Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI1
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 1983/85
		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	424	37	1375	35	1516	21	143	59	355	44	0	.	3812	16
	fir	163	66	177	53	950	38	33	100	17	100	0	.	1340	29
	pine	75	81	152	58	0	.	0	.	0	.	0	.	227	47
	larch	30	100	0	.	0	.	99	100	123	66	28	75	280	47
	Arolla pine	0	.	0	.	0	.	0	.	18	100	0	.	18	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	366	39	318	38	297	39	0	.	15	67	0	.	996	22
	maple	79	66	54	82	77	37	0	.	0	.	0	.	210	35
	ash	178	63	164	52	138	70	22	88	0	.	0	.	501	34
	oak	49	83	57	55	5	100	0	.	0	.	26	100	137	42
	sweet chestnut	0	.	0	.	18	100	25	100	0	.	0	.	43	71
	other broadleaves	89	56	176	50	78	52	26	68	3	100	12	65	384	29
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1453	30	2472	25	3080	20	348	46	531	37	66	52	7950	13
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	61	72	471	31	1501	23	157	61	623	41	75	56	2889	16
	fir	46	93	187	53	197	51	19	100	49	71	12	100	512	30
	pine	0	.	189	37	0	.	6	79	0	.	0	.	195	36
	larch	2	100	30	92	0	.	121	51	93	92	63	50	309	37
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	7	87	2	100	0	.	0	.	4	100	13	59
	beech	160	62	177	41	73	51	0	.	0	.	18	70	428	30
	maple	7	100	58	77	40	56	2	100	32	89	2	100	142	41
	ash	32	82	57	67	35	85	7	100	0	.	0	.	132	43
	oak	0	.	93	75	4	100	4	58	0	.	0	.	102	69
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	19	64	19	64
	other broadleaves	7	60	14	72	36	37	15	53	15	95	39	46	127	24
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	315	40	1285	22	1890	21	331	36	812	34	232	29	4866	12
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1454	23	3448	15	3851	17	975	27	914	29	241	63	10883	9
	fir	695	27	1119	26	551	24	30	97	22	94	84	71	2501	15
	pine	256	36	282	33	122	63	618	31	143	42	0	.	1421	18
	larch	13	100	231	42	28	86	449	35	153	35	171	46	1045	20
	Arolla pine	0	.	0	.	0	.	44	55	38	87	0	.	82	50
	other conifers	8	86	105	46	4	89	0	.	0	100	0	.	117	42
	beech	2349	17	1316	20	1109	21	125	87	63	76	818	27	5780	10
	maple	252	28	538	25	460	22	15	63	51	75	27	58	1342	14
	ash	384	30	990	19	557	27	11	71	126	62	70	36	2139	13
	oak	183	32	595	27	22	49	45	61	0	.	249	30	1094	17
	sweet chestnut	0	.	0	.	0	.	40	73	0	.	1576	18	1616	17
	other broadleaves	156	39	590	26	451	25	206	33	257	41	1501	17	3161	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5748	13	9215	10	7156	13	2557	19	1768	21	4736	11	31181	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3883	16	6838	15	10780	12	1509	35	1793	33	716	44	25519	8
	fir	2826	19	2195	20	2362	19	0	.	0	.	206	64	7589	11
	pine	762	28	790	27	121	52	92	45	733	39	0	.	2498	17
	larch	13	100	127	73	47	82	492	36	446	39	685	40	1810	21
	Arolla pine	0	.	0	.	0	.	55	70	73	71	0	.	128	50
	other conifers	3	72	127	77	3	72	0	.	0	.	0	.	132	74
	beech	4415	15	4362	15	3040	19	30	100	241	55	825	27	12912	8
	maple	648	19	427	27	591	24	25	100	94	94	8	100	1793	13
	ash	396	42	986	22	440	30	16	82	0	.	42	66	1880	16
	oak	268	33	784	26	4	100	1	100	0	.	130	61	1186	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	592	31	592	31
	other broadleaves	290	30	315	33	468	33	63	68	24	73	345	36	1507	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	13504	10	16950	10	17856	10	2283	27	3404	24	3548	18	57546	5
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5914	14	20479	9	21836	9	1741	26	8245	15	2153	26	60367	5
	fir	4101	15	7954	12	5540	15	20	100	563	36	293	50	18472	8
	pine	668	28	2670	20	406	67	250	64	665	34	97	100	4755	14
	larch	135	50	470	35	289	47	916	29	1395	23	316	40	3521	14
	Arolla pine	0	.	0	.	0	.	66	100	226	43	0	.	293	40
	other conifers	155	96	181	79	0	.	0	.	0	.	0	.	336	62
	beech	5914	11	8096	11	5426	15	15	100	466	34	304	53	20220	7
	maple	666	24	304	27	836	19	0	.	98	66	4	100	1908	13
	ash	287	33	1383	19	482	29	1	100	42	100	7	100	2202	15
	oak	240	39	1471	21	41	63	0	.	0	.	80	70	1833	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	308	54	308	54
	other broadleaves	398	33	558	24	421	27	4	100	57	60	66	54	1503	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	18478	9	43565	6	35277	8	3013	21	11756	12	3627	19	115717	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3732	18	10432	14	13581	12	1176	32	6080	17	2889	23	37891	7
	fir	2761	20	3415	18	3883	17	63	100	404	53	278	70	10804	10
	pine	128	60	916	31	0	.	44	72	401	46	95	65	1586	22
	larch	34	100	184	50	97	59	954	31	1324	32	1331	30	3923	17
	Arolla pine	0	.	0	.	0	.	48	100	254	58	18	100	319	49
	other conifers	1	100	137	84	1	100	0	.	0	.	0	.	139	83
	beech	3079	20	6079	15	2541	21	68	100	701	52	188	66	12657	10
	maple	428	30	293	29	257	35	0	.	31	95	0	.	1009	18
	ash	128	41	452	36	103	49	0	.	0	.	0	.	683	26
	oak	29	76	800	28	25	78	0	.	34	100	0	.	887	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	683	48	683	48
	other broadleaves	116	53	528	31	242	56	17	100	28	75	38	57	969	23
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	10436	13	23236	10	20730	11	2370	23	9257	14	5520	17	71549	5
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4774	14	1824	20	11669	10	3995	15	6124	13	1868	24	30254	6
	fir	2114	17	1735	23	4509	15	405	49	188	43	485	51	9437	10
	pine	291	33	152	40	291	51	742	27	793	29	73	74	2343	15
	larch	0	.	58	100	129	55	2270	17	1425	20	1112	20	4994	10
	Arolla pine	0	.	0	.	8	100	350	27	585	30	0	.	943	21
	other conifers	4	66	42	85	16	100	0	.	2	100	10	100	74	54
	beech	2343	17	1797	19	2339	17	152	47	222	63	945	24	7799	9
	maple	296	25	443	35	603	19	5	74	31	61	31	58	1409	15
	ash	316	38	1048	24	394	28	34	100	38	77	21	84	1852	16
	oak	36	78	591	25	23	97	7	61	36	100	194	57	886	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	628	36	628	36
	other broadleaves	123	29	710	26	377	25	64	43	39	65	348	34	1659	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	10298	10	8401	13	20356	8	8025	12	9483	11	5716	13	62279	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20242	6	44868	5	64733	4	9696	10	24135	7	7942	12	171616	2
	fir	12706	8	16782	8	17994	7	571	38	1243	25	1358	27	50654	4
	pine	2181	15	5151	12	940	35	1752	18	2735	17	265	48	13024	7
	larch	227	36	1100	22	590	29	5302	11	4958	13	3706	14	15882	6
	Arolla pine	0	.	0	.	8	100	563	23	1195	21	18	100	1783	16
	other conifers	171	87	599	36	25	64	0	.	3	84	15	77	813	32
	beech	18626	6	22143	6	14825	8	391	38	1708	26	3098	13	60791	3
	maple	2377	11	2117	12	2865	9	46	58	338	37	71	35	7814	6
	ash	1722	16	5081	9	2150	13	90	47	205	45	140	30	9388	7
	oak	804	19	4391	11	125	33	57	49	69	71	679	24	6125	9
	sweet chestnut	0	.	0	.	18	100	64	59	0	.	3805	13	3888	13
	other broadleaves	1179	16	2891	12	2073	13	394	22	424	28	2349	12	9311	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	60233	3	105123	3	106345	3	18928	8	37012	6	23447	5	351089	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. 1354491. Birmensdorf, Swiss Federal Research Institute WSL

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