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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
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	47	100	0	.	0	.	2	100	-18	100	258	69	21	96	106	99	125	73	0	.	0	0	6	100	211	43	205	91	961	32
	fir	0	.	0	.	0	.	0	.	0	.	127	84	0	0	10	100	0	.	0	.	0	.	0	.	0	.	0	.	137	78
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0
	larch	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	71	135	50	83	100	276	41
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	0	0	.	27	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	27	74	0	.	-15	138	0	.	18	100	14	100	11	100	3	100	0	.	0	.	0	.	0	.	24	100	0	.	82	55
	maple	13	100	0	.	0	.	0	.	0	.	5	100	12	100	13	100	0	.	0	.	0	.	0	.	0	.	0	.	44	52
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	oak	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	1	100	0	.	0	.	4	100	0	.	6	100	0	.	3	72	0	.	0	.	0	.	0	.	0	.	0	.	14	53
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	88	76	0	.	6	404	6	100	0	100	410	56	44	70	139	86	125	73	0	.	0	0	80	70	370	36	288	94	1555	27
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	69	76	92	80	36	97	245	65	409	40	319	39	526	35	214	61	461	61	140	82	153	73	178	49	643	48	145	70	3630	16
	fir	130	83	0	.	99	66	121	58	179	56	106	93	170	70	103	59	53	71	0	.	69	73	15	100	9	100	1	100	1055	24
	pine	47	100	0	.	0	.	16	100	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	67	83	0	.	135	55
	larch	0	.	0	.	0	.	0	.	72	71	0	.	16	100	0	.	114	74	0	.	0	0	250	52	60	71	4	100	516	33
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	3	100	0	.	1	100	0	.	0	.	0	.	0	.	5	100	0	.	0	.	9	68
	beech	120	47	259	60	57	48	445	38	395	29	26	100	46	55	179	48	1	100	22	100	0	.	2	100	0	.	13	100	1564	18
	maple	8	78	7	72	11	75	31	79	91	78	20	71	50	58	36	88	22	100	10	100	67	59	-6	240	1	100	20	100	368	28
	ash	5	70	34	116	25	97	1	100	39	70	28	85	5	77	17	100	8	100	0	.	8	100	14	100	0	.	0	.	184	35
	oak	12	100	0	.	0	.	1	100	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	32	71
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	16	100
	other broadleaves	24	51	5	60	99	97	16	47	42	44	27	67	12	80	18	100	16	73	0	.	20	46	24	77	16	71	15	90	335	32
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	414	45	397	53	327	43	876	32	1252	23	527	36	827	32	567	41	676	47	172	68	318	53	482	39	796	41	214	64	7845	11
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1055	30	445	46	546	39	1197	25	2326	22	507	49	718	38	863	35	601	39	337	57	107	58	670	37	1012	25	263	39	10648	9
	fir	623	39	20	100	108	58	509	35	423	40	51	81	509	37	300	44	73	71	9	100	71	100	87	80	45	71	84	84	2912	15
	pine	68	56	43	100	17	100	89	73	156	45	0	.	5	100	43	100	0	.	0	.	0	.	439	50	338	36	15	100	1213	23
	larch	12	100	9	100	121	59	4	100	51	54	10	100	14	100	0	.	98	59	0	.	0	.	428	31	235	44	365	37	1346	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	98	61	98	0	.	138	70
	other conifers	-2	374	36	77	1	100	56	56	50	61	0	.	0	.	0	.	8	100	0	.	0	.	8	85	28	48	50	100	235	32
	beech	478	36	311	43	233	35	271	35	731	41	19	79	263	48	107	58	9	100	7	100	147	64	179	68	1	100	754	30	3511	14
	maple	194	33	86	42	26	83	68	49	515	32	79	68	51	45	50	74	90	46	42	91	8	71	75	46	54	66	133	41	1471	15
	ash	183	43	87	50	45	65	133	62	259	32	56	65	35	62	114	53	30	69	77	59	17	72	86	48	19	72	191	35	1331	14
	oak	215	60	87	100	63	76	21	94	38	58	0	.	0	.	0	.	25	100	0	.	0	.	34	57	0	.	296	39	780	26
	sweet chestnut	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	79	0	.	1503	23	1537	23
	other broadleaves	111	42	89	45	91	47	194	42	185	32	8	100	66	49	116	53	126	71	71	69	56	67	396	24	511	34	1458	19	3478	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	2935	19	1212	27	1251	29	2543	19	4734	16	730	40	1662	26	1593	28	1061	29	543	42	405	47	2512	20	2306	18	5113	12	28600	6
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1996	27	590	43	690	47	1236	29	1648	27	1219	42	1468	32	1592	32	669	35	120	45	852	43	1034	41	1782	28	307	44	15203	10
	fir	651	24	263	58	299	65	516	40	221	52	426	58	263	57	541	40	18	68	316	63	43	63	0	.	86	71	171	65	3815	15
	pine	486	50	155	45	126	70	6	100	130	49	0	.	16	100	14	100	0	.	37	100	0	.	608	36	562	47	0	.	2140	21
	larch	10	100	30	81	0	.	15	73	133	62	0	.	0	.	0	.	0	.	0	.	38	77	427	40	567	32	715	41	1934	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	77	212	45	0	.	228	42
	other conifers	0	.	72	82	103	100	133	83	17	75	0	.	0	.	7	81	0	.	0	.	2	100	1	100	1	100	0	.	337	48
	beech	2771	19	794	31	84	54	808	30	773	28	255	61	244	61	624	34	196	57	356	48	180	44	102	58	37	62	1611	26	8835	10
	maple	247	31	88	53	30	71	131	40	367	33	262	45	245	62	291	45	159	53	68	61	190	49	12	100	19	100	67	58	2175	14
	ash	350	39	128	50	288	61	174	37	682	30	176	65	278	73	400	41	18	100	109	78	69	72	10	85	40	90	251	56	2972	15
	oak	316	46	169	61	432	43	186	44	61	52	34	74	6	100	0	.	17	73	10	100	0	.	30	50	0	.	219	33	1480	19
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	26	100	0	.	797	31	829	30
	other broadleaves	270	46	86	51	28	59	227	48	390	41	14	55	11	72	56	95	25	69	47	64	14	73	136	58	116	58	1087	27	2507	16
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0
	total	7097	15	2375	22	2080	29	3433	21	4428	18	2387	29	2531	25	3524	22	1103	31	1063	41	1388	36	2402	23	3422	20	5223	15	42455	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4039	16	872	32	662	51	1456	33	3259	20	2489	26	1900	28	4077	20	1540	31	1949	37	1354	40	2432	24	7535	15	2109	26	35674	6
	fir	3673	18	192	47	504	55	797	37	1308	25	977	39	1030	30	1687	24	320	47	483	51	308	66	446	55	167	69	287	51	12180	9
	pine	389	39	244	50	163	71	174	48	761	27	0	.	170	63	31	100	0	.	143	75	0	.	239	46	543	38	17	100	2874	15
	larch	0	.	15	73	25	100	81	82	280	48	0	.	0	.	77	74	14	100	0	.	9	100	661	33	1237	25	904	34	3303	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	409	36	0	.	409	36
	other conifers	10	89	0	.	0	.	13	100	52	84	0	.	0	.	9	71	0	.	0	.	0	.	0	.	0	.	0	.	84	56
	beech	4328	14	2550	21	1626	29	1411	26	2471	18	392	39	1298	33	2095	22	728	39	180	73	465	41	128	83	263	53	761	35	18696	7
	maple	522	19	276	39	22	72	69	51	419	25	62	62	73	50	504	27	174	40	43	72	101	49	13	73	16	72	2	100	2295	11
	ash	524	37	243	42	506	42	639	71	932	22	68	71	229	70	707	35	228	56	18	58	162	55	0	0	2	100	120	78	4376	15
	oak	187	38	177	57	150	56	308	40	540	30	0	.	28	100	0	0	0	.	0	.	0	.	5	100	59	100	92	75	1546	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	547	55	554	54
	other broadleaves	161	34	240	45	152	66	137	56	209	33	33	69	74	45	67	49	127	73	13	76	123	64	52	58	6	100	203	52	1597	16
	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	13833	10	4808	18	3810	23	5084	20	10231	13	4021	24	4801	19	9255	15	3139	23	2830	30	2522	30	3978	21	10236	13	5042	17	83589	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4704	16	1070	37	2138	27	4460	20	4787	18	3655	25	8260	15	8257	17	3444	25	2351	29	2519	31	4430	20	8990	14	4022	22	63086	5
	fir	4816	17	676	36	964	38	4625	18	2736	21	666	42	5268	16	2231	27	1229	33	708	51	391	61	115	77	701	39	350	65	25475	7
	pine	169	48	28	74	182	74	414	36	363	39	0	.	294	100	25	76	0	.	0	.	0	.	179	86	360	40	0	.	2015	22
	larch	63	75	15	100	49	71	37	100	33	100	0	.	0	.	97	71	68	67	0	.	132	71	3022	21	2614	24	1186	31	7315	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	230	53	119	61	0	.	349	41
	other conifers	3	100	4	100	185	100	237	78	293	100	0	.	71	100	-11	128	0	.	0	.	0	.	0	.	0	.	0	.	782	51
	beech	3873	15	2154	23	1694	29	3759	17	4496	17	558	43	3066	23	2349	26	1024	35	522	46	811	38	27	100	625	47	689	43	25648	7
	maple	506	24	238	45	48	66	85	43	433	33	64	55	280	30	357	33	114	52	113	49	138	59	68	62	36	117	0	.	2480	12
	ash	216	45	201	48	567	44	635	38	1257	26	152	80	208	45	313	52	34	61	218	62	35	112	29	86	38	100	0	.	3903	14
	oak	290	50	93	86	450	56	196	40	966	40	0	.	0	.	42	74	0	.	16	100	0	.	0	0	0	.	14	100	2066	24
	sweet chestnut	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	384	56	385	56
	other broadleaves	66	45	94	66	271	94	137	47	266	38	19	62	97	53	129	44	434	63	39	95	62	70	8	80	10	87	164	64	1795	23
	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	14705	11	4573	21	6548	19	14586	12	15631	11	5114	22	17546	12	13787	14	6347	19	3966	26	4088	26	8107	15	13494	12	6807	16	135299	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5158	12	5	72	677	39	814	36	867	29	1634	27	6063	14	1246	29	4754	20	1139	31	1316	33	4904	15	6278	15	3914	20	38768	6
	fir	4495	16	96	73	165	63	1188	32	399	39	692	47	3181	19	1042	30	739	41	311	46	107	62	624	39	332	37	846	40	14218	9
	pine	309	33	114	65	169	86	0	.	135	59	0	.	147	100	0	.	39	59	41	92	0	.	529	28	658	41	131	60	2274	18
	larch	0	.	0	.	11	100	49	100	61	71	0	.	0	.	30	100	21	100	0	.	0	.	3327	21	967	21	1562	18	6028	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	473	28	540	30	5	100	1011	21
	other conifers	4	523	181	98	0	.	109	92	21	100	0	.	5	85	0	.	0	.	6	100	-16	100	1	100	10	73	0	.	322	64
	beech	3890	15	418	42	330	43	602	36	946	27	339	58	2443	20	820	34	448	41	188	59	218	47	484	45	339	44	1590	20	13057	8
	maple	765	22	75	68	60	52	25	150	216	62	124	44	572	25	182	59	299	42	24	100	165	61	100	58	130	51	60	69	2798	12
	ash	374	32	309	54	428	40	260	45	351	37	9	86	168	44	361	44	208	60	15	100	87	86	125	60	117	60	93	49	2905	14
	oak	167	60	180	79	61	77	181	62	251	36	9	100	41	88	8	78	9	100	65	71	0	.	106	66	72	100	273	43	1422	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	18	100	2023	22	2071	22
	other broadleaves	233	25	56	77	277	49	190	89	243	53	33	54	132	32	219	65	190	46	37	62	54	60	213	44	166	38	905	21	2948	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
	total	15397	9	1433	32	2179	26	3417	25	3492	20	2840	24	12751	11	3908	23	6700	18	1827	28	1931	28	10917	12	9627	12	11403	11	87821	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17067	7	3073	19	4750	17	9409	12	13279	9	10081	13	18956	8	16354	10	11593	12	6036	18	6301	17	13654	10	26451	7	10966	11	167969	2
	fir	14388	8	1246	25	2139	24	7756	13	5266	13	3046	21	10421	10	5914	13	2433	22	1827	27	989	33	1287	28	1341	24	1738	25	59792	4
	pine	1468	21	584	28	657	37	699	26	1550	18	0	.	632	55	113	51	39	59	221	54	0	.	1995	19	2528	19	164	50	10650	8
	larch	85	59	68	46	219	39	185	49	629	28	10	100	30	71	204	46	315	36	0	.	179	55	8161	12	5815	13	4818	13	20718	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	823	24	1342	19	5	100	2164	15
	other conifers	16	170	292	65	290	73	549	44	436	68	0	.	77	93	5	306	8	100	6	100	-13	118	15	58	39	39	50	100	1770	27
	beech	15485	6	6485	12	4010	17	7298	11	9829	9	1604	23	7372	12	6177	13	2407	20	1275	26	1820	21	922	30	1290	28	5418	12	71394	3
	maple	2255	10	770	22	196	29	408	22	2041	15	616	24	1284	18	1433	17	860	21	301	29	668	25	262	31	256	35	281	29	11631	5
	ash	1651	17	1003	23	1858	22	1841	29	3520	13	489	36	924	31	1915	19	527	35	437	38	378	35	263	34	215	41	655	28	15676	7
	oak	1187	23	707	33	1165	28	892	22	1876	23	43	63	74	61	50	63	51	57	91	54	0	.	176	42	130	71	894	21	7336	10
	sweet chestnut	0	.	0	.	2	100	0	.	6	100	0	.	0	.	0	.	7	100	0	.	0	.	90	53	18	100	5269	12	5391	12
	other broadleaves	867	18	570	25	917	35	905	26	1335	18	141	27	392	21	608	29	918	34	207	35	329	31	829	19	825	24	3831	11	12674	6
	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	54468	3	14798	9	16202	10	29944	7	39767	5	16030	11	40163	6	32773	7	19151	10	10401	14	10651	14	28478	7	40250	6	34090	4	387165	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. 1231024. Birmensdorf, Swiss Federal Research Institute WSL

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