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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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 NFI4/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	0	137	78
	pine	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	larch	0	.	13	100	0	.	47	71	135	50	91	91	285	39
	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	0
	total	88	76	12	225	718	38	80	70	370	36	296	91	1564	27
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	91	62	986	28	1702	23	199	45	796	41	233	50	4008	15
	fir	193	63	351	36	482	37	15	100	36	79	1	100	1077	23
	pine	47	100	5	100	16	100	0	0	70	79	0	.	138	54
	larch	0	.	72	71	131	66	254	51	64	68	86	75	606	30
	Arolla pine	0	.	0	.	0	.	0	.	2	100	0	.	2	100
	other conifers	0	.	3	100	1	100	5	100	3	76	0	.	12	54
	beech	184	49	954	21	435	38	2	100	14	100	18	73	1607	17
	maple	11	64	106	68	237	30	1	100	1	100	24	85	380	27
	ash	38	105	69	53	64	49	14	100	0	.	13	100	197	33
	oak	12	100	146	75	0	.	0	0	0	.	6	100	164	68
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	16	100	16	100
	other broadleaves	28	45	187	54	89	33	26	71	52	59	52	52	435	26
	indeterminable	0	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	602	36	2878	17	3156	18	517	36	1038	34	450	37	8642	10
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1684	24	4475	14	3491	17	727	35	1400	21	494	29	12269	8
	fir	618	39	1450	21	628	30	111	66	29	96	120	63	2954	15
	pine	53	71	320	33	56	78	457	48	403	32	15	100	1304	22
	larch	12	100	184	42	121	50	567	28	387	37	609	27	1881	15
	Arolla pine	0	.	0	.	0	.	77	98	102	66	0	.	179	56
	other conifers	42	79	99	41	45	83	1	100	30	45	57	89	275	30
	beech	930	36	1235	17	593	31	18	81	1	100	780	29	3557	14
	maple	254	30	717	25	404	23	80	43	54	66	188	34	1697	13
	ash	275	40	473	23	445	30	242	44	48	51	206	33	1689	14
	oak	97	55	328	48	27	91	34	57	0	.	299	39	785	26
	sweet chestnut	0	.	0	0	25	100	8	100	0	.	1571	23	1605	22
	other broadleaves	225	36	514	20	645	24	401	24	525	31	2003	18	4313	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	4189	17	9795	10	6480	12	2723	19	2980	16	6342	11	32509	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2308	24	4269	17	6029	16	1072	40	2225	24	348	42	16251	9
	fir	730	23	1517	26	1660	27	0	.	110	58	171	65	4188	15
	pine	561	44	321	38	88	54	614	35	562	47	0	.	2146	21
	larch	33	76	154	54	11	100	454	38	590	31	820	36	2062	19
	Arolla pine	0	.	0	.	0	.	70	61	288	43	0	.	358	36
	other conifers	15	100	301	54	20	67	1	100	1	100	0	.	337	48
	beech	3347	16	1952	20	1802	20	102	58	103	58	1640	26	8946	10
	maple	343	25	510	26	1216	22	12	100	91	69	68	57	2240	14
	ash	404	34	1260	23	1008	30	10	85	40	90	251	56	2972	15
	oak	284	38	904	27	54	41	30	50	0	.	219	33	1491	19
	sweet chestnut	0	.	6	100	0	.	26	100	0	.	797	31	829	30
	other broadleaves	294	41	690	29	176	38	220	52	130	52	1130	26	2638	15
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	total	8320	13	11883	11	12063	12	2610	22	4140	18	5443	14	44459	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4646	15	6799	15	13305	11	2201	26	7646	15	2121	26	36717	6
	fir	4142	18	2894	18	4508	15	230	74	167	69	287	51	12229	9
	pine	611	31	1235	21	283	52	239	46	543	38	17	100	2928	14
	larch	9	100	458	35	34	58	930	37	1510	23	935	33	3876	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	6967	11	6161	13	4832	16	128	83	297	48	761	35	19146	7
	maple	782	18	702	20	863	18	5	100	16	72	2	100	2369	11
	ash	656	32	2434	23	1112	28	0	0	56	97	120	78	4379	15
	oak	417	32	972	22	40	71	5	100	59	100	92	75	1586	17
	sweet chestnut	0	.	0	.	7	100	0	.	0	.	547	55	554	54
	other broadleaves	334	33	569	27	448	30	53	58	23	71	203	52	1630	16
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	.	0	0
	total	18588	9	22282	9	25436	9	3792	22	10725	12	5083	17	85908	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5974	15	12798	11	29380	8	3164	22	9248	14	4068	22	64632	5
	fir	5792	15	9397	12	9160	12	101	88	873	37	350	65	25672	7
	pine	178	46	994	24	302	97	179	86	360	40	0	.	2015	22
	larch	100	54	141	51	177	51	2670	22	2827	23	1736	27	7652	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	6083	13	11557	10	7345	13	0	.	827	39	689	43	26499	7
	maple	781	21	512	27	1135	16	11	100	111	77	0	.	2550	12
	ash	434	33	2686	18	898	26	4	100	38	100	0	.	4060	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	181	39	1379	40	895	39	12	63	31	73	164	64	2661	25
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0
	total	19870	9	41933	6	49311	7	6371	17	14435	12	7404	15	139323	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5961	12	2189	20	18356	9	4750	15	7213	13	4318	18	42787	5
	fir	4777	15	2662	20	5331	14	440	48	436	32	860	39	14505	9
	pine	353	32	375	47	288	55	487	29	737	38	203	47	2443	17
	larch	49	100	72	62	56	66	3528	20	1119	19	2289	15	7112	11
	Arolla pine	0	.	0	.	-7	100	554	26	557	28	60	92	1164	19
	other conifers	17	148	299	69	-5	350	1	100	11	70	0	.	323	64
	beech	4224	14	2460	17	4268	14	371	53	445	42	1681	20	13449	8
	maple	873	20	396	41	1367	17	67	74	369	51	65	65	3137	12
	ash	742	29	1145	23	863	25	80	84	117	60	158	41	3105	13
	oak	115	45	726	31	161	40	114	61	0	.	399	35	1516	19
	sweet chestnut	0	.	0	.	104	100	30	100	0	.	2046	22	2180	22
	other broadleaves	254	24	1003	31	697	23	185	47	176	36	1022	19	3337	12
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	0	0	0
	total	17364	8	11326	12	31479	7	10607	12	11180	11	13101	10	95058	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20710	6	31499	6	72772	4	12118	10	28740	7	11788	10	177626	2
	fir	16251	8	18270	8	21906	7	897	33	1650	23	1788	25	60763	4
	pine	1802	19	3250	13	1034	36	1977	19	2676	18	235	41	10974	8
	larch	204	39	1093	20	530	27	8451	12	6631	12	6566	11	23475	6
	Arolla pine	0	.	0	.	-7	100	958	22	1478	18	60	92	2489	13
	other conifers	290	66	1346	33	67	65	8	66	45	35	57	89	1813	27
	beech	21761	5	24321	6	19302	7	622	37	1711	24	5569	12	73286	3
	maple	3056	9	2943	11	5252	8	177	36	641	34	347	25	12416	5
	ash	2549	14	8067	10	4395	12	349	37	299	35	748	25	16407	6
	oak	1077	18	4969	13	298	26	184	40	59	100	1028	20	7615	9
	sweet chestnut	2	100	6	100	137	79	65	63	0	.	5360	12	5569	12
	other broadleaves	1317	15	4345	16	2960	15	896	20	936	20	4574	11	15029	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	69020	3	100110	3	128644	3	26701	7	44867	5	38120	4	407462	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1231039. Birmensdorf, Swiss Federal Research Institute WSL

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