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
number of stems
development stage · main tree species
regional demarcation: biogeographical region
unit: 1000 n
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 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	370	27	535	28	2090	16	174	43	620	45	30	71	3820	12
	fir	242	30	135	44	714	29	8	100	10	71	0	.	1109	20
	pine	87	64	104	58	20	100	10	100	0	.	0	.	221	38
	larch	20	100	0	.	0	.	170	80	157	63	33	59	380	45
	Arolla pine	0	.	0	.	0	.	31	88	27	100	0	.	58	66
	other conifers	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	beech	732	24	428	28	579	31	0	.	24	67	147	93	1910	16
	maple	257	32	143	39	377	29	33	100	0	.	51	73	861	18
	ash	223	43	233	39	433	46	105	49	0	.	80	64	1074	23
	oak	92	45	150	53	34	89	0	.	0	.	14	100	290	33
	sweet chestnut	0	.	0	.	8	100	24	100	0	.	30	64	62	51
	other broadleaves	237	36	290	38	547	24	109	46	103	61	183	34	1469	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2261	18	2022	20	4802	14	664	31	941	39	568	32	11258	9
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	164	39	978	25	1873	17	148	41	519	31	273	33	3955	11
	fir	171	45	278	30	166	40	70	57	35	87	37	74	757	19
	pine	21	100	101	41	41	100	62	62	41	100	0	.	266	31
	larch	20	100	62	67	7	100	130	33	110	42	177	34	506	19
	Arolla pine	0	.	0	.	0	.	0	.	19	100	0	.	19	100
	other conifers	0	.	19	71	21	71	0	.	10	100	51	100	101	55
	beech	152	39	348	28	207	32	20	100	0	.	115	44	843	17
	maple	49	61	129	41	214	36	10	100	55	71	19	71	476	22
	ash	134	63	90	42	67	44	38	100	0	.	107	74	436	30
	oak	0	.	52	49	21	71	109	60	0	.	0	.	182	40
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	105	47	105	47
	other broadleaves	185	45	247	36	359	30	212	40	243	54	381	28	1626	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	895	26	2305	16	2976	14	799	22	1032	23	1266	18	9273	8
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3860	21	12716	11	11279	12	2261	25	3040	23	1176	37	34332	7
	fir	1558	22	2858	21	1973	21	423	59	60	83	83	95	6955	12
	pine	541	38	591	32	626	47	2779	24	1197	35	9	100	5743	15
	larch	59	86	524	39	74	59	1442	24	676	30	1033	29	3808	14
	Arolla pine	0	.	0	.	0	.	357	50	271	66	0	.	628	40
	other conifers	30	74	368	43	102	52	0	.	10	100	0	.	510	33
	beech	6328	13	4335	15	4432	17	379	56	311	57	4325	18	20111	8
	maple	741	19	1615	23	1601	17	142	39	117	50	303	36	4519	11
	ash	1028	23	2480	16	1811	20	87	55	387	56	1108	21	6900	10
	oak	1298	29	1941	26	365	43	228	47	0	.	1555	24	5387	14
	sweet chestnut	0	.	110	100	39	71	63	83	0	.	7337	13	7550	13
	other broadleaves	886	28	2612	17	3317	18	2027	25	1188	27	8516	10	18546	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	16329	10	30150	7	25619	9	10190	13	7256	15	25445	7	114989	4
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5601	12	7648	11	16946	9	3363	21	3610	19	1207	32	38375	6
	fir	4505	13	2857	16	3183	14	0	.	155	69	174	79	10875	8
	pine	875	23	819	24	258	44	821	46	977	31	226	71	3976	15
	larch	10	100	246	47	52	59	1142	23	385	31	659	32	2494	15
	Arolla pine	0	.	0	.	0	.	409	47	166	55	0	.	575	37
	other conifers	42	60	101	48	83	49	0	.	0	.	0	.	225	30
	beech	8502	10	7293	11	5664	12	192	76	334	51	2103	22	24088	6
	maple	1437	14	1003	20	1560	17	54	100	132	86	44	71	4230	10
	ash	684	29	1396	19	1026	21	83	73	0	.	139	45	3328	12
	oak	635	30	848	22	133	63	20	71	0	.	189	43	1826	16
	sweet chestnut	0	.	20	100	0	.	0	.	0	.	1293	26	1313	26
	other broadleaves	1056	18	1096	30	1448	17	117	50	109	54	1029	26	4855	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	23348	7	23326	7	30353	7	6202	16	5868	16	7063	13	96160	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4633	11	13158	7	17529	7	2840	17	9105	11	2178	18	49442	4
	fir	4375	11	6575	9	5263	11	174	70	475	27	391	36	17252	6
	pine	641	25	1315	16	172	52	287	44	599	26	110	56	3125	11
	larch	51	51	225	26	79	41	1263	23	1173	19	504	27	3293	12
	Arolla pine	0	.	0	.	0	.	169	52	311	38	0	.	480	30
	other conifers	83	54	133	43	21	100	0	.	0	.	0	.	237	32
	beech	6787	9	8651	8	6119	10	81	63	1129	26	364	36	23131	5
	maple	1186	15	850	20	1329	15	0	.	119	55	19	100	3503	9
	ash	392	23	1539	14	692	22	10	100	17	100	33	74	2682	11
	oak	391	23	1014	17	83	44	0	.	4	100	91	51	1583	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	685	35	685	35
	other broadleaves	865	19	1001	16	898	22	51	53	99	49	285	34	3200	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	19403	7	34460	5	32185	6	4875	15	13030	9	4660	13	108614	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1976	14	4124	11	7699	10	728	24	3554	12	1766	17	19846	5
	fir	2156	15	2098	14	2231	15	109	87	248	36	189	38	7031	8
	pine	118	47	409	22	20	100	73	79	270	49	47	65	938	20
	larch	24	85	53	42	147	73	622	27	534	25	634	22	2014	14
	Arolla pine	0	.	0	.	0	.	43	71	352	39	29	71	424	34
	other conifers	9	100	76	51	10	100	0	.	0	.	0	.	96	43
	beech	1934	14	4158	10	2100	14	106	100	278	38	696	32	9272	7
	maple	436	31	304	23	546	27	0	.	21	71	4	100	1311	16
	ash	210	33	525	23	104	39	0	.	0	.	0	.	839	17
	oak	84	59	694	18	18	64	0	.	15	100	0	.	812	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	571	40	571	40
	other broadleaves	323	31	878	21	337	37	170	64	60	71	167	41	1936	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7270	10	13319	8	13213	8	1851	20	5333	11	4103	14	45089	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4848	11	1921	15	15893	7	6632	12	10498	9	2663	16	42454	4
	fir	3284	13	1729	19	5771	10	597	34	299	29	267	41	11947	7
	pine	452	27	287	32	899	26	2041	21	2618	19	146	67	6443	11
	larch	0	.	47	69	83	35	2574	13	1661	14	1783	15	6148	8
	Arolla pine	0	.	0	.	157	66	610	23	1091	21	4	100	1862	15
	other conifers	178	66	90	48	87	100	0	.	337	51	10	100	703	33
	beech	4660	13	3269	14	5417	11	214	53	397	41	2558	19	16515	7
	maple	727	18	811	23	1893	14	38	79	133	50	139	39	3741	10
	ash	646	27	1344	19	1310	19	58	74	167	44	96	42	3621	11
	oak	122	39	658	28	102	48	88	46	145	63	372	55	1487	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	1230	30	1230	30
	other broadleaves	834	21	1832	18	2329	14	459	28	463	28	1894	16	7812	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	15751	8	11988	9	33941	6	13312	9	17810	8	11162	9	103964	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21452	5	41079	4	73309	3	16146	8	30946	5	9293	9	192225	2
	fir	16290	5	16530	6	19302	5	1381	26	1282	17	1141	21	55925	3
	pine	2736	13	3627	10	2036	20	6073	14	5702	13	538	37	20712	6
	larch	183	37	1156	21	442	29	7343	9	4696	9	4823	10	18643	5
	Arolla pine	0	.	0	.	157	66	1620	19	2237	16	33	64	4047	12
	other conifers	342	38	791	24	324	35	0	.	357	48	62	85	1876	17
	beech	29095	4	28481	4	24518	5	992	31	2473	17	10310	10	95870	2
	maple	4833	7	4855	10	7520	7	277	32	578	28	577	23	18641	4
	ash	3317	12	7606	8	5444	10	382	29	570	40	1561	17	18881	5
	oak	2623	16	5357	11	757	25	446	30	165	57	2221	20	11567	8
	sweet chestnut	0	.	131	86	47	61	88	66	0	.	11252	10	11517	9
	other broadleaves	4386	9	7957	9	9234	8	3145	17	2264	17	12457	8	39444	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	85257	3	117570	2	143090	2	37893	5	51270	4	54268	3	489347	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. 1356597. Birmensdorf, Swiss Federal Research Institute WSL

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