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: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
development stage	main tree species	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	.
	spruce	419	28	1015	28	2204	16	191	46	711	35	36	74	4576	12
	fir	261	34	174	46	804	30	18	100	11	84	0	.	1269	22
	pine	57	61	100	52	1	100	1	100	0	.	0	.	160	39
	larch	17	100	0	.	0	.	121	53	87	56	24	60	249	33
	Arolla pine	0	.	0	.	0	.	12	79	10	100	0	.	21	63
	other conifers	0	.	16	100	0	.	0	.	0	.	0	.	16	100
	beech	584	25	377	28	279	31	0	.	9	68	83	94	1332	16
	maple	87	41	98	44	151	30	3	100	0	.	22	94	361	21
	ash	136	48	141	42	140	46	33	54	0	.	10	67	459	24
	oak	64	48	109	48	-1	411	0	.	0	.	14	100	186	34
	sweet chestnut	0	.	0	.	10	100	14	100	0	.	8	69	32	56
	other broadleaves	81	40	131	41	125	31	21	51	15	79	41	38	413	18
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1707	18	2160	19	3713	14	413	33	842	31	238	39	9073	9
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	82	48	567	25	1441	21	168	44	515	30	101	42	2873	13
	fir	95	69	204	34	119	48	34	51	27	71	24	78	503	23
	pine	6	100	131	33	3	100	5	65	2	100	0	.	147	30
	larch	1	100	20	78	29	100	139	37	69	76	54	42	313	27
	Arolla pine	0	.	0	.	0	.	0	.	1	100	0	.	1	100
	other conifers	0	.	4	87	2	77	0	.	1	100	2	100	9	50
	beech	138	45	224	26	121	33	6	100	0	.	72	68	561	19
	maple	27	51	58	52	41	44	1	100	17	89	2	75	146	28
	ash	48	48	49	48	26	66	4	100	0	.	24	63	150	27
	oak	0	.	90	52	3	78	13	57	0	.	0	.	107	44
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	47	53	47	53
	other broadleaves	84	73	58	49	55	35	18	39	17	59	36	32	268	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	480	31	1406	16	1841	19	388	25	650	26	361	23	5125	9
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1131	19	3314	12	3541	13	623	24	799	25	276	43	9685	7
	fir	656	21	1116	20	704	19	99	58	12	94	46	71	2631	11
	pine	221	33	383	28	146	42	568	25	207	37	7	100	1532	14
	larch	27	79	211	33	21	66	340	28	217	32	271	38	1088	16
	Arolla pine	0	.	0	.	0	.	48	50	43	58	0	.	91	38
	other conifers	4	86	67	44	4	67	0	.	0	100	0	.	75	39
	beech	2004	14	1302	16	1154	17	89	68	59	54	777	19	5386	8
	maple	205	22	481	20	388	18	22	41	38	58	69	41	1204	11
	ash	326	25	888	16	471	21	8	55	75	57	157	28	1925	10
	oak	370	29	538	21	81	49	38	47	0	.	295	23	1323	13
	sweet chestnut	0	.	36	100	9	81	21	73	0	.	1373	14	1439	14
	other broadleaves	165	27	577	19	530	18	301	27	185	34	1571	12	3329	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5110	10	8912	8	7049	9	2159	14	1636	17	4841	8	29707	4
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4221	12	6542	11	11354	9	1736	22	2140	20	625	36	26618	6
	fir	3017	14	2138	16	2467	15	0	.	61	79	219	59	7901	8
	pine	739	22	803	21	158	41	306	46	637	29	140	72	2781	13
	larch	8	100	272	43	80	63	739	23	355	32	461	34	1914	15
	Arolla pine	0	.	0	.	0	.	159	44	72	52	0	.	231	34
	other conifers	2	59	93	66	7	58	0	.	0	.	0	.	102	60
	beech	4972	10	4169	11	3173	14	53	76	139	54	753	21	13260	6
	maple	597	15	449	21	523	18	13	100	55	88	6	78	1643	10
	ash	417	27	905	17	462	26	9	82	0	.	42	45	1834	12
	oak	271	25	675	23	47	56	3	84	0	.	96	49	1092	16
	sweet chestnut	0	.	5	100	0	.	0	.	0	.	460	25	465	25
	other broadleaves	274	21	477	34	499	23	36	66	17	57	328	30	1632	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	14519	7	16526	7	18769	7	3053	17	3475	16	3131	14	59473	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5902	10	20652	7	21930	7	2266	17	8593	11	2121	18	61465	4
	fir	4358	12	8306	9	5981	11	75	70	379	32	286	34	19385	6
	pine	814	23	2176	15	292	56	265	42	625	25	124	58	4296	11
	larch	89	45	484	25	267	41	1368	18	1540	18	563	25	4312	10
	Arolla pine	0	.	0	.	0	.	99	54	253	41	0	.	352	33
	other conifers	85	96	190	52	2	100	0	.	0	.	0	.	277	46
	beech	6069	9	8279	8	4980	11	13	74	556	32	252	39	20149	5
	maple	646	17	563	19	712	15	0	.	59	67	2	100	1982	10
	ash	295	25	1631	14	495	26	0	100	25	100	5	83	2452	11
	oak	345	26	1439	16	73	47	0	.	7	100	52	62	1916	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	495	33	495	33
	other broadleaves	343	24	519	19	378	22	5	54	33	61	68	39	1347	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	18948	7	44238	5	35112	6	4092	14	12069	9	3968	13	118428	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4074	14	10680	10	15250	9	1002	26	6749	12	2846	17	40601	5
	fir	2857	15	3586	14	3843	14	55	73	591	32	404	40	11336	8
	pine	225	48	1047	23	43	100	159	86	353	48	52	65	1879	18
	larch	28	75	173	44	223	64	1139	23	1094	25	1319	21	3976	13
	Arolla pine	0	.	0	.	0	.	41	73	284	39	15	74	341	34
	other conifers	1	100	94	70	1	100	0	.	0	.	0	.	95	69
	beech	2695	16	6197	11	2419	17	37	100	433	50	354	33	12134	8
	maple	324	26	247	25	234	28	0	.	19	96	9	100	833	15
	ash	161	29	535	24	98	36	0	.	0	.	0	.	795	18
	oak	208	58	1165	18	18	67	0	.	18	100	0	.	1410	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	606	34	606	34
	other broadleaves	135	32	474	23	163	47	22	65	18	72	29	46	841	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	10709	10	24197	7	22292	8	2455	18	9559	11	5634	12	74847	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4410	10	1879	14	12077	7	3826	12	6574	10	1882	16	30648	4
	fir	2353	13	1489	19	4775	11	445	38	286	28	315	45	9663	7
	pine	400	26	249	27	422	28	662	20	981	19	43	74	2758	10
	larch	0	.	85	66	89	46	2103	13	1446	14	1257	14	4981	8
	Arolla pine	0	.	0	.	45	58	311	22	579	22	-1	100	933	15
	other conifers	21	75	35	62	9	100	0	.	24	50	6	100	95	33
	beech	2309	13	2285	15	2533	12	82	48	178	47	867	18	8253	7
	maple	260	19	481	24	671	14	3	75	23	49	25	44	1462	11
	ash	281	28	918	19	480	18	31	72	46	46	23	62	1779	12
	oak	81	61	613	19	51	56	16	65	94	67	187	57	1040	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	799	28	799	28
	other broadleaves	163	23	792	19	497	19	59	30	95	38	408	21	2014	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	10280	8	8825	9	21648	6	7538	9	10325	8	5810	9	64425	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20241	5	44648	4	67797	3	9811	8	26080	5	7888	9	176466	2
	fir	13598	6	17012	6	18692	5	725	26	1366	18	1293	20	52687	3
	pine	2463	12	4889	9	1066	21	1966	15	2804	13	365	36	13553	6
	larch	171	31	1245	16	710	27	5950	8	4809	9	3949	10	16833	5
	Arolla pine	0	.	0	.	45	58	670	17	1241	16	14	81	1969	12
	other conifers	114	73	498	28	24	43	0	.	26	47	9	77	670	24
	beech	18771	4	22832	5	14660	6	281	32	1375	22	3158	9	61077	3
	maple	2146	8	2377	9	2718	7	42	39	211	34	136	28	7630	4
	ash	1664	11	5066	7	2173	10	85	35	146	37	261	20	9394	5
	oak	1339	15	4629	8	273	24	70	31	119	55	644	21	7073	6
	sweet chestnut	0	.	41	89	19	65	35	59	0	.	3787	10	3883	10
	other broadleaves	1245	11	3027	10	2247	9	463	19	380	20	2480	9	9843	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	61751	2	106265	2	110424	2	20098	5	38557	4	23983	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. 1354507. Birmensdorf, Swiss Federal Research Institute WSL

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