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
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 2009/17
		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	24	100	53	53	668	28	110	52	331	39	117	56	1303	19
	fir	0	.	25	87	197	51	31	100	4	100	0	0	257	41
	pine	0	.	0	.	0	0	26	100	0	.	0	.	26	100
	larch	0	.	8	71	0	.	72	63	20	53	13	76	114	42
	Arolla pine	0	.	0	.	0	.	23	100	0	0	0	.	23	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	25	86	79	42	88	41	0	.	4	100	8	100	205	27
	maple	15	100	20	100	41	55	0	.	0	.	0	.	76	44
	ash	0	.	4	100	10	100	0	.	16	100	0	.	30	64
	oak	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	11	100	23	71	25	62	0	.	33	72	0	0	92	38
	indeterminable	0	.	0	.	0	0	0	.	0	0	0	0	0	0
	total	75	58	217	34	1029	24	263	41	408	33	137	51	2130	15
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	90	40	832	21	1952	14	206	32	1003	21	205	36	4288	9
	fir	266	30	419	22	482	23	30	100	69	50	11	100	1277	14
	pine	33	51	75	50	14	79	38	78	95	51	0	.	255	28
	larch	0	.	51	52	38	58	195	33	163	31	166	36	614	17
	Arolla pine	0	.	0	.	17	100	0	.	34	72	0	.	51	58
	other conifers	0	.	11	100	102	74	41	100	61	62	0	.	214	44
	beech	299	25	799	16	414	22	21	100	42	79	36	63	1611	11
	maple	69	42	223	26	497	23	10	100	10	100	14	77	824	16
	ash	109	44	152	28	173	34	40	79	0	.	19	100	493	19
	oak	4	100	91	36	0	.	0	0	0	.	4	100	98	34
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	59	90	59	90
	other broadleaves	85	34	422	21	583	22	276	35	256	45	276	41	1898	13
	indeterminable	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	total	956	19	3074	11	4273	11	858	25	1732	17	790	23	11684	6
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4996	19	10675	11	10354	12	1993	23	6899	16	1821	23	36737	6
	fir	1135	26	3712	18	1353	24	82	62	18	63	341	61	6641	12
	pine	417	45	329	48	624	43	1714	31	2419	23	14	100	5517	15
	larch	48	74	544	40	396	39	2279	21	1777	27	2614	18	7657	11
	Arolla pine	0	.	0	.	48	100	185	55	331	49	10	100	573	34
	other conifers	126	40	250	28	168	73	10	100	416	34	29	76	1000	21
	beech	2693	20	3216	12	2084	20	88	64	115	59	3769	20	11964	9
	maple	1372	21	3079	14	2473	17	386	31	273	52	954	25	8536	9
	ash	610	27	2507	14	1632	21	534	31	268	34	1260	20	6810	9
	oak	473	53	898	31	243	66	739	35	4	100	924	30	3281	17
	sweet chestnut	0	.	0	0	56	87	6	100	0	.	4573	19	4634	19
	other broadleaves	827	25	2884	19	3392	16	2673	18	3189	20	8944	12	21908	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	12696	11	28093	7	22822	8	10689	12	15709	10	25251	8	115260	3
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3146	18	5667	12	9186	12	2658	26	4767	17	1326	29	26750	7
	fir	1558	17	1699	21	1478	23	207	64	115	51	250	52	5307	11
	pine	640	32	335	34	114	60	1367	26	1029	30	93	100	3578	15
	larch	26	64	218	37	23	84	987	24	792	27	1153	24	3199	13
	Arolla pine	0	.	0	.	0	.	261	44	682	38	0	.	942	30
	other conifers	121	67	313	40	142	77	10	100	10	100	10	100	606	31
	beech	5104	12	3013	14	3557	17	604	43	497	43	3606	20	16380	8
	maple	789	17	863	22	2281	18	229	41	92	54	297	39	4551	11
	ash	601	24	1530	19	1052	24	67	54	159	59	953	33	4362	12
	oak	984	31	762	25	99	36	127	42	0	.	550	32	2521	16
	sweet chestnut	0	.	4	100	0	.	30	100	0	.	1946	23	1980	22
	other broadleaves	849	26	1052	19	1193	25	804	29	432	30	3146	16	7476	9
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	13820	10	15455	8	19125	9	7349	15	8574	13	13332	10	77655	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3310	12	4712	12	11279	9	2602	18	7291	12	2424	20	31618	5
	fir	3709	12	1948	15	3410	13	209	50	245	38	333	38	9854	7
	pine	460	20	510	18	315	46	263	37	693	27	153	74	2395	13
	larch	4	100	228	29	41	41	859	27	920	20	976	25	3028	13
	Arolla pine	0	.	0	.	96	100	122	76	399	32	0	.	617	30
	other conifers	231	64	99	39	40	50	0	.	126	78	0	.	496	37
	beech	7301	9	5441	10	4800	14	232	67	578	31	1453	25	19804	6
	maple	1148	13	878	18	1472	14	10	100	49	45	36	69	3594	8
	ash	606	28	1231	17	902	19	0	0	100	53	121	51	2960	11
	oak	423	29	501	21	127	71	10	100	86	77	65	43	1212	16
	sweet chestnut	0	.	0	.	8	71	0	.	0	.	620	38	628	38
	other broadleaves	887	19	773	20	1110	16	246	48	136	47	526	30	3679	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	18079	7	16321	7	23600	7	4554	16	10622	10	6706	12	79883	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3276	11	4791	8	13722	7	2659	16	5883	11	2366	15	32697	4
	fir	3915	10	4584	9	5293	9	109	60	278	32	228	52	14407	5
	pine	143	37	390	19	105	68	227	50	173	37	22	100	1060	16
	larch	20	45	129	32	200	39	1313	17	1221	20	817	19	3700	10
	Arolla pine	0	.	0	.	0	.	124	45	191	34	0	.	315	27
	other conifers	51	52	213	32	42	61	0	.	0	.	0	.	307	25
	beech	4407	8	7481	7	5919	9	14	77	540	27	836	30	19197	5
	maple	1062	15	995	14	1359	13	70	71	107	52	93	70	3687	8
	ash	435	23	1321	14	774	18	48	60	62	59	11	100	2652	10
	oak	211	28	736	18	63	40	0	.	0	.	38	64	1049	14
	sweet chestnut	10	100	10	100	0	.	0	.	0	.	393	29	413	28
	other broadleaves	692	21	1265	18	822	18	169	42	55	51	549	23	3552	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	14224	7	21915	5	28298	5	4733	13	8510	9	5353	11	83034	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5658	10	2168	14	19933	7	6847	11	11114	10	4799	13	50520	4
	fir	5300	11	2233	15	5960	10	561	34	405	28	592	28	15050	6
	pine	255	27	204	30	913	36	888	27	1082	24	194	60	3536	14
	larch	22	71	35	51	135	67	3639	12	1789	15	3167	13	8787	8
	Arolla pine	0	.	0	.	81	89	1467	19	1544	20	106	60	3198	13
	other conifers	223	45	136	43	434	43	10	100	226	64	30	100	1058	25
	beech	5211	12	2850	12	6727	11	374	56	699	32	3502	17	19362	6
	maple	1632	16	631	22	2772	13	99	60	440	38	329	35	5903	9
	ash	779	21	1189	18	1465	16	262	70	317	58	639	26	4650	10
	oak	222	34	430	23	138	32	309	44	42	67	509	26	1649	14
	sweet chestnut	0	.	76	100	130	100	4	100	0	.	3013	16	3223	16
	other broadleaves	1121	17	1332	18	2795	15	710	22	617	24	3820	12	10396	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	20423	7	11284	9	41482	5	15171	8	18273	8	20700	7	127334	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20500	6	28899	5	67094	3	17075	7	37288	5	13058	8	183914	2
	fir	15883	5	14620	6	18173	5	1228	22	1134	16	1755	19	52794	3
	pine	1949	15	1843	13	2085	22	4523	15	5491	13	476	40	16367	7
	larch	120	36	1214	20	834	24	9345	8	6682	10	8905	8	27100	5
	Arolla pine	0	.	0	.	241	54	2182	15	3180	14	116	55	5720	10
	other conifers	752	27	1022	17	927	28	71	63	840	27	69	56	3680	12
	beech	25040	5	22879	4	23590	5	1332	28	2474	16	13209	9	88523	3
	maple	6087	7	6690	8	10894	7	805	21	970	24	1724	17	27171	4
	ash	3140	10	7934	7	6009	9	952	27	920	25	3003	14	21958	4
	oak	2318	18	3422	11	669	29	1185	25	131	54	2090	17	9815	8
	sweet chestnut	10	100	89	86	193	72	40	77	0	.	10604	10	10937	10
	other broadleaves	4473	9	7750	9	9921	8	4877	12	4718	15	17261	7	49001	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	80272	3	96360	2	140630	2	43617	5	63829	4	72270	3	496978	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. 1283877. Birmensdorf, Swiss Federal Research Institute WSL

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