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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NFI5
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 2018/26
		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	0	0	116	76	316	57	0	0	464	43	76	65	972	30
	fir	0	.	13	100	140	73	0	.	0	.	0	0	153	68
	pine	55	77	33	71	0	.	8	100	40	80	0	.	135	43
	larch	0	.	0	.	0	.	23	100	51	71	87	63	161	43
	Arolla pine	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	59	97	54	73	60	72	0	.	0	.	0	.	174	47
	maple	0	.	4	100	50	58	0	.	0	.	0	.	54	54
	ash	0	.	10	100	0	.	0	.	0	.	0	.	10	100
	oak	0	.	67	80	0	.	0	.	0	.	0	0	67	80
	sweet chestnut	0	.	0	.	6	100	0	.	0	.	0	0	6	100
	other broadleaves	0	.	34	100	0	.	0	.	0	.	0	0	34	100
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	.	0	0
	total	114	86	331	46	573	44	31	79	555	39	162	47	1766	22
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	353	43	579	33	1481	22	78	59	1446	32	421	51	4359	15
	fir	131	45	229	43	362	34	0	0	0	.	0	0	722	23
	pine	20	100	80	62	26	75	0	.	76	73	4	100	206	39
	larch	23	96	22	100	127	78	42	87	168	54	169	63	551	32
	Arolla pine	0	.	0	.	0	.	0	.	27	100	0	.	27	100
	other conifers	0	.	19	68	6	78	0	.	2	100	0	.	27	51
	beech	106	38	592	35	751	47	0	.	19	100	17	100	1485	28
	maple	83	56	70	53	141	34	0	.	17	88	0	.	310	25
	ash	44	97	12	100	73	62	0	100	0	.	0	.	129	49
	oak	23	100	141	57	0	.	0	.	0	.	0	.	164	51
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	10	56	139	56	92	44	7	79	59	72	59	87	364	30
	indeterminable	0	.	0	.	0	0	0	.	0	0	0	0	0	0
	total	792	27	1883	22	3059	18	126	47	1813	28	678	46	8352	11
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	745	25	4015	15	3776	16	461	35	1608	22	558	34	11163	9
	fir	1005	31	1574	21	1091	26	73	71	47	81	128	56	3918	14
	pine	76	63	155	48	114	65	190	44	361	30	0	.	896	20
	larch	0	.	351	34	125	47	627	33	415	38	672	29	2191	16
	Arolla pine	0	.	0	.	0	.	37	79	146	43	0	.	184	38
	other conifers	64	65	109	75	51	70	7	71	29	44	6	100	266	38
	beech	860	23	1828	14	1052	27	10	65	23	89	928	26	4699	11
	maple	394	25	690	25	737	18	154	42	2	100	166	39	2143	12
	ash	163	50	580	20	407	28	205	48	39	74	205	42	1600	14
	oak	91	55	234	35	48	64	73	49	0	.	221	35	666	20
	sweet chestnut	0	.	0	.	26	100	0	.	0	.	953	28	979	28
	other broadleaves	81	28	787	28	582	22	554	33	533	31	1401	20	3937	11
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	3478	16	10322	9	8010	11	2390	18	3204	16	5238	11	32642	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1898	26	4738	15	5293	17	1569	33	3059	22	555	34	17112	9
	fir	853	27	1537	23	1150	28	8	100	187	60	210	68	3945	14
	pine	269	46	297	34	66	71	733	36	407	52	78	86	1850	21
	larch	26	71	146	48	52	100	763	27	559	34	540	37	2087	17
	Arolla pine	0	.	0	.	0	.	131	76	287	47	8	100	426	39
	other conifers	37	82	289	38	36	80	1	100	2	76	0	.	365	32
	beech	2585	20	1718	19	1650	27	66	61	187	58	2065	24	8271	11
	maple	245	41	749	21	1177	22	0	.	157	48	144	52	2472	14
	ash	184	35	624	28	853	37	37	61	41	76	386	59	2125	20
	oak	132	62	617	35	23	61	29	60	0	.	399	38	1199	23
	sweet chestnut	0	.	0	.	0	.	42	78	5	100	1088	26	1134	25
	other broadleaves	278	42	536	26	234	28	175	47	136	41	1578	22	2938	14
	indeterminable	0	0	0	0	0	0	0	.	0	.	0	0	0	0
	total	6507	15	11251	11	10535	12	3554	20	5027	17	7052	12	43925	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3933	16	5006	17	11336	13	2183	31	5625	18	1858	29	29940	7
	fir	2857	20	2780	20	3039	17	81	67	98	89	286	53	9142	11
	pine	623	39	849	25	58	86	327	43	897	36	31	100	2785	17
	larch	8	100	151	43	0	0	774	37	950	28	1349	32	3233	18
	Arolla pine	0	.	0	.	0	.	19	100	222	48	0	.	240	45
	other conifers	17	84	292	57	2	100	0	.	0	.	0	.	310	54
	beech	6573	12	4779	14	3916	16	151	87	199	63	910	35	16529	8
	maple	1022	18	652	21	889	20	5	100	18	72	22	58	2607	11
	ash	737	29	1830	22	983	30	5	100	0	.	116	89	3671	15
	oak	264	35	1184	27	24	71	26	59	0	.	149	54	1647	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	637	44	637	44
	other broadleaves	475	33	564	28	255	24	23	47	15	53	735	39	2069	18
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	16508	9	18087	10	20502	10	3595	24	8023	14	6094	16	72809	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5808	14	10410	12	31366	7	4690	18	11120	13	5229	18	68624	5
	fir	7132	14	10283	11	12045	11	402	56	1001	33	1089	39	31953	6
	pine	290	38	1002	25	394	66	26	77	475	36	0	.	2187	19
	larch	78	63	383	36	220	50	3470	21	3344	19	1968	23	9464	11
	Arolla pine	0	.	0	.	0	.	249	53	324	45	0	.	574	34
	other conifers	12	71	1068	48	4	100	0	.	0	.	0	.	1084	47
	beech	6798	11	12819	9	7996	13	1	100	903	36	1078	33	29595	6
	maple	931	17	882	25	1403	17	11	100	116	64	0	.	3344	11
	ash	403	33	2882	18	932	26	6	100	45	95	0	.	4267	13
	oak	267	42	2020	25	62	63	0	.	64	100	34	60	2448	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	1404	33	1404	33
	other broadleaves	223	43	1071	25	799	39	26	47	126	73	205	41	2449	18
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	.	0	0
	total	21942	9	42822	6	55221	6	8882	15	17518	11	11007	12	157392	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5531	12	2135	23	16982	9	4174	15	7615	13	4093	30	40530	6
	fir	4649	14	2394	20	5951	14	516	46	442	34	355	40	14307	8
	pine	412	31	406	45	484	44	589	30	483	34	98	57	2472	16
	larch	50	100	93	69	77	60	3327	21	1811	18	2643	16	8002	11
	Arolla pine	0	.	0	.	12	100	610	26	698	28	65	99	1385	19
	other conifers	62	46	9	81	-3	749	0	.	9	73	82	98	159	56
	beech	3107	14	2615	19	4632	14	383	47	610	38	1640	22	12987	8
	maple	1044	20	507	25	1581	16	124	63	343	41	149	47	3747	10
	ash	564	32	904	34	804	31	98	84	196	53	205	33	2770	17
	oak	285	38	734	29	189	43	93	78	6	100	300	43	1607	18
	sweet chestnut	0	.	0	.	68	100	29	100	0	.	1341	25	1438	24
	other broadleaves	303	27	984	24	835	22	284	29	163	35	1188	25	3757	12
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	16008	9	10781	12	31611	7	10227	12	12377	10	12159	14	93163	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18267	6	26999	6	70550	4	13155	10	30938	7	12791	12	172699	2
	fir	16628	7	18810	8	23778	7	1081	31	1776	22	2068	24	64140	4
	pine	1745	18	2823	14	1142	31	1872	19	2739	17	211	44	10531	8
	larch	185	41	1148	19	602	29	9028	12	7298	11	7427	10	25688	6
	Arolla pine	0	.	0	.	12	100	1047	22	1704	18	73	89	2836	13
	other conifers	192	32	1785	31	96	53	7	66	42	34	88	92	2211	25
	beech	20088	5	24405	6	20058	7	611	37	1941	22	6638	11	73741	3
	maple	3719	9	3554	10	5978	8	294	35	652	27	482	25	14678	5
	ash	2095	15	6842	11	4052	14	350	37	321	37	913	30	14573	7
	oak	1062	19	4997	14	347	28	220	38	70	91	1103	20	7799	10
	sweet chestnut	0	.	0	.	99	73	71	62	5	100	5430	12	5605	12
	other broadleaves	1369	17	4115	11	2796	14	1068	20	1032	20	5167	11	15548	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	65350	3	95477	3	129511	3	28805	7	48517	5	42390	4	410050	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. 1354534. Birmensdorf, Swiss Federal Research Institute WSL

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