Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI3
number of stems
development stage · main tree species
regional demarcation: economic region
unit: n/ha
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/07
		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	21	88	0	.	39	70	41	59	0	.	117	30	42	43	164	76	73	59	80	.	63	58	5	85	44	50	55	73	59	24
	fir	28	88	0	.	0	.	39	42	0	.	27	94	12	69	31	62	6	93	0	.	0	.	0	.	0	.	0	.	13	36
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	91	0	.	0	.	0	.	3	92	21	47	2	52
	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	17	88	6	84	28	55	56	86	32	46	8	97	21	75	25	93	90	93	0	.	0	.	0	.	3	90	51	86	25	34
	maple	0	.	0	.	0	.	0	.	0	.	0	.	6	66	5	93	3	93	0	.	10	90	0	.	0	.	0	.	2	46
	ash	0	.	0	.	0	.	16	92	3	93	5	96	4	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	56
	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	13	86	0	.	0	.	0	.	15	92	0	.	0	.	22	59	0	.	0	.	10	90	0	.	0	.	0	.	4	45
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	79	71	6	84	67	50	151	43	50	53	156	27	85	40	246	54	176	53	80	.	83	57	5	85	51	40	127	47	107	17
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12	40	23	50	49	69	6	62	39	42	66	46	65	40	28	51	48	72	65	51	61	89	29	37	87	28	23	93	37	15
	fir	17	51	5	98	5	95	8	61	9	48	13	87	54	48	5	66	6	96	0	.	36	48	0	.	5	97	0	.	11	23
	pine	3	98	0	.	3	95	3	98	5	56	0	.	0	.	0	.	0	.	0	.	0	.	68	97	9	97	0	.	8	71
	larch	0	.	0	.	3	95	0	.	1	98	0	.	0	.	0	.	0	.	5	87	0	.	43	37	8	50	10	63	5	32
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	74	3	97	0	.	1	65
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	97	0	.	0	.	1	100
	beech	24	42	46	23	48	67	48	29	35	25	0	.	8	66	12	52	12	66	0	.	30	60	4	97	0	.	61	80	26	16
	maple	12	48	8	67	20	73	7	61	5	51	13	87	0	.	9	56	0	.	0	.	10	90	1	97	0	.	5	94	6	23
	ash	7	84	8	83	7	96	8	59	6	86	24	88	0	.	4	97	9	95	0	.	10	90	0	.	0	.	0	.	5	30
	oak	2	98	4	73	2	95	1	98	3	58	0	.	0	.	0	.	0	.	0	.	0	.	19	97	0	.	6	94	3	53
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	90	49	4	60
	other broadleaves	13	56	16	53	24	52	4	98	14	59	0	.	1	97	25	63	22	52	0	.	0	.	6	66	13	68	31	78	13	21
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	90	24	110	19	162	39	85	25	117	18	116	41	128	25	83	35	98	37	70	44	147	32	194	35	126	18	226	22	122	8
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	283	29	301	49	287	34	320	17	349	17	459	28	336	27	453	21	203	32	208	35	159	37	169	37	322	19	22	52	243	8
	fir	78	37	27	95	50	43	70	33	48	48	96	65	41	72	90	44	19	97	30	90	0	.	7	79	10	57	8	66	36	17
	pine	2	73	65	63	7	65	34	66	10	36	0	.	21	97	0	.	0	.	0	.	0	.	104	38	88	36	0	.	30	22
	larch	2	99	9	64	35	71	6	58	7	68	0	.	12	97	17	97	54	44	0	.	0	.	85	28	35	42	48	36	30	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	99	7	85	0	.	2	67
	other conifers	6	69	27	54	15	96	8	56	9	49	0	.	0	.	0	.	3	97	0	.	0	.	2	99	21	54	1	99	7	26
	beech	163	34	42	34	88	43	84	21	73	26	27	66	48	57	52	36	76	88	4	90	85	93	30	67	2	99	123	29	74	14
	maple	54	30	20	64	33	53	36	37	92	24	43	61	21	45	43	50	99	38	53	81	81	42	13	37	10	64	16	37	39	12
	ash	32	49	80	56	40	39	18	39	61	28	43	71	20	55	86	45	23	60	142	39	44	68	19	54	7	56	22	37	34	14
	oak	44	66	55	95	99	65	3	59	10	53	15	95	4	96	0	.	19	97	0	.	0	.	30	48	0	.	40	46	23	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	69	0	.	185	23	38	25
	other broadleaves	35	45	29	55	55	42	69	44	25	28	65	64	52	40	68	53	69	53	204	43	230	41	161	22	175	31	227	17	118	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	697	11	656	17	709	12	648	8	685	7	747	15	556	14	809	11	565	16	642	20	599	23	632	10	677	8	693	7	673	3
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	157	19	178	43	106	42	209	22	164	22	265	41	457	25	193	28	419	23	69	44	505	38	170	23	298	26	62	35	193	8
	fir	89	20	45	79	58	79	68	37	21	29	101	45	36	42	63	39	16	69	208	37	28	65	25	60	18	77	4	81	46	13
	pine	23	45	13	41	31	96	6	49	15	45	0	.	5	98	0	.	0	.	0	.	0	.	93	32	119	44	0	.	26	21
	larch	0	.	2	98	0	.	2	75	13	58	0	.	0	.	0	.	0	.	0	.	38	71	108	20	50	51	24	39	22	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	65	7	98	0	.	2	57
	other conifers	0	.	4	98	0	.	12	46	3	99	0	.	0	.	3	98	0	.	0	.	5	94	1	99	0	.	0	.	2	34
	beech	231	12	273	20	118	48	142	21	97	22	126	51	131	50	231	26	87	51	263	33	294	35	34	61	22	64	129	28	147	8
	maple	29	28	14	49	8	96	5	63	41	28	47	41	38	35	82	31	125	49	98	36	88	63	4	75	12	75	4	52	28	14
	ash	29	35	51	53	100	42	20	34	35	26	65	51	83	65	74	31	21	94	106	62	177	76	8	58	21	74	41	44	43	16
	oak	31	56	12	58	126	44	7	56	6	54	8	65	3	98	3	97	0	.	0	.	0	.	6	51	0	.	30	34	18	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	99	0	.	139	24	23	26
	other broadleaves	30	36	43	44	58	71	36	36	47	33	18	77	17	55	72	51	17	66	52	70	157	57	40	41	51	38	180	18	66	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	620	6	635	11	604	14	509	8	441	8	631	19	770	14	721	11	683	14	797	15	1293	20	512	10	598	12	614	6	616	3
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	126	11	47	26	112	31	99	25	98	14	253	15	177	19	192	16	218	17	431	24	93	25	275	14	343	9	130	20	172	5
	fir	94	13	46	30	65	50	86	25	37	27	54	39	91	22	52	26	41	31	69	46	33	53	24	49	4	53	18	41	54	8
	pine	12	29	16	36	14	53	5	80	16	28	0	.	9	69	2	69	4	98	53	66	0	.	20	41	46	34	3	99	14	16
	larch	0	.	3	62	2	55	2	99	4	51	0	.	0	99	1	99	6	91	0	.	0	.	46	27	37	26	80	29	15	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	69	13	47	0	.	2	42
	other conifers	4	53	1	99	0	.	1	99	2	74	0	.	0	.	1	99	0	.	0	.	0	.	1	99	0	.	0	.	1	37
	beech	153	10	201	12	154	17	149	17	99	13	56	35	88	25	154	23	64	28	60	92	149	24	17	75	12	42	60	35	104	6
	maple	30	13	41	25	2	74	17	45	27	27	37	44	24	23	45	33	34	28	14	95	65	41	19	57	1	99	3	99	24	9
	ash	17	32	31	40	30	34	32	54	28	17	6	98	17	43	43	33	26	44	2	95	29	45	1	99	0	.	6	49	19	12
	oak	10	52	11	53	25	62	14	39	8	34	2	97	0	.	3	99	0	.	0	.	0	.	1	99	0	.	8	43	6	23
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	41	33	3	35
	other broadleaves	18	18	19	33	29	54	14	49	21	35	43	46	16	40	17	42	40	41	25	67	11	60	32	43	2	57	41	30	22	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	463	5	416	8	433	12	419	9	341	6	451	12	423	10	510	10	434	9	655	11	379	10	440	9	458	7	390	7	436	2
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	75	13	37	35	121	18	86	15	69	12	224	17	141	11	186	11	166	18	208	21	177	25	156	17	220	10	144	16	137	4
	fir	89	12	36	57	37	31	99	15	37	18	61	39	134	12	62	17	72	27	78	40	36	41	14	66	7	43	25	51	61	6
	pine	3	39	3	70	8	61	7	37	10	28	0	.	4	100	1	58	0	.	12	98	0	.	10	86	7	38	2	99	5	20
	larch	0	70	0	.	1	98	0	100	1	61	0	.	0	.	1	60	8	48	0	.	5	62	94	20	37	19	28	23	13	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	42	10	40	0	.	2	30
	other conifers	1	70	0	.	2	98	1	70	2	80	0	.	0	100	1	70	0	.	0	.	0	.	0	.	0	.	0	.	1	37
	beech	87	10	130	15	79	23	98	12	114	13	52	41	137	14	94	15	68	30	97	27	62	30	2	99	17	34	40	37	80	5
	maple	28	25	27	37	17	51	8	42	17	28	10	48	14	30	18	26	8	52	15	37	29	40	14	58	4	48	1	99	14	11
	ash	11	34	17	33	44	35	17	28	20	26	15	84	6	61	7	40	6	90	17	51	5	66	3	72	2	83	0	.	11	13
	oak	6	41	12	40	32	41	8	46	15	25	0	.	0	.	1	70	0	.	2	75	0	.	1	99	1	99	1	74	5	17
	sweet chestnut	0	.	0	.	6	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	38	2	38
	other broadleaves	27	32	9	49	30	56	9	36	25	35	3	97	6	48	10	39	30	40	11	46	18	61	10	46	7	56	20	39	15	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	326	6	272	10	376	8	334	7	310	7	365	12	443	6	380	6	357	10	440	12	331	16	316	8	312	6	293	8	347	2
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	106	13	2	95	55	29	64	46	118	28	190	30	240	13	184	26	233	12	282	31	310	24	190	14	227	11	94	21	174	5
	fir	140	14	4	63	40	46	136	44	31	42	116	42	83	17	55	40	20	66	94	56	34	68	12	50	6	47	11	62	49	10
	pine	11	53	76	50	22	82	2	95	3	74	0	.	17	98	0	.	9	55	17	70	0	.	52	28	27	33	6	73	19	19
	larch	0	99	0	.	0	.	0	.	2	98	0	.	0	.	0	.	2	99	0	.	0	.	81	25	34	19	56	23	25	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	29	36	51	26	1	99	12	21
	other conifers	2	74	40	81	0	.	24	66	35	69	0	.	1	100	3	97	0	.	19	95	10	97	0	.	6	64	0	.	4	31
	beech	91	17	155	51	102	44	103	45	102	20	89	69	86	24	56	34	33	40	51	65	94	53	33	46	22	46	106	21	70	10
	maple	23	23	41	40	45	51	12	95	39	39	41	33	34	52	38	39	36	40	0	.	35	52	5	64	4	47	4	66	19	16
	ash	5	52	22	95	34	45	14	43	68	46	5	68	9	54	70	41	5	78	10	63	15	97	15	84	16	71	5	49	14	22
	oak	11	57	72	62	2	94	26	66	10	57	0	.	3	69	0	.	4	70	12	63	0	.	4	74	2	100	12	48	7	23
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	1	100	74	25	12	26
	other broadleaves	15	33	129	32	21	82	26	69	31	43	19	46	23	28	26	43	46	39	36	63	19	61	26	34	11	32	65	19	31	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	406	6	541	29	320	14	408	13	441	15	460	22	496	7	430	12	389	10	522	20	517	15	448	8	408	6	434	7	437	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	122	8	84	26	121	15	132	11	137	10	235	11	203	8	206	9	203	9	238	14	205	17	178	9	255	6	82	10	164	3
	fir	94	7	32	28	43	25	79	11	34	16	74	21	91	9	58	13	34	20	79	24	28	26	15	27	7	24	12	26	48	4
	pine	10	22	19	29	12	36	10	39	11	16	0	.	9	56	1	48	3	49	17	49	0	.	55	19	38	18	2	54	15	9
	larch	0	55	2	42	5	59	2	42	4	31	0	.	1	91	2	74	10	34	0	99	6	63	79	11	35	12	46	14	18	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	16	25	20	20	0	100	4	15
	other conifers	2	35	7	47	2	78	5	30	6	37	0	.	0	76	1	44	0	100	3	99	2	76	1	57	4	42	0	100	2	15
	beech	134	7	155	10	99	13	104	8	92	8	61	28	99	11	107	12	56	19	80	23	113	19	23	29	15	23	95	13	86	3
	maple	29	10	25	17	15	27	13	22	36	14	30	21	22	24	34	16	38	20	20	31	48	23	10	26	4	29	6	27	21	5
	ash	16	18	32	25	39	19	19	20	33	14	19	33	15	32	35	19	13	29	29	33	38	50	9	42	7	45	15	27	20	7
	oak	15	28	19	35	42	28	8	24	10	18	3	57	1	52	1	61	3	68	3	53	0	.	9	34	1	74	19	26	10	11
	sweet chestnut	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	1	65	0	100	100	13	12	13
	other broadleaves	22	14	30	20	33	26	23	25	26	15	23	30	16	18	27	23	38	21	35	32	55	33	48	17	32	24	111	11	39	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	444	3	404	7	414	7	395	5	387	4	446	8	457	4	472	5	400	6	505	8	495	11	442	4	419	4	489	4	438	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1283288. Birmensdorf, Swiss Federal Research Institute WSL

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