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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NFI1
number of stems
development stage · main tree species
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	245	45	139	59	84	75	51	63	414	47	407	36	625	37	152	57	304	41	78	100	120	94	251	53	277	42	0	.	3146	14
	fir	83	58	7	100	64	100	110	66	97	72	256	62	487	49	0	.	40	84	0	.	7	100	56	78	10	100	0	.	1217	26
	pine	98	100	62	100	0	.	110	81	20	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	290	51
	larch	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	246	100	108	63	39	74	427	61
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100	0	.	49	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	168	52	147	42	39	84	184	60	256	49	145	96	196	59	80	90	173	86	0	.	126	100	0	.	42	66	0	.	1555	22
	maple	46	72	142	53	17	100	52	100	38	100	30	100	101	70	61	76	32	71	11	100	0	.	17	100	0	.	0	.	548	26
	ash	155	59	136	95	50	100	76	100	93	86	140	100	36	100	102	84	29	100	0	.	284	100	100	73	0	.	0	.	1201	33
	oak	160	79	42	61	0	.	23	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	100	0	.	25	100	307	47
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	79	0	.	0	.	58	79
	other broadleaves	137	51	334	50	0	.	30	64	12	100	52	75	105	62	154	83	18	100	0	.	78	100	127	61	55	100	89	63	1192	23
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1093	33	1044	40	253	69	635	47	931	35	1030	37	1549	35	548	64	598	41	89	88	616	82	911	42	541	33	153	45	9990	13
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	150	56	31	100	93	67	363	61	327	58	423	51	354	42	380	43	849	36	121	81	145	78	120	42	689	40	220	50	4265	15
	fir	137	77	0	.	17	100	220	52	24	77	61	100	79	100	59	58	51	71	0	.	17	100	7	100	65	87	43	100	781	26
	pine	0	.	62	64	0	.	7	100	93	66	0	.	0	.	0	.	0	.	0	.	0	.	69	79	0	.	0	.	231	40
	larch	34	100	17	100	0	.	17	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	138	42	147	51	145	54	506	26
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	17	100	0	.	17	100	0	.	0	.	0	.	0	.	17	100	0	.	0	.	87	100	0	.	0	.	138	66
	beech	170	55	20	100	0	.	128	60	119	45	30	100	0	.	122	54	44	100	7	100	0	.	0	.	0	.	68	53	710	23
	maple	0	.	59	100	0	.	101	62	17	100	0	.	96	66	126	77	17	100	0	.	35	100	18	100	102	71	18	100	590	29
	ash	58	100	14	100	0	.	84	72	24	77	15	100	35	100	12	100	0	.	0	.	0	.	71	100	0	.	0	.	312	38
	oak	0	.	7	100	24	100	0	.	34	100	0	.	21	100	0	.	0	.	0	.	0	.	56	58	0	.	0	.	142	40
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	98	68	98	68
	other broadleaves	69	79	7	100	0	.	34	71	52	57	0	.	156	49	7	100	118	55	0	.	0	.	175	53	370	61	262	41	1249	24
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	618	38	234	50	134	62	973	37	697	42	529	44	740	32	706	36	1098	33	128	77	198	68	741	30	1372	28	854	26	9023	10
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4685	27	1675	39	1563	41	3733	27	5569	21	2881	35	4023	32	3095	35	1421	36	1389	47	1317	48	3489	28	3446	28	886	54	39170	9
	fir	1791	28	292	56	474	72	1804	40	353	36	281	62	258	47	639	36	71	81	722	48	21	100	202	91	89	100	156	95	7153	15
	pine	205	69	395	63	325	71	208	82	157	41	0	.	378	100	0	.	0	.	104	100	0	.	3315	30	885	43	0	.	5973	20
	larch	17	100	38	71	7	100	127	83	260	49	0	.	0	.	0	.	91	83	0	.	161	71	1497	30	439	47	1059	35	3695	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	199	53	186	69	0	.	384	43
	other conifers	17	100	278	77	0	.	117	55	100	62	0	.	0	.	21	100	0	.	52	100	46	100	0	.	18	100	0	.	648	38
	beech	4868	21	2634	27	882	38	827	37	1880	28	584	56	1015	52	1052	43	422	81	702	44	402	67	847	54	166	100	4338	26	20622	10
	maple	687	28	226	38	130	67	77	100	1022	28	124	67	245	43	610	35	464	50	255	47	242	60	114	56	37	100	168	46	4402	13
	ash	863	40	517	41	619	39	275	51	1233	25	452	50	551	69	723	37	202	76	234	55	53	100	106	73	605	64	729	32	7164	13
	oak	887	49	121	62	1591	42	369	49	92	44	39	71	37	72	21	100	124	86	18	100	0	.	252	66	0	.	1447	37	5001	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	117	82	0	.	8044	18	8161	18
	other broadleaves	676	49	139	53	1073	42	717	49	952	33	250	65	444	46	1076	48	188	46	574	58	105	46	1371	30	1457	34	8282	14	17306	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	14697	14	6315	21	6663	22	8255	20	11618	15	4612	28	6951	23	7237	24	2984	27	4051	28	2347	40	11509	17	7328	20	25110	10	119678	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3943	19	1351	37	2259	31	1996	33	2951	22	3201	29	5381	20	3228	31	1872	35	1402	41	1791	40	2876	29	2905	31	1334	41	36491	8
	fir	4396	19	395	47	439	60	756	40	1101	34	817	49	1044	30	1019	30	369	44	188	60	163	67	45	87	0	.	72	73	10804	11
	pine	598	42	484	37	60	89	74	51	429	44	0	.	216	96	21	100	16	71	179	61	0	.	162	56	993	44	0	.	3233	19
	larch	0	.	24	77	0	.	0	.	68	80	0	.	0	.	0	.	43	87	28	100	0	.	842	32	372	41	969	39	2346	21
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	78	61	125	83	0	.	204	56
	other conifers	54	74	0	.	0	.	70	100	59	52	0	.	0	.	18	100	0	.	25	100	18	100	0	.	0	.	0	.	244	38
	beech	6597	15	2152	26	1668	29	2078	27	1827	22	600	39	1955	32	2251	32	650	41	437	72	777	44	227	75	520	58	2302	28	24040	8
	maple	1133	19	157	45	137	69	81	60	473	30	465	44	422	31	354	48	549	46	15	100	281	76	115	88	15	100	39	100	4237	13
	ash	543	47	94	64	256	64	131	53	532	35	0	.	594	42	693	47	397	45	15	100	43	81	117	63	0	.	103	60	3519	17
	oak	497	49	280	43	193	45	79	61	331	39	87	100	46	100	0	.	55	74	0	.	0	.	0	.	0	.	219	53	1787	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1729	33	1729	33
	other broadleaves	970	30	194	36	135	72	56	76	558	43	156	51	210	49	220	49	606	39	0	.	53	100	208	50	74	100	1302	32	4740	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	18730	11	5132	22	5147	21	5321	21	8329	16	5325	24	9866	16	7805	21	4557	24	2290	35	3126	32	4672	22	5004	24	8069	17	93374	5
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3721	16	490	43	2451	26	4706	15	4518	14	3372	25	5171	16	4714	17	3900	22	1761	33	752	36	2518	24	8597	15	2043	24	48714	5
	fir	3688	17	237	59	675	37	3138	19	1585	20	487	64	3028	19	1375	26	529	54	632	51	440	40	107	75	345	40	451	52	16716	8
	pine	445	37	112	52	516	51	228	43	876	25	0	.	135	89	48	88	47	100	0	.	0	.	213	70	605	33	85	100	3309	15
	larch	14	71	7	100	94	73	28	100	154	41	0	.	0	.	14	100	58	64	0	.	44	61	832	38	1175	27	207	42	2626	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	100	300	42	0	.	360	38
	other conifers	34	100	0	.	63	100	52	74	75	100	0	.	7	100	18	100	0	.	0	.	0	.	0	.	0	.	0	.	248	45
	beech	5108	13	1504	25	765	30	3209	16	3990	16	349	57	3898	19	1382	28	700	38	391	47	821	32	62	100	621	40	517	45	23316	6
	maple	765	28	478	38	23	100	107	50	260	43	51	57	377	34	414	33	296	50	168	49	201	56	18	100	47	62	35	100	3240	13
	ash	404	36	74	49	253	54	247	40	517	24	16	100	213	35	291	46	0	.	135	70	92	70	36	71	28	100	40	100	2345	14
	oak	166	41	101	52	354	50	334	38	459	26	21	100	0	.	0	.	0	.	6	100	0	.	0	.	58	100	42	71	1541	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	100	515	61	543	58
	other broadleaves	724	28	231	53	142	50	271	36	429	32	119	61	344	66	199	39	166	57	105	58	249	52	35	100	194	47	299	44	3507	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	15069	11	3233	23	5336	19	12318	11	12862	11	4415	25	13172	13	8453	15	5696	19	3199	28	2600	26	3881	21	11998	13	4233	19	106464	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1752	19	156	57	622	45	1358	24	1580	22	803	48	1332	24	2293	23	1166	34	450	42	1064	36	1126	29	3386	17	1671	24	18759	7
	fir	1461	21	104	60	292	72	774	29	625	29	121	73	1602	24	825	27	311	50	50	87	46	100	29	100	165	60	127	49	6533	10
	pine	66	55	0	.	35	65	111	58	179	42	0	.	0	.	15	71	0	.	0	.	0	.	32	81	303	49	88	66	829	24
	larch	7	100	0	.	13	100	13	100	13	71	0	.	9	100	25	72	7	100	0	.	0	.	683	31	683	31	363	32	1817	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	100	374	50	0	.	420	46
	other conifers	17	100	0	.	0	.	54	71	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	89	51
	beech	1665	21	728	29	508	38	1628	20	1176	22	9	100	1471	30	1062	29	244	54	146	74	211	62	195	100	462	38	278	64	9783	9
	maple	485	45	41	56	16	100	98	54	205	38	0	.	102	74	192	49	52	100	15	100	298	59	0	.	37	71	0	.	1541	21
	ash	60	66	67	53	49	71	197	44	206	63	0	.	0	.	104	56	15	100	0	.	28	81	0	.	0	.	0	.	726	25
	oak	0	.	32	63	137	71	61	44	269	38	0	.	0	.	34	73	0	.	6	100	0	.	18	100	28	100	0	.	587	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	541	59	541	59
	other broadleaves	255	58	86	73	156	64	321	51	455	39	15	100	42	82	116	53	67	89	23	75	6	100	307	71	89	85	181	63	2120	19
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5767	16	1214	28	1828	33	4616	17	4708	17	949	47	4557	18	4684	19	1862	30	691	44	1653	32	2437	23	5528	14	3249	20	43744	6
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4960	15	131	57	84	46	514	40	741	29	2092	30	4898	16	2884	27	2430	23	587	41	752	41	7497	15	9875	13	2652	22	40098	6
	fir	3104	17	14	100	126	72	1098	33	442	42	870	38	3013	19	613	34	391	37	192	73	160	82	789	38	205	42	340	52	11357	9
	pine	376	35	19	100	13	71	33	100	81	53	0	.	95	71	0	.	240	73	263	72	0	.	2271	27	2105	28	144	88	5643	16
	larch	0	.	0	.	0	.	0	.	50	100	0	.	14	100	0	.	68	51	0	.	0	.	2667	16	1369	19	1560	21	5727	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	710	31	1182	28	0	.	1901	21
	other conifers	35	71	17	100	57	100	0	.	172	91	0	.	0	.	22	100	0	.	0	.	0	.	17	100	52	100	0	.	372	48
	beech	3761	16	760	47	747	46	710	43	1294	26	425	45	1265	22	1643	33	1138	33	53	53	206	47	494	45	216	63	2916	25	15628	9
	maple	698	26	136	53	0	.	194	49	650	34	224	40	262	29	286	43	247	44	0	.	187	46	308	60	100	84	105	67	3397	13
	ash	619	39	101	72	301	54	174	45	811	30	244	53	305	54	435	51	139	69	0	.	136	53	86	84	0	.	111	49	3461	15
	oak	483	63	17	100	0	.	97	51	214	37	21	100	0	.	61	100	0	.	30	100	0	.	103	47	0	.	329	54	1356	28
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1112	42	1112	42
	other broadleaves	676	35	243	51	128	83	136	40	1274	30	321	44	431	38	590	53	245	46	156	71	233	52	544	38	141	50	1370	24	6488	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	14713	10	1439	37	1457	38	2958	23	5729	19	4197	23	10282	13	6533	20	4907	19	1282	39	1672	31	15486	11	15245	11	10639	13	96539	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19455	8	3973	21	7156	16	12721	11	16100	9	13180	13	21783	9	16745	11	11944	12	5787	19	5941	19	17877	10	29175	7	8805	12	190643	3
	fir	14660	8	1048	27	2088	26	7900	13	4228	13	2892	23	9509	10	4531	13	1762	22	1785	28	854	29	1237	29	879	25	1189	28	54562	4
	pine	1788	20	1134	28	948	37	771	30	1836	17	0	.	824	55	84	57	304	60	546	44	0	.	6063	19	4891	17	317	52	19508	9
	larch	72	55	122	42	114	61	185	60	551	29	0	.	23	72	39	58	266	37	28	100	205	57	6905	12	4292	12	4342	14	17144	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	1093	23	2216	19	0	.	3318	15
	other conifers	157	40	312	69	119	71	309	36	405	46	0	.	7	100	96	45	17	100	77	75	65	77	104	85	70	78	0	.	1740	20
	beech	22338	7	7945	13	4609	16	8764	10	10542	9	2142	23	9799	12	7592	14	3371	19	1737	28	2543	21	1824	31	2028	23	10419	14	95653	3
	maple	3814	11	1239	19	323	41	710	24	2666	15	893	27	1604	15	2042	17	1659	24	466	32	1244	27	591	37	338	36	365	32	17953	6
	ash	2702	19	1002	27	1528	24	1185	20	3416	14	867	34	1734	28	2359	21	784	33	385	41	636	48	515	32	632	62	983	25	18728	7
	oak	2193	27	601	25	2299	30	964	24	1400	16	168	57	104	55	116	59	179	64	61	59	0	.	485	38	87	75	2063	27	10720	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	175	61	28	100	12039	13	12242	13
	other broadleaves	3507	16	1235	21	1633	30	1567	26	3732	16	914	27	1732	22	2362	26	1408	21	858	42	724	28	2766	19	2380	24	11785	10	36602	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	70688	4	18611	10	20818	10	35075	7	44876	6	21056	11	47118	6	35967	8	21702	9	11730	15	12211	14	39636	7	47016	6	52308	5	478813	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. 1395504. Birmensdorf, Swiss Federal Research Institute WSL

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