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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NFI1
growing stock (stemwood)
development stage · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
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 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	252	32	153	53	158	53	40	63	639	38	439	32	681	30	448	37	583	36	73	59	167	51	265	41	643	37	36	74	4576	12
	fir	214	39	35	85	12	73	56	59	111	65	216	75	466	38	24	65	45	58	0	.	22	71	58	76	11	84	0	.	1269	22
	pine	47	72	14	92	0	.	51	84	45	61	0	.	0	.	1	100	0	.	0	.	0	.	1	100	0	.	0	.	160	39
	larch	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	121	53	87	56	24	60	249	33
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	79	10	100	0	.	21	63
	other conifers	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100
	beech	321	34	297	34	42	60	103	47	198	42	35	88	30	58	59	85	71	68	31	81	52	60	0	.	9	68	83	94	1332	16
	maple	40	54	57	54	9	88	3	100	54	62	20	68	19	50	59	50	40	56	6	100	29	100	4	80	0	.	22	94	361	21
	ash	93	61	43	78	8	76	75	69	52	52	5	100	23	86	28	49	18	76	0	.	71	82	34	52	0	.	10	67	459	24
	oak	70	47	44	75	39	95	20	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-1	411	0	.	14	100	186	34
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	72	0	.	8	69	32	56
	other broadleaves	43	48	100	52	10	71	18	53	21	73	7	61	11	62	32	63	41	58	6	100	43	62	23	46	15	79	41	38	413	18
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1080	23	775	29	277	44	366	38	1120	29	722	37	1230	26	651	34	798	29	117	54	385	41	540	32	774	33	238	39	9073	9
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	77	51	37	88	80	62	109	50	290	38	168	49	198	57	358	40	355	38	264	66	83	85	239	40	524	30	91	46	2873	13
	fir	117	59	0	.	11	102	112	50	50	61	-1	186	31	100	32	59	55	79	8	100	3	100	34	51	27	71	24	78	503	23
	pine	6	100	25	67	11	100	8	100	88	43	0	.	0	.	0	.	3	100	0	.	0	.	5	65	2	100	0	.	147	30
	larch	1	100	0	100	0	.	5	76	44	74	0	.	0	.	0	.	0	.	0	.	0	.	154	35	77	68	31	52	313	27
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	1	100
	other conifers	0	.	3	100	0	.	1	100	0	.	0	.	0	.	0	.	1	100	0	100	0	.	2	100	1	100	0	.	9	50
	beech	68	63	54	82	19	79	111	39	96	39	12	85	0	.	92	42	10	100	4	100	14	76	7	86	0	.	72	68	561	19
	maple	8	100	31	80	0	.	11	63	20	63	1	100	5	62	30	65	6	77	0	.	15	80	1	100	17	89	2	75	146	28
	ash	26	71	29	75	2	100	8	71	32	47	1	100	7	72	18	94	0	.	0	.	1	100	4	100	0	.	24	63	150	27
	oak	0	.	38	100	15	100	6	100	31	70	0	.	2	100	0	.	0	.	0	.	0	.	14	54	0	.	0	.	107	44
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	31	62	47	53
	other broadleaves	49	59	70	87	0	.	2	72	22	48	0	.	15	48	7	80	9	53	1	110	7	100	34	48	24	51	29	32	268	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	350	36	287	41	138	50	374	29	674	25	180	46	258	47	536	33	441	34	277	63	123	63	494	26	689	25	303	25	5125	9
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	928	21	384	33	661	30	784	24	1419	18	698	31	1011	23	786	25	330	35	410	43	578	34	642	23	777	26	276	43	9685	7
	fir	617	22	81	42	118	48	544	33	285	31	182	45	147	38	264	34	39	60	108	45	78	65	113	52	11	100	46	71	2631	11
	pine	101	50	152	40	84	50	38	68	243	37	0	.	57	75	0	.	3	100	72	58	0	.	568	25	207	37	7	100	1532	14
	larch	27	79	76	68	17	71	45	76	75	40	0	.	0	.	1	100	13	99	0	.	17	75	345	27	201	34	271	38	1088	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	50	43	58	0	.	91	38
	other conifers	1	100	22	79	5	100	32	67	13	66	0	.	0	.	1	342	0	.	1	100	-1	100	0	.	0	100	0	.	75	39
	beech	1304	17	711	21	376	33	266	33	664	25	253	45	236	37	266	31	128	49	98	38	129	57	141	47	36	60	777	19	5386	8
	maple	168	27	103	34	118	41	36	57	268	27	24	52	47	34	97	35	88	39	47	42	89	47	31	34	19	58	69	41	1204	11
	ash	271	31	155	34	240	30	72	48	421	22	113	51	102	50	194	32	41	55	53	59	18	62	13	50	75	57	157	28	1925	10
	oak	302	34	92	55	280	31	82	39	156	34	3	71	9	85	14	89	52	71	0	100	0	.	38	47	0	.	295	23	1323	13
	sweet chestnut	0	.	0	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	29	60	0	.	1373	14	1439	14
	other broadleaves	122	30	50	41	241	34	90	36	249	28	30	57	125	35	173	37	41	39	50	50	54	59	349	25	183	34	1571	12	3329	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3842	12	1825	17	2176	17	1989	18	3792	12	1303	26	1735	18	1796	18	734	23	841	27	964	28	2316	14	1552	17	4841	8	29707	4
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3311	14	1097	27	1988	22	1024	27	2427	17	2643	20	3352	15	2260	22	1567	25	1078	27	1322	28	1908	21	2042	21	599	37	26618	6
	fir	2351	15	534	35	405	40	495	31	813	25	447	32	1100	26	897	24	187	36	171	47	146	61	76	53	61	79	219	59	7901	8
	pine	525	26	332	29	121	61	56	52	476	28	0	.	58	71	28	78	13	61	126	54	0	.	303	46	603	31	140	72	2781	13
	larch	0	.	43	74	113	84	9	100	115	53	0	.	0	.	44	100	36	65	37	100	0	.	739	23	317	33	461	34	1914	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	159	44	72	52	0	.	231	34
	other conifers	2	59	2	100	14	100	59	100	20	55	0	.	1	100	3	72	0	.	1	100	1	100	0	.	0	.	0	.	102	60
	beech	3877	12	1310	17	1107	25	1078	24	1186	18	347	36	1068	22	1347	24	343	37	254	51	333	45	119	46	139	54	753	21	13260	6
	maple	498	17	175	26	46	45	104	51	130	27	127	47	169	33	142	37	112	34	22	72	78	64	27	61	7	71	6	78	1643	10
	ash	302	34	138	35	154	40	101	39	459	25	55	76	129	43	306	37	91	49	20	64	9	67	27	75	0	.	42	45	1834	12
	oak	239	35	225	33	135	35	34	58	299	38	19	84	12	80	7	72	23	89	1	100	0	.	3	100	0	.	96	49	1092	16
	sweet chestnut	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	460	25	465	25
	other broadleaves	244	22	234	59	134	55	22	47	116	41	27	44	97	63	101	46	242	35	18	76	8	72	48	53	12	69	328	30	1632	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	11350	8	4090	15	4223	15	2981	17	6041	12	3664	17	5985	12	5134	15	2613	19	1727	23	1897	24	3409	16	3253	17	3105	14	59473	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5090	11	805	36	3912	17	6517	12	7719	11	3583	18	7987	11	5842	13	3826	18	1468	30	1797	25	2556	16	8303	11	2061	19	61465	4
	fir	3805	13	311	40	738	31	3307	15	3264	15	764	33	3126	15	2072	18	409	38	541	39	405	38	129	54	229	38	286	34	19385	6
	pine	620	28	191	45	484	41	314	35	1290	18	0	.	161	91	140	51	35	100	46	100	53	100	265	42	572	26	124	58	4296	11
	larch	35	58	9	100	141	53	41	73	311	30	0	.	81	92	53	66	100	60	0	.	348	44	1368	18	1287	18	538	26	4312	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	99	54	253	41	0	.	352	33
	other conifers	1	100	0	.	121	75	24	94	104	82	0	.	27	92	1	100	0	.	0	.	0	.	0	.	0	.	0	.	277	46
	beech	4169	11	2010	17	1043	22	2747	15	3480	12	450	38	2419	18	1674	19	610	35	409	32	654	31	13	74	218	36	252	39	20149	5
	maple	396	18	236	34	16	76	209	35	335	25	58	42	212	31	258	24	103	43	63	45	79	53	1	100	15	59	2	100	1982	10
	ash	216	30	175	39	255	39	384	34	679	19	81	63	124	33	378	33	0	.	89	57	33	108	7	93	25	100	5	83	2452	11
	oak	259	36	192	42	374	38	323	27	614	23	27	71	27	81	34	73	0	.	6	100	0	.	0	.	28	79	31	80	1916	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	487	34	495	33
	other broadleaves	281	26	120	50	39	67	115	34	301	26	17	48	73	41	64	43	80	54	77	55	93	44	5	54	16	53	67	40	1347	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	14873	8	4047	15	7124	13	13980	9	18098	8	4979	17	14238	10	10516	11	5164	15	2699	22	3463	19	4444	13	10952	9	3852	13	118428	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3654	15	314	46	1051	29	3701	17	4248	16	1733	25	3866	17	5194	16	2635	22	951	33	1819	27	2069	22	6946	12	2419	18	40601	5
	fir	2402	17	114	42	363	41	1450	23	958	26	467	39	2563	19	1422	23	309	44	84	83	140	96	67	62	591	32	404	40	11336	8
	pine	170	41	88	100	64	73	289	42	682	30	0	.	0	.	23	82	0	.	0	.	0	.	159	86	353	48	52	65	1879	18
	larch	19	100	9	100	34	100	28	100	88	67	34	100	16	100	46	71	15	100	0	.	0	.	1560	21	1244	24	883	23	3976	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	73	294	38	5	100	341	34
	other conifers	1	100	0	.	0	.	83	78	10	100	0	.	1	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	95	69
	beech	1981	17	1280	25	738	33	2436	17	1908	18	284	47	1176	27	945	27	277	41	54	64	225	62	44	86	433	50	354	33	12134	8
	maple	235	31	107	44	12	89	37	77	145	31	1	100	16	52	141	35	37	66	9	90	66	59	0	.	19	96	9	100	833	15
	ash	150	36	70	39	108	57	147	51	208	35	13	100	20	71	50	52	9	100	0	.	19	76	0	.	0	.	0	.	795	18
	oak	149	72	138	49	308	43	276	29	469	28	4	100	0	.	33	92	0	.	11	100	0	.	4	100	18	100	0	.	1410	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	606	34	606	34
	other broadleaves	126	31	79	57	78	70	118	50	178	34	2	100	12	71	34	58	52	71	23	58	4	100	87	75	18	72	29	46	841	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8887	11	2200	22	2757	22	8566	12	8894	12	2538	24	7670	13	7887	14	3335	20	1132	31	2273	25	4031	16	9916	11	4761	13	74847	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4294	10	210	37	229	34	649	26	588	22	1631	20	4351	11	2126	17	2570	15	623	31	809	28	4206	11	6596	10	1766	16	30648	4
	fir	2279	13	44	61	48	87	839	27	224	34	810	27	3184	14	662	21	274	32	97	47	126	53	529	33	234	29	315	45	9663	7
	pine	309	27	70	59	50	55	82	71	137	35	5	100	186	46	27	90	77	57	69	60	59	78	675	20	981	19	30	97	2758	10
	larch	0	.	7	100	0	.	0	.	77	71	0	.	20	100	0	.	68	53	0	.	1	100	2196	12	1446	14	1165	15	4981	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	21	100	11	100	4	100	8	100	0	.	311	22	579	22	-1	100	933	15
	other conifers	21	77	0	100	19	100	0	100	23	59	0	.	0	.	2	100	0	.	0	0	0	.	6	100	24	50	0	.	95	33
	beech	1882	13	454	32	472	34	533	30	959	21	330	42	840	19	875	22	478	30	38	55	319	40	96	42	111	58	867	18	8253	7
	maple	225	20	36	57	67	56	74	55	304	31	101	32	89	30	231	28	152	29	2	100	102	33	37	50	16	56	25	44	1462	11
	ash	276	29	91	53	235	45	64	42	503	25	68	50	158	31	191	35	43	60	0	.	58	42	49	53	18	64	23	62	1779	12
	oak	112	47	57	63	86	60	110	41	304	28	7	105	7	100	47	63	24	100	12	100	0	0	17	62	73	82	187	57	1040	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	799	28	799	28
	other broadleaves	156	25	68	39	93	58	95	49	573	23	41	31	103	31	153	32	53	35	9	67	64	50	144	50	60	32	401	21	2014	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	9554	8	1038	26	1299	24	2446	18	3693	14	2992	18	8959	9	4324	14	3743	14	859	28	1539	22	8265	9	10137	8	5576	10	64425	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17607	5	2999	15	8079	11	12825	8	17331	7	10895	9	21446	6	17013	7	11867	9	4867	14	6575	12	11884	7	25830	5	7248	9	176466	2
	fir	11784	6	1117	21	1696	19	6803	10	5707	10	2884	15	10616	8	5371	10	1318	18	1009	24	921	26	1005	21	1164	19	1293	20	52687	3
	pine	1778	14	873	20	814	27	836	22	2961	12	5	100	462	39	218	37	131	44	313	32	112	63	1977	15	2718	13	352	37	13553	6
	larch	82	43	161	40	304	41	129	42	710	20	34	100	117	68	145	45	232	33	37	100	366	42	6482	8	4660	10	3373	10	16833	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	21	100	11	100	4	100	8	100	0	.	670	17	1251	16	4	135	1969	12
	other conifers	26	62	44	54	159	59	198	47	170	52	0	.	28	88	7	54	1	100	3	63	0	1233	9	77	26	47	0	.	670	24
	beech	13602	5	6116	9	3797	12	7274	9	8492	7	1711	18	5770	10	5258	10	1917	16	889	21	1727	18	420	24	946	27	3158	9	61077	3
	maple	1570	9	745	15	267	25	475	22	1256	12	331	22	557	17	958	13	538	16	148	26	457	21	101	27	93	31	136	28	7630	4
	ash	1335	14	701	17	1002	18	851	20	2354	10	335	28	563	18	1164	16	202	29	163	37	210	36	134	29	118	43	261	20	9394	5
	oak	1130	18	785	19	1237	18	851	16	1872	13	60	44	57	46	135	38	98	49	31	58	0	0	74	31	118	56	623	22	7073	6
	sweet chestnut	0	.	0	.	41	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	52	46	24	74	3763	10	3883	10
	other broadleaves	1021	11	721	25	596	23	460	20	1460	13	123	21	437	20	564	18	519	21	183	29	274	25	691	19	327	21	2466	9	9843	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	49936	3	14262	7	17994	7	30702	5	42312	4	16378	8	40075	5	30845	6	16828	7	7652	11	10644	10	23499	5	37275	4	22677	4	361078	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. 1354501. Birmensdorf, Swiss Federal Research Institute WSL

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