Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI3
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 2004/06
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	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	34	100	0	0	134	99	131	89	0	0	238	61	233	53	169	66	173	62	130	100	126	96	-4	100	133	67	143	94	1640	24
	fir	85	100	0	.	0	.	39	58	0	.	111	100	5	71	46	91	30	100	0	.	0	.	0	.	0	.	0	0	317	47
	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	.	39	100	0	.	0	0	0	0	32	100	86	76	156	53
	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	14	100	11	100	24	63	43	76	109	59	8	100	25	92	6	100	96	100	0	.	0	.	0	.	18	100	21	100	374	34
	maple	0	.	0	.	0	.	0	.	0	.	0	.	26	77	18	100	10	100	0	.	22	100	0	.	0	.	0	.	77	48
	ash	0	.	0	.	0	.	5	100	30	100	2	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	81
	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	5	100	0	.	0	.	0	.	7	100	0	.	0	.	16	91	0	.	0	.	2	100	0	.	0	.	0	.	30	56
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0
	total	138	96	11	100	158	92	218	65	146	60	358	53	291	46	254	56	348	61	130	100	150	95	-4	100	183	53	250	81	2631	20
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	103	70	85	57	98	63	77	63	274	53	47	93	271	50	74	81	323	89	147	81	90	100	109	71	503	43	48	100	2248	21
	fir	62	52	10	100	2	100	69	66	207	72	3	100	68	134	77	74	17	100	0	.	142	62	0	0	4	100	0	.	660	32
	pine	47	100	0	.	24	100	68	100	151	63	0	.	0	.	0	.	0	.	0	.	0	.	29	100	40	100	0	.	359	38
	larch	0	.	0	.	22	100	0	.	21	100	0	.	0	.	0	.	0	.	22	100	0	.	310	47	138	51	52	94	564	31
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	99	6	100	0	.	66	91
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	13	100
	beech	160	48	818	33	109	67	334	37	428	38	0	.	11	74	30	93	71	97	0	.	92	99	1	100	0	.	65	63	2119	18
	maple	33	60	53	78	8	72	14	61	54	51	8	100	0	.	15	69	0	.	0	.	2	100	8	100	0	.	2	100	197	29
	ash	60	77	66	76	26	100	70	88	26	90	21	100	0	.	1	100	4	100	0	.	3	100	0	.	0	.	0	.	278	36
	oak	12	100	96	75	20	100	17	100	68	59	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	2	100	233	38
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	90	63	90	63
	other broadleaves	35	66	17	75	72	80	9	100	26	60	0	.	8	100	16	64	13	68	0	.	0	.	5	77	21	84	13	80	235	30
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	511	36	1144	31	383	50	655	36	1255	28	79	83	359	53	215	69	428	70	169	72	328	64	553	34	712	34	273	42	7064	12
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	852	37	256	55	349	47	1188	24	2480	23	845	37	858	38	1075	35	441	38	53	67	160	50	921	41	1326	26	109	42	10914	10
	fir	483	46	36	100	79	50	289	40	256	42	188	59	160	57	302	42	20	100	5	100	0	.	20	64	84	54	265	64	2187	17
	pine	39	86	88	69	15	86	49	69	204	42	0	.	11	100	0	.	0	.	0	.	0	.	386	45	340	38	0	.	1131	22
	larch	11	100	10	76	50	76	50	91	57	59	0	.	8	100	22	100	59	75	0	.	0	.	285	30	258	45	285	33	1095	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	84	100	47	97	0	.	131	73
	other conifers	6	71	98	74	30	100	23	61	28	56	0	.	0	.	0	.	6	100	0	.	0	.	7	100	16	56	42	100	257	36
	beech	444	40	33	47	99	57	244	34	506	41	9	75	69	58	108	47	234	94	6	100	28	100	154	61	3	100	977	31	2915	17
	maple	78	41	21	70	36	58	112	42	382	29	63	68	19	134	70	78	63	52	55	83	37	61	42	47	29	67	100	45	1107	15
	ash	112	62	27	80	37	54	85	56	453	42	38	71	24	58	236	45	19	64	49	60	16	71	64	54	13	58	109	45	1283	19
	oak	70	71	84	100	103	70	22	74	52	63	19	100	4	100	0	.	12	100	0	.	0	.	88	71	0	.	208	44	662	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	76	0	.	1036	30	1066	29
	other broadleaves	45	37	-2	497	78	49	203	46	86	37	44	72	40	54	107	64	134	77	74	67	46	73	354	25	385	34	1135	21	2728	12
	indeterminable	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0
	total	2142	24	651	36	875	32	2266	19	4503	17	1205	31	1192	33	1919	26	989	36	243	50	288	44	2435	21	2501	19	4267	14	25476	6
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2374	20	1073	35	293	44	1547	31	1482	25	811	48	1771	31	1203	38	506	38	39	69	1381	56	1409	26	1174	32	683	39	15747	10
	fir	1329	28	192	75	244	80	450	41	287	32	315	49	216	55	471	41	48	80	171	58	18	82	181	68	48	92	21	93	3990	15
	pine	533	43	113	47	91	100	102	47	243	48	0	.	19	100	0	.	0	.	0	.	0	.	556	36	352	50	0	.	2009	19
	larch	0	.	21	100	0	.	32	71	166	53	0	.	0	.	0	.	0	.	0	.	34	84	901	27	335	50	317	44	1806	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	116	69	20	100	0	.	135	61
	other conifers	0	.	13	100	0	.	256	62	3	100	0	.	0	.	6	100	0	.	0	.	2	100	2	100	0	.	0	.	282	56
	beech	3209	17	1294	29	330	63	1041	30	696	27	270	45	313	62	1008	35	101	61	216	55	429	55	219	65	49	69	1069	30	10244	10
	maple	341	27	43	65	4	100	57	70	282	34	96	58	124	42	284	35	131	59	44	54	182	57	14	73	7	76	21	62	1630	14
	ash	467	33	185	76	445	59	178	44	560	32	244	63	301	75	407	38	43	100	93	90	467	89	55	72	50	65	196	63	3690	18
	oak	347	45	55	57	405	47	162	53	73	48	13	79	6	100	5	100	0	.	0	.	0	.	44	78	0	.	253	37	1364	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	100	0	.	1355	27	1383	27
	other broadleaves	314	42	142	50	73	91	181	45	315	42	13	74	41	80	203	57	6	73	22	71	257	83	173	59	142	48	1390	23	3271	15
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0
	total	8912	13	3132	23	1884	31	4006	21	4109	18	1761	31	2791	25	3588	24	835	36	584	49	2770	43	3696	18	2176	24	5306	14	45551	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6248	14	959	30	1506	34	2114	27	3671	19	2629	24	3508	23	3252	21	3342	24	1650	40	813	38	3889	20	6445	15	2491	25	42519	6
	fir	4161	16	651	40	529	50	1666	29	1614	25	575	41	1348	26	1045	27	541	34	177	73	240	56	416	51	204	58	436	41	13602	9
	pine	494	28	299	41	272	60	122	76	816	37	0	.	189	62	63	73	72	100	153	75	0	.	224	51	1180	33	61	100	3944	15
	larch	0	.	61	59	51	60	52	100	287	55	0	.	15	100	55	100	21	75	0	.	0	.	977	27	1205	26	1314	30	4039	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	71	245	49	0	.	285	43
	other conifers	13	57	3	100	0	.	73	100	7	83	0	.	0	.	4	100	0	.	0	.	0	.	1	100	0	0	0	.	101	73
	beech	5906	12	2851	20	1855	26	2094	24	2456	18	328	43	878	27	2111	29	816	34	98	85	744	37	125	83	283	46	567	32	21112	7
	maple	1055	18	382	28	13	79	165	43	516	28	181	48	264	32	410	41	220	35	17	100	223	42	101	55	6	100	40	100	3592	10
	ash	763	37	266	40	386	44	745	67	945	22	23	100	247	57	566	40	315	43	9	100	195	52	12	100	0	.	133	76	4606	16
	oak	205	31	136	66	277	57	272	42	346	35	20	100	0	.	32	100	0	.	0	.	0	.	17	100	0	.	192	57	1495	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	507	36	512	35
	other broadleaves	234	24	223	38	217	49	125	64	341	43	56	40	66	51	182	52	179	58	12	78	68	78	169	48	8	61	370	36	2250	14
	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	19078	9	5832	17	5106	19	7429	18	10998	13	3811	21	6515	17	7721	17	5512	19	2115	34	2283	27	5970	17	9577	13	6110	15	98057	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4867	16	844	41	2929	27	5672	18	5384	17	3340	26	8498	15	10299	14	2903	25	3085	25	2755	29	3691	21	11235	13	4257	20	69760	5
	fir	4137	17	185	50	1102	37	4811	17	2401	22	815	43	6009	15	3329	19	1264	32	964	41	337	59	251	73	587	40	649	46	26841	7
	pine	230	40	35	71	196	70	433	34	776	27	0	.	268	100	58	68	0	.	66	100	0	.	163	85	499	35	38	100	2761	17
	larch	61	75	0	.	36	100	37	100	189	63	0	.	0	.	106	64	270	52	0	.	152	64	3115	20	2668	21	1247	28	7881	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	230	48	200	46	0	.	430	33
	other conifers	5	74	0	.	141	100	45	98	241	100	0	.	69	100	6	71	0	.	0	.	0	.	0	.	0	.	0	.	507	57
	beech	3728	15	1368	25	1119	34	3550	18	5179	15	455	43	4440	20	2916	21	878	34	613	36	634	40	25	100	580	45	499	41	25985	6
	maple	529	29	238	43	146	63	98	46	579	31	41	73	313	41	402	27	107	56	127	36	127	49	40	62	45	88	1	100	2793	12
	ash	285	36	256	45	543	41	755	33	1309	25	137	74	111	48	316	52	23	71	254	73	48	64	11	123	36	85	0	.	4085	14
	oak	410	40	164	59	503	49	379	48	1196	28	0	.	0	.	46	80	0	.	52	77	0	.	3	100	50	100	17	80	2819	18
	sweet chestnut	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	873	41	881	41
	other broadleaves	239	39	112	63	273	68	106	57	365	30	5	100	117	50	123	45	467	56	60	58	73	59	32	54	66	57	59	44	2096	18
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0
	total	14492	11	3202	23	6997	18	15885	12	17619	10	4793	23	19826	11	17602	12	5912	19	5221	21	4126	25	7560	15	15968	11	7639	15	146841	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2970	18	25	100	248	59	204	59	858	31	1394	25	4704	15	961	30	3208	23	609	45	841	37	2886	18	4921	15	2290	24	26119	6
	fir	3083	19	52	72	69	58	753	49	365	43	761	48	1915	22	402	41	402	55	192	71	220	76	211	49	245	47	229	66	8899	11
	pine	160	52	218	58	171	85	15	100	31	71	0	.	107	82	0	.	36	59	35	94	0	.	553	26	450	36	94	68	1869	17
	larch	9	100	0	.	0	0	0	.	14	100	0	.	0	.	0	.	18	100	0	.	0	.	2124	29	1045	21	1320	23	4529	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	276	36	784	26	4	100	1076	21
	other conifers	14	71	155	99	0	.	181	94	63	80	0	.	3	100	1	100	0	.	5	100	-16	100	0	.	16	62	0	.	423	56
	beech	1514	20	483	60	176	69	313	58	618	29	327	69	1601	28	271	41	266	49	66	64	56	63	245	46	315	48	1710	24	7962	11
	maple	291	24	45	62	50	71	11	100	118	43	134	43	349	36	142	51	243	38	0	.	79	70	31	68	91	52	80	60	1666	13
	ash	46	55	65	100	102	57	68	64	403	44	8	77	126	59	313	50	51	95	10	77	60	100	99	73	129	73	60	55	1539	20
	oak	168	68	368	89	22	100	75	51	86	50	0	.	77	75	0	.	13	71	42	82	0	.	10	72	56	100	181	50	1097	34
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	19	100	953	27	993	26
	other broadleaves	80	47	202	53	16	73	144	96	151	74	28	65	165	40	35	53	134	42	45	72	14	58	111	60	80	40	583	25	1791	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
	total	8336	13	1612	49	854	42	1764	35	2707	24	2651	26	9047	13	2126	28	4384	19	1004	37	1256	34	6568	15	8152	12	7504	12	57964	5
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17447	7	3242	18	5557	18	10934	11	14149	9	9304	13	19843	8	17034	10	10897	12	5715	18	6166	19	12900	10	25736	7	10022	11	168948	2
	fir	13339	8	1125	28	2024	26	8076	13	5129	13	2766	22	9721	10	5673	13	2322	21	1509	29	957	32	1080	30	1173	25	1601	25	56495	4
	pine	1502	20	752	25	769	36	789	25	2220	18	0	.	594	52	121	50	108	69	254	54	0	.	1911	18	2862	18	192	50	12074	8
	larch	82	59	93	46	160	40	171	48	734	30	0	.	23	74	183	49	407	38	22	100	186	55	7710	12	5680	12	4621	13	20071	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	807	24	1302	20	4	100	2124	15
	other conifers	39	36	269	63	171	84	578	43	342	72	0	.	72	96	18	48	6	100	5	100	-13	118	23	67	32	42	42	100	1584	27
	beech	14976	6	6858	12	3713	18	7618	11	9993	9	1396	25	7338	14	6451	14	2463	20	998	27	1983	22	769	30	1248	26	4909	13	70711	3
	maple	2327	11	782	20	257	39	457	23	1932	14	522	24	1094	18	1341	18	775	20	241	29	674	24	237	29	179	37	244	32	11061	5
	ash	1732	20	865	26	1540	25	1907	30	3727	13	473	40	810	35	1839	20	456	33	414	49	789	54	240	38	229	45	498	34	15518	7
	oak	1212	22	904	41	1329	27	927	25	1820	20	51	56	87	67	83	59	25	61	93	56	0	.	180	42	106	71	853	22	7670	10
	sweet chestnut	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	78	52	19	100	4814	13	4925	13
	other broadleaves	953	18	694	24	729	32	767	28	1292	19	145	30	438	23	682	25	933	32	213	33	460	49	844	20	702	22	3550	11	12401	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
	total	53609	4	15583	10	16257	11	32224	7	41337	5	14658	11	40020	6	33425	7	18409	10	9464	15	11201	15	26778	7	39268	6	31349	4	383583	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. 1230981. Birmensdorf, Swiss Federal Research Institute WSL

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