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: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
development stage	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	47	100	-16	113	509	44	6	100	211	43	205	91	961	32
	fir	0	.	0	.	137	78	0	.	0	.	0	.	137	78
	pine	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	larch	0	.	13	100	0	.	47	71	135	50	83	100	276	41
	Arolla pine	0	.	0	.	0	.	27	100	0	0	0	.	27	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	27	74	3	1012	29	65	0	.	24	100	0	.	82	55
	maple	13	100	0	.	31	61	0	.	0	.	0	.	44	52
	ash	0	.	0	.	4	100	0	.	0	.	0	.	4	100
	oak	0	.	9	100	0	.	0	.	0	.	0	.	9	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	1	100	4	100	9	72	0	.	0	.	0	.	14	53
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	.	0	0
	total	88	76	12	225	718	38	80	70	370	36	288	94	1555	27
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	91	62	957	28	1619	24	175	50	643	48	145	70	3630	16
	fir	193	63	351	36	460	38	15	100	36	79	1	100	1055	24
	pine	47	100	5	100	16	100	0	0	67	83	0	.	135	55
	larch	0	.	72	71	131	66	250	52	60	71	4	100	516	33
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	3	100	1	100	5	100	0	.	0	.	9	68
	beech	184	49	941	22	425	39	2	100	0	.	13	100	1564	18
	maple	11	64	106	68	229	31	1	100	1	100	20	100	368	28
	ash	38	105	69	53	64	49	14	100	0	.	0	.	184	35
	oak	12	100	21	96	0	.	0	0	0	.	0	.	32	71
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	16	100	16	100
	other broadleaves	28	45	168	59	82	35	24	77	19	63	15	90	335	32
	indeterminable	0	0	0	0	0	0	0	.	0	.	0	.	0	0
	total	602	36	2691	17	3026	18	487	38	825	40	214	64	7845	11
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1565	26	4475	14	2630	20	647	38	1015	25	316	34	10648	9
	fir	618	39	1450	21	628	30	87	80	29	96	101	72	2912	15
	pine	53	71	320	33	47	91	439	50	338	36	15	100	1213	23
	larch	12	100	184	42	121	50	421	31	203	49	405	34	1346	18
	Arolla pine	0	.	0	.	0	.	77	98	61	98	0	.	138	70
	other conifers	42	79	99	41	8	100	1	100	28	48	57	89	235	32
	beech	923	36	1235	17	583	32	14	100	1	100	754	30	3511	14
	maple	211	30	717	25	288	28	68	49	54	66	133	41	1471	15
	ash	244	43	462	23	338	27	78	53	19	72	191	35	1331	14
	oak	97	55	328	48	25	100	34	57	0	.	296	39	780	26
	sweet chestnut	0	.	0	0	25	100	8	100	0	.	1503	23	1537	23
	other broadleaves	183	41	496	20	438	29	368	26	453	36	1540	19	3478	11
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	3948	17	9765	10	5131	14	2243	22	2200	19	5312	12	28600	6
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2308	24	4137	18	5474	17	1034	41	1891	27	357	41	15203	10
	fir	730	23	1349	27	1454	27	0	.	110	58	171	65	3815	15
	pine	561	44	321	38	88	54	608	36	562	47	0	.	2140	21
	larch	33	76	154	54	11	100	427	40	579	32	731	40	1934	20
	Arolla pine	0	.	0	.	0	.	17	77	212	45	0	.	228	42
	other conifers	15	100	301	54	20	67	1	100	1	100	0	.	337	48
	beech	3347	16	1928	21	1744	20	102	58	103	58	1611	26	8835	10
	maple	343	25	510	26	1153	23	12	100	91	69	67	58	2175	14
	ash	404	34	1260	23	1008	30	10	85	40	90	251	56	2972	15
	oak	284	38	904	27	44	45	30	50	0	.	219	33	1480	19
	sweet chestnut	0	.	6	100	0	.	26	100	0	.	797	31	829	30
	other broadleaves	294	41	690	29	171	39	136	58	130	52	1087	27	2507	16
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	total	8320	13	11560	11	11166	12	2402	23	3718	19	5289	14	42455	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4619	15	6799	15	12411	12	2201	26	7523	15	2121	26	35674	6
	fir	4142	18	2894	18	4459	16	230	74	167	69	287	51	12180	9
	pine	611	31	1235	21	229	60	239	46	543	38	17	100	2874	15
	larch	9	100	458	35	34	58	661	33	1206	25	935	33	3303	15
	Arolla pine	0	.	0	.	0	.	0	.	409	36	0	.	409	36
	other conifers	23	69	58	76	3	75	0	.	0	.	0	.	84	56
	beech	6887	11	6161	13	4463	15	128	83	297	48	761	35	18696	7
	maple	750	19	702	20	821	19	5	100	16	72	2	100	2295	11
	ash	656	32	2434	23	1109	28	0	0	56	97	120	78	4376	15
	oak	417	32	972	22	0	.	5	100	59	100	92	75	1546	17
	sweet chestnut	0	.	0	.	7	100	0	.	0	.	547	55	554	54
	other broadleaves	334	33	569	27	420	31	51	60	20	78	203	52	1597	16
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	.	0	0
	total	18449	9	22282	9	23957	9	3521	22	10297	13	5083	17	83589	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5974	15	12798	11	28092	8	3116	22	9084	14	4022	22	63086	5
	fir	5790	15	9397	12	8966	12	101	88	873	37	350	65	25475	7
	pine	178	46	994	24	302	97	179	86	360	40	0	.	2015	22
	larch	100	54	141	51	177	51	2613	22	2689	23	1595	29	7315	13
	Arolla pine	0	.	0	.	0	.	230	53	119	61	0	.	349	41
	other conifers	193	96	587	60	3	100	0	.	0	.	0	.	782	51
	beech	5371	13	11557	10	7206	14	0	.	827	39	689	43	25648	7
	maple	775	21	512	27	1071	17	11	100	111	77	0	.	2480	12
	ash	406	34	2685	18	770	29	4	100	38	100	0	.	3903	14
	oak	153	47	1883	26	16	100	0	.	0	.	14	100	2066	24
	sweet chestnut	2	100	0	.	0	.	0	.	0	.	384	56	385	56
	other broadleaves	160	43	768	37	685	41	8	80	10	87	164	64	1795	23
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0
	total	19100	9	41322	6	47287	7	6263	17	14112	12	7217	16	135299	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5660	12	2189	20	16019	9	4632	16	6354	14	3914	20	38768	6
	fir	4646	15	2662	20	5201	15	440	48	423	33	846	40	14218	9
	pine	353	32	375	47	227	68	486	30	658	41	175	52	2274	18
	larch	49	100	72	62	51	72	3291	21	876	21	1689	18	6028	13
	Arolla pine	0	.	0	.	-7	100	473	28	486	31	60	92	1011	21
	other conifers	17	148	299	69	-5	350	1	100	10	73	0	.	322	64
	beech	4179	14	2460	17	4120	15	371	53	336	44	1590	20	13057	8
	maple	863	20	396	41	1210	18	50	93	218	51	60	69	2798	12
	ash	742	29	1145	23	729	28	80	84	117	60	93	49	2905	14
	oak	115	45	726	31	142	44	94	72	0	.	345	40	1422	20
	sweet chestnut	0	.	0	.	0	.	30	100	0	.	2041	22	2071	22
	other broadleaves	252	24	875	32	579	25	167	52	168	38	906	21	2948	13
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	0	0	0
	total	16876	9	11198	12	28267	8	10116	12	9645	12	11719	11	87821	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20264	6	31338	6	66754	4	11811	10	26722	7	11081	11	167969	2
	fir	16119	8	18103	8	21305	7	873	34	1638	23	1755	25	59792	4
	pine	1802	19	3250	13	911	40	1952	20	2528	19	207	45	10650	8
	larch	204	39	1093	20	525	28	7709	12	5747	13	5440	12	20718	7
	Arolla pine	0	.	0	.	-7	100	823	24	1287	19	60	92	2164	15
	other conifers	290	66	1346	33	29	80	8	66	39	39	57	89	1770	27
	beech	20917	5	24284	6	18569	7	618	37	1588	24	5418	12	71394	3
	maple	2965	9	2943	11	4802	9	148	40	491	32	281	29	11631	5
	ash	2490	15	8055	10	4021	13	185	43	270	39	655	28	15676	7
	oak	1077	18	4844	13	227	32	164	44	59	100	966	21	7336	10
	sweet chestnut	2	100	6	100	33	80	65	63	0	.	5287	12	5391	12
	other broadleaves	1253	16	3569	13	2384	15	755	20	800	23	3914	11	12674	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	67382	3	98830	3	119552	3	25111	7	41168	6	35121	4	387165	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. 1231027. Birmensdorf, Swiss Federal Research Institute WSL

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