449	9	1858	30	1671	31	2939	23	3568	19	4635	22	15210	11	5681	20	7764	16	3527	27	4112	23	15855	11	17064	11	17916	12	119249	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13529	8	2384	23	3661	21	8983	13	10277	11	10222	14	18735	10	12682	10	12927	11	6381	17	5215	18	19216	9	35488	7	13471	12	173170	3
	fir	14929	8	1020	30	1605	22	6616	13	3277	14	3088	21	10047	11	3805	15	1833	21	2098	22	587	27	1532	24	634	26	1637	24	52707	4
	pine	1160	24	499	28	260	38	497	33	655	22	0	.	883	57	37	55	215	53	538	43	179	80	4670	21	4745	19	338	53	14674	10
	larch	21	58	127	51	671	64	92	48	632	36	140	71	67	81	135	74	470	41	0	.	326	59	8371	11	5596	11	8507	12	25155	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1791	22	3947	20	65	79	5813	15
	other conifers	419	40	293	49	47	72	349	28	686	37	0	.	75	46	170	40	249	81	144	72	82	64	173	56	644	34	44	70	3376	15
	beech	17420	7	6427	12	4115	18	6656	10	9306	9	2180	24	8808	11	6273	13	3191	20	1932	24	2589	23	2313	27	1669	23	12725	12	85602	3
	maple	5494	12	1772	19	1108	34	1391	19	3635	13	1234	24	2615	20	2700	17	2794	18	759	27	1317	24	1958	25	778	29	1781	26	29336	5
	ash	1844	17	1153	22	1300	23	1154	20	2348	14	832	32	1129	34	1430	20	976	28	804	33	444	31	1405	28	778	43	2433	22	18030	6
	oak	1948	32	544	30	1141	31	546	24	870	17	88	56	125	51	52	50	176	65	67	68	35	100	1142	32	91	72	2180	24	9004	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	238	67	45	72	10448	13	10739	13
	other broadleaves	3389	15	1128	19	1489	23	2283	20	3894	14	1388	28	1888	18	1803	23	2416	21	1269	37	915	34	5160	14	4819	21	16738	10	48580	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
	total	60153	4	15346	10	15398	11	28567	8	35579	6	19171	11	44373	6	29086	8	25264	9	13991	13	11689	13	47968	6	59235	5	70366	4	476187	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. 1283958. Birmensdorf, Swiss Federal Research Institute WSL

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