Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
growing stock (stemwood)
development stage · main tree species
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		protection forest region
		Jura + Plateau		Northwestern 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	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	31	163	403	50	106	99	6	100	211	43	205	91	961	32
	fir	0	.	127	84	10	100	0	.	0	0	0	.	137	78
	pine	0	.	0	0	0	.	0	0	0	.	0	.	0	0
	larch	13	100	0	.	0	.	56	62	135	50	83	100	285	39
	Arolla pine	0	.	0	.	0	.	27	100	0	0	0	.	27	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	29	115	26	71	3	100	0	.	24	100	0	.	82	55
	maple	13	100	18	77	13	100	0	.	0	.	0	.	44	52
	ash	0	.	0	.	4	100	0	.	0	.	0	.	4	100
	oak	9	100	0	.	0	.	0	.	0	.	0	.	9	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	5	77	6	100	3	72	0	.	0	.	0	.	14	53
	indeterminable	0	.	0	0	0	.	0	.	0	0	0	.	0	0
	total	99	72	579	43	139	86	89	64	370	36	288	94	1564	27
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	880	28	1384	26	511	40	203	44	800	41	230	51	4008	15
	fir	528	33	350	46	174	45	15	100	9	100	1	100	1077	23
	pine	68	73	0	.	0	.	0	0	70	79	0	.	138	54
	larch	72	71	131	66	0	0	254	51	64	68	86	75	606	30
	Arolla pine	0	.	0	.	0	.	0	.	2	100	0	.	2	100
	other conifers	3	100	1	100	0	.	5	100	3	76	0	.	12	54
	beech	1289	20	73	50	211	43	2	100	14	100	18	73	1607	17
	maple	147	51	93	42	113	46	1	1336	1	100	24	85	380	27
	ash	104	52	42	62	25	76	14	100	0	.	13	100	197	33
	oak	158	70	0	.	0	.	0	0	0	.	6	100	164	68
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	16	100	16	100
	other broadleaves	205	49	63	39	44	48	26	71	44	68	52	52	435	26
	indeterminable	0	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	3454	15	2136	22	1079	29	521	36	1006	34	447	37	8642	10
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5688	13	2401	21	1528	24	814	31	1397	21	441	32	12269	8
	fir	1682	21	633	32	380	40	111	66	45	71	103	71	2954	15
	pine	373	30	5	100	51	85	457	48	403	32	15	100	1304	22
	larch	196	40	121	50	0	.	580	27	420	35	563	28	1881	15
	Arolla pine	0	.	0	.	0	.	77	98	102	66	0	.	179	56
	other conifers	141	37	45	83	0	.	8	85	30	45	50	100	275	30
	beech	2030	19	302	43	261	43	183	66	1	100	780	29	3557	14
	maple	931	20	240	31	182	38	101	37	54	66	188	34	1697	13
	ash	748	20	122	39	315	40	250	43	48	51	206	33	1689	14
	oak	425	39	25	100	0	.	36	53	0	.	299	39	785	26
	sweet chestnut	0	0	0	.	0	.	34	79	0	.	1571	23	1605	22
	other broadleaves	730	17	294	37	341	33	445	22	613	29	1891	19	4313	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	12945	9	4187	16	3058	19	3097	18	3114	15	6107	11	32509	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6159	14	3504	21	3022	23	1152	37	2116	25	298	46	16251	9
	fir	1950	19	707	41	1273	31	0	.	86	71	171	65	4188	15
	pine	903	30	16	100	51	78	614	35	562	47	0	.	2146	21
	larch	187	46	0	.	38	77	454	38	578	32	805	37	2062	19
	Arolla pine	0	.	0	.	0	.	70	61	288	43	0	.	358	36
	other conifers	326	50	0	.	10	66	1	100	1	100	0	.	337	48
	beech	5230	13	696	35	1242	23	102	58	37	62	1640	26	8946	10
	maple	863	18	666	31	612	28	12	100	19	100	68	57	2240	14
	ash	1622	19	472	50	578	33	10	85	40	90	251	56	2972	15
	oak	1164	23	57	51	20	71	30	50	0	.	219	33	1491	19
	sweet chestnut	6	100	0	.	0	.	26	100	0	.	797	31	829	30
	other broadleaves	1001	23	56	38	116	53	220	52	116	58	1130	26	2638	15
	indeterminable	0	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	19412	8	6174	16	6962	17	2690	22	3843	19	5377	14	44459	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	10325	11	6577	16	7627	16	2432	24	7647	15	2109	26	36717	6
	fir	6475	13	2328	22	2527	20	446	55	167	69	287	51	12229	9
	pine	1731	18	170	63	228	54	239	46	543	38	17	100	2928	14
	larch	401	38	14	100	179	61	930	37	1447	23	904	34	3876	15
	Arolla pine	0	.	0	.	0	.	0	.	409	36	0	.	409	36
	other conifers	75	62	0	.	9	71	0	.	0	.	0	.	84	56
	beech	12491	8	2738	23	2765	19	128	83	263	53	761	35	19146	7
	maple	1346	14	309	28	683	22	13	73	16	72	2	100	2369	11
	ash	2846	20	525	40	886	30	0	0	2	100	120	78	4379	15
	oak	1382	18	28	100	19	100	5	100	59	100	92	75	1586	17
	sweet chestnut	0	.	7	100	0	.	0	.	0	.	547	55	554	54
	other broadleaves	899	21	234	43	232	40	55	56	8	77	203	52	1630	16
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	37970	6	12928	12	15157	12	4249	21	10561	13	5042	17	85908	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17159	9	16049	11	13649	13	4552	20	9154	14	4068	22	64632	5
	fir	14000	10	7177	14	3330	22	115	77	701	39	350	65	25672	7
	pine	1156	22	294	100	25	76	179	86	360	40	0	.	2015	22
	larch	196	40	68	67	229	51	3079	21	2752	23	1327	29	7652	13
	Arolla pine	0	.	0	.	0	.	230	53	119	61	0	.	349	41
	other conifers	722	54	71	100	-11	128	0	.	0	.	0	.	782	51
	beech	16715	8	4649	17	3794	19	27	100	625	47	689	43	26499	7
	maple	1317	17	522	22	608	25	68	62	36	117	0	.	2550	12
	ash	2974	16	454	37	565	38	29	86	38	100	0	.	4060	14
	oak	1995	25	0	.	58	60	0	0	0	.	14	100	2066	24
	sweet chestnut	2	100	0	.	0	.	0	.	0	.	384	56	385	56
	other broadleaves	1473	37	752	46	230	35	12	63	31	73	164	64	2661	25
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	57707	5	30036	9	22477	11	8291	15	13817	12	6995	16	139323	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7821	10	14076	10	4386	16	5088	15	7098	14	4318	18	42787	5
	fir	6474	13	4632	16	1582	23	624	39	332	37	860	39	14505	9
	pine	728	29	211	71	147	57	531	28	667	41	159	53	2443	17
	larch	121	55	26	83	33	92	3564	20	1208	19	2161	15	7112	11
	Arolla pine	0	.	-7	100	0	.	554	26	612	27	5	100	1164	19
	other conifers	316	65	5	85	-10	172	1	100	11	70	0	.	323	64
	beech	6232	11	3249	17	1432	24	515	43	339	44	1681	20	13449	8
	maple	1150	20	1094	19	421	36	126	49	281	59	65	65	3137	12
	ash	1722	18	460	36	513	35	135	56	117	60	158	41	3105	13
	oak	839	27	70	57	74	63	133	54	72	100	327	37	1516	19
	sweet chestnut	0	.	0	.	0	.	135	81	18	100	2028	23	2180	22
	other broadleaves	1129	26	390	26	392	41	230	41	175	37	1021	19	3337	12
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	0	0	0
	total	26533	7	24206	8	8970	14	11636	11	10927	11	12786	10	95058	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	48063	4	44393	6	30830	7	14247	9	28423	7	11670	10	177626	2
	fir	31110	5	15954	8	9276	11	1311	28	1341	24	1771	25	60763	4
	pine	4958	11	696	50	502	32	2020	19	2606	18	192	45	10974	8
	larch	1185	19	360	33	479	34	8917	11	6604	12	5930	11	23475	6
	Arolla pine	0	.	-7	100	0	.	958	22	1532	18	5	100	2489	13
	other conifers	1583	30	121	66	-2	1200	15	58	45	35	50	100	1813	27
	beech	44017	4	11731	9	9708	10	958	29	1304	27	5569	12	73286	3
	maple	5767	7	2943	11	2633	12	322	27	406	43	347	25	12416	5
	ash	10015	8	2075	19	2886	15	437	31	244	37	748	25	16407	6
	oak	5972	11	180	34	172	36	205	37	130	71	957	20	7615	9
	sweet chestnut	8	82	7	100	0	.	194	59	18	100	5342	12	5569	12
	other broadleaves	5443	13	1794	22	1358	17	987	18	986	21	4460	11	15029	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	158121	2	80247	4	57842	5	30572	7	43639	5	37041	4	407462	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. 1354524. Birmensdorf, Swiss Federal Research Institute WSL

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