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: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern 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	1242	23	1702	19	689	28	265	41	643	37	36	74	4576	12
	fir	427	28	726	33	46	48	58	76	11	84	0	.	1269	22
	pine	157	40	0	.	1	100	1	100	0	.	0	.	160	39
	larch	17	100	0	.	0	.	121	53	87	56	24	60	249	33
	Arolla pine	0	.	0	.	0	.	12	79	10	100	0	.	21	63
	other conifers	16	100	0	.	0	.	0	.	0	.	0	.	16	100
	beech	962	19	136	44	143	45	0	.	9	68	83	94	1332	16
	maple	162	31	79	35	94	44	4	80	0	.	22	94	361	21
	ash	270	33	46	53	99	61	34	52	0	.	10	67	459	24
	oak	173	35	0	.	0	.	-1	411	0	.	14	100	186	34
	sweet chestnut	0	.	0	.	0	.	24	72	0	.	8	69	32	56
	other broadleaves	192	31	60	43	82	42	23	46	15	79	41	38	413	18
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3618	14	2750	17	1153	24	540	32	774	33	238	39	9073	9
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	593	24	721	27	704	33	239	40	524	30	91	46	2873	13
	fir	291	33	85	63	42	48	34	51	27	71	24	78	503	23
	pine	137	32	3	100	0	.	5	65	2	100	0	.	147	30
	larch	50	65	0	.	0	.	154	35	77	68	31	52	313	27
	Arolla pine	0	.	0	.	0	.	0	.	1	100	0	.	1	100
	other conifers	4	87	1	100	0	100	2	100	1	100	0	.	9	50
	beech	349	24	22	65	111	36	7	86	0	.	72	68	561	19
	maple	70	42	11	48	44	51	1	100	17	89	2	75	146	28
	ash	97	34	8	67	19	88	4	100	0	.	24	63	150	27
	oak	90	52	2	100	0	.	14	54	0	.	0	.	107	44
	sweet chestnut	0	.	0	.	0	.	0	.	16	100	31	62	47	53
	other broadleaves	142	48	25	36	15	60	34	48	24	51	29	32	268	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1823	15	879	24	936	28	494	26	689	25	303	25	5125	9
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4176	10	2039	17	1774	18	642	23	777	26	276	43	9685	7
	fir	1645	15	368	28	450	25	113	52	11	100	46	71	2631	11
	pine	617	21	60	71	72	58	568	25	207	37	7	100	1532	14
	larch	240	30	13	99	18	71	345	27	201	34	271	38	1088	16
	Arolla pine	0	.	0	.	0	.	48	50	43	58	0	.	91	38
	other conifers	74	40	0	.	1	166	0	.	0	100	0	.	75	39
	beech	3321	10	617	25	493	24	141	47	36	60	777	19	5386	8
	maple	693	15	159	25	233	25	31	34	19	58	69	41	1204	11
	ash	1159	13	256	31	265	26	13	50	75	57	157	28	1925	10
	oak	911	17	64	59	14	91	38	47	0	.	295	23	1323	13
	sweet chestnut	36	100	0	.	2	100	29	60	0	.	1373	14	1439	14
	other broadleaves	753	16	197	25	277	27	349	25	183	34	1571	12	3329	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	13625	6	3772	13	3600	13	2316	14	1552	17	4841	8	29707	4
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	9847	9	7562	11	4660	15	1908	21	2042	21	599	37	26618	6
	fir	4598	11	1734	19	1214	20	76	53	61	79	219	59	7901	8
	pine	1511	15	71	59	153	46	303	46	603	31	140	72	2781	13
	larch	279	42	36	65	82	71	739	23	317	33	461	34	1914	15
	Arolla pine	0	.	0	.	0	.	159	44	72	52	0	.	231	34
	other conifers	97	63	1	100	4	53	0	.	0	.	0	.	102	60
	beech	8558	8	1757	17	1934	19	119	46	139	54	753	21	13260	6
	maple	953	12	408	22	242	31	27	61	7	71	6	78	1643	10
	ash	1154	15	275	30	335	34	27	75	0	.	42	45	1834	12
	oak	932	18	53	51	8	63	3	100	0	.	96	49	1092	16
	sweet chestnut	5	100	0	.	0	.	0	.	0	.	460	25	465	25
	other broadleaves	750	23	366	29	127	38	48	53	12	69	328	30	1632	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	28685	5	12262	9	8759	11	3409	16	3253	17	3105	14	59473	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	24042	6	15396	8	9107	11	2556	16	8303	11	2061	19	61465	4
	fir	11425	8	4299	13	3018	15	129	54	229	38	286	34	19385	6
	pine	2899	13	197	77	239	42	265	42	572	26	124	58	4296	11
	larch	537	23	181	53	402	39	1368	18	1287	18	538	26	4312	10
	Arolla pine	0	.	0	.	0	.	99	54	253	41	0	.	352	33
	other conifers	250	50	27	92	1	100	0	.	0	.	0	.	277	46
	beech	13449	6	3479	15	2737	14	13	74	218	36	252	39	20149	5
	maple	1192	13	373	22	400	20	1	100	15	59	2	100	1982	10
	ash	1710	13	205	31	501	28	7	93	25	100	5	83	2452	11
	oak	1762	14	55	54	41	64	0	.	28	79	31	80	1916	13
	sweet chestnut	0	.	0	.	0	.	0	.	8	100	487	34	495	33
	other broadleaves	855	15	170	31	233	28	5	54	16	53	67	40	1347	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	58121	4	24381	7	16678	9	4444	13	10952	9	3852	13	118428	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12969	9	8234	12	7965	13	2069	22	6946	12	2419	18	40601	5
	fir	5288	11	3340	16	1646	22	67	62	591	32	404	40	11336	8
	pine	1293	20	0	.	23	82	159	86	353	48	52	65	1879	18
	larch	178	43	64	62	46	71	1560	21	1244	24	883	23	3976	13
	Arolla pine	0	.	0	.	0	.	41	73	294	38	5	100	341	34
	other conifers	94	70	1	100	1	100	0	.	0	.	0	.	95	69
	beech	8343	9	1737	21	1224	24	44	86	433	50	354	33	12134	8
	maple	536	19	55	47	215	30	0	.	19	96	9	100	833	15
	ash	684	20	42	50	68	43	0	.	0	.	0	.	795	18
	oak	1340	18	4	100	44	73	4	100	18	100	0	.	1410	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	606	34	606	34
	other broadleaves	579	20	66	58	61	39	87	75	18	72	29	46	841	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	31304	6	13542	10	11293	11	4031	16	9916	11	4761	13	74847	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5971	8	8552	8	3558	13	4206	11	6596	10	1766	16	30648	4
	fir	3434	11	4267	12	885	18	529	33	234	29	315	45	9663	7
	pine	649	19	268	36	155	43	675	20	981	19	30	97	2758	10
	larch	85	66	88	47	1	100	2196	12	1446	14	1165	15	4981	8
	Arolla pine	0	.	26	86	19	71	311	22	579	22	-1	100	933	15
	other conifers	62	45	0	.	2	100	6	100	24	50	0	.	95	33
	beech	4299	10	1649	16	1232	19	96	42	111	58	867	18	8253	7
	maple	707	17	341	18	335	22	37	50	16	56	25	44	1462	11
	ash	1170	16	269	24	250	28	49	53	18	64	23	62	1779	12
	oak	667	19	38	68	59	54	17	62	73	82	187	57	1040	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	799	28	799	28
	other broadleaves	987	16	197	20	226	26	144	50	60	32	401	21	2014	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	18031	6	15695	7	6722	11	8265	9	10137	8	5576	10	64425	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	58840	3	44208	4	28455	6	11884	7	25830	5	7248	9	176466	2
	fir	27107	4	14818	6	7301	9	1005	21	1164	19	1293	20	52687	3
	pine	7264	7	598	32	644	23	1977	15	2718	13	352	37	13553	6
	larch	1386	15	383	30	549	31	6482	8	4660	10	3373	10	16833	5
	Arolla pine	0	.	26	86	19	71	670	17	1251	16	4	135	1969	12
	other conifers	597	27	29	85	10	43	9	77	26	47	0	.	670	24
	beech	39281	3	9398	8	7874	8	420	24	946	27	3158	9	61077	3
	maple	4312	6	1426	10	1563	10	101	27	93	31	136	28	7630	4
	ash	6243	6	1100	13	1537	14	134	29	118	43	261	20	9394	5
	oak	5876	7	216	28	166	33	74	31	118	56	623	22	7073	6
	sweet chestnut	41	89	0	.	2	100	52	46	24	74	3763	10	3883	10
	other broadleaves	4259	7	1080	13	1021	13	691	19	327	21	2466	9	9843	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	155207	1	73281	3	49140	4	23499	5	37275	4	22677	4	361078	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. 1354500. Birmensdorf, Swiss Federal Research Institute WSL

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