Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
number of stems
development stage · main tree species
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	80	62	424	49	245	93	25	100	239	42	139	72	1152	30
	fir	0	.	71	90	22	100	0	.	0	.	0	.	93	73
	pine	0	.	0	0	0	.	0	0	0	.	0	.	0	0
	larch	8	100	0	.	0	.	85	92	35	53	15	100	143	57
	Arolla pine	0	.	0	.	0	.	42	100	0	0	0	.	42	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	92	52	52	72	17	100	0	.	7	100	0	.	169	38
	maple	27	100	37	80	17	100	0	.	0	.	0	.	81	53
	ash	0	.	0	.	17	100	0	.	0	.	0	.	17	100
	oak	8	100	0	.	0	.	0	.	0	.	0	.	8	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	39	71	6	100	39	71	0	.	0	.	0	.	84	47
	indeterminable	0	.	0	0	0	.	0	.	0	.	0	.	0	0
	total	253	37	590	43	358	80	152	81	282	40	154	71	1789	25
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	914	23	1443	22	426	39	256	37	597	37	87	85	3724	13
	fir	561	28	278	40	205	46	55	100	35	100	19	100	1152	20
	pine	88	72	0	.	0	.	0	0	113	68	0	.	201	49
	larch	55	54	43	72	0	0	203	48	22	75	19	100	342	32
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	20	100	15	100	0	.	69	100	0	.	0	.	104	71
	beech	1335	17	145	53	171	46	35	100	0	.	11	100	1697	15
	maple	401	27	228	47	228	58	31	64	17	100	8	100	913	22
	ash	306	28	160	45	44	83	55	100	0	.	0	.	564	23
	oak	31	63	0	.	0	.	0	0	0	.	0	.	31	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	11	100	11	100
	other broadleaves	551	23	296	42	200	41	124	65	119	72	40	60	1330	17
	indeterminable	0	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	4261	12	2608	19	1274	26	828	37	902	31	196	61	10070	9
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15469	12	4915	24	3670	24	1745	34	4240	26	1000	47	31039	9
	fir	3825	21	1540	34	848	45	120	76	181	87	85	56	6600	16
	pine	818	41	139	100	17	100	1969	42	1817	35	25	100	4784	23
	larch	662	55	490	45	0	.	1065	34	347	41	962	40	3527	20
	Arolla pine	0	.	0	.	0	.	178	91	130	88	0	.	308	64
	other conifers	488	28	17	100	0	.	52	74	447	47	16	100	1020	25
	beech	5949	14	785	44	905	43	517	62	35	100	2715	34	10908	13
	maple	3693	18	1188	29	512	38	427	42	265	61	579	39	6664	13
	ash	2588	19	512	42	785	36	422	45	70	71	957	31	5333	13
	oak	1379	41	123	100	0	.	422	56	0	.	1108	43	3033	26
	sweet chestnut	0	0	0	.	0	.	25	71	0	.	5859	23	5884	23
	other broadleaves	2737	18	834	30	1174	36	2606	24	2946	34	7469	19	17767	11
	indeterminable	0	0	0	0	0	.	0	0	0	0	0	0	0	0
	total	37607	8	10544	16	7910	19	9550	17	10477	17	20777	12	96866	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7725	15	5962	21	3837	24	2288	45	3812	25	651	42	24275	10
	fir	3001	19	1107	42	1212	32	0	.	129	71	302	70	5751	15
	pine	1025	30	24	100	114	94	1334	37	1110	42	0	.	3608	21
	larch	155	48	0	.	120	76	703	37	980	34	897	37	2855	19
	Arolla pine	0	.	0	.	0	.	61	59	492	48	0	.	552	43
	other conifers	497	47	0	.	60	58	17	100	17	100	0	.	591	40
	beech	7766	12	1858	35	2810	25	404	61	129	59	4065	26	17033	10
	maple	1756	17	1518	33	1135	32	45	100	71	100	194	52	4719	15
	ash	2096	19	674	45	868	33	54	75	184	74	650	61	4525	16
	oak	1786	27	164	43	18	100	155	49	0	.	416	32	2539	20
	sweet chestnut	7	100	0	.	0	.	55	100	0	.	1709	35	1771	34
	other broadleaves	2012	19	233	38	443	33	492	43	451	39	2622	25	6252	13
	indeterminable	0	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	27824	9	11539	16	10618	17	5609	24	7374	19	11507	15	74471	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7385	11	4955	18	5667	18	2544	24	7339	15	2300	26	30190	7
	fir	5334	14	2188	22	1449	22	402	45	85	61	144	51	9602	10
	pine	1063	18	91	66	197	71	331	48	455	39	14	100	2151	16
	larch	190	40	6	100	35	53	611	38	698	28	906	38	2447	19
	Arolla pine	0	.	0	.	0	.	0	.	372	36	0	.	372	36
	other conifers	262	52	0	.	54	57	0	.	0	.	0	.	316	44
	beech	12323	9	2844	20	2911	20	340	81	319	51	1154	38	19891	7
	maple	1612	13	751	25	763	26	55	74	33	71	18	100	3232	11
	ash	1849	19	565	35	863	28	0	0	18	100	70	71	3365	14
	oak	885	19	38	100	0	0	18	100	35	100	76	54	1053	18
	sweet chestnut	0	.	7	100	0	.	0	.	0	.	727	45	734	45
	other broadleaves	1339	18	712	27	330	34	233	59	35	100	476	41	3126	13
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	32241	7	12159	14	12268	13	4535	22	9389	13	5885	17	76477	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6851	9	7141	11	5475	14	2878	21	5049	15	1949	22	29342	5
	fir	7040	9	4116	13	1490	20	131	71	231	44	245	81	13253	7
	pine	420	21	117	100	15	72	187	87	141	44	0	.	879	26
	larch	55	39	72	62	48	53	1405	24	1102	24	475	34	3157	14
	Arolla pine	0	.	0	.	0	.	192	49	169	53	0	.	361	36
	other conifers	210	39	7	100	36	71	0	.	0	.	0	.	253	34
	beech	9691	7	3689	16	2701	17	37	100	604	36	734	46	17456	6
	maple	1563	15	559	22	606	26	261	61	83	64	0	.	3072	11
	ash	1771	17	393	38	344	32	49	73	12	100	0	.	2570	14
	oak	789	23	0	.	23	58	0	0	0	.	8	100	819	22
	sweet chestnut	17	100	0	.	0	.	0	.	0	.	235	48	252	45
	other broadleaves	1448	24	494	33	352	35	55	74	39	71	448	38	2836	16
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	29852	6	16588	9	11090	11	5196	16	7429	13	4095	17	74250	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7311	12	13409	11	4430	19	7394	15	9308	14	4119	18	45971	6
	fir	7389	12	4904	15	1338	25	707	39	217	43	652	36	15207	8
	pine	439	28	617	78	62	77	1366	30	504	31	195	78	3184	21
	larch	59	56	46	100	153	100	3311	19	961	23	2200	23	6729	13
	Arolla pine	0	.	10	100	0	.	1092	30	1244	31	49	100	2394	21
	other conifers	361	34	35	70	133	74	18	100	121	77	0	.	667	28
	beech	8072	11	4683	17	1990	28	861	47	631	50	3226	21	19463	8
	maple	2279	17	1497	19	759	34	215	59	231	40	110	62	5090	11
	ash	1436	18	695	29	617	39	439	74	524	64	310	41	4021	16
	oak	677	27	56	54	98	54	379	59	55	100	476	41	1741	21
	sweet chestnut	0	.	0	.	0	.	8	100	28	100	3431	22	3467	22
	other broadleaves	2121	18	1641	26	1042	34	815	27	588	34	2693	18	8901	10
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	0	0	0
	total	30146	7	27593	8	10620	15	16604	11	14412	12	17462	10	116835	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	45734	5	38248	6	23751	8	17131	10	30584	7	10246	11	165693	3
	fir	27150	6	14203	8	6564	12	1416	25	878	27	1447	26	51657	4
	pine	3852	13	988	52	405	45	5188	20	4139	20	234	66	14807	10
	larch	1183	32	658	35	356	51	7382	11	4146	13	5475	14	19199	7
	Arolla pine	0	.	10	100	0	.	1566	24	2406	20	49	100	4030	15
	other conifers	1838	18	74	46	282	39	156	53	585	39	16	100	2952	15
	beech	45228	4	14057	9	11505	10	2194	29	1726	24	11906	13	86616	3
	maple	11331	7	5777	12	4020	14	1035	27	699	29	909	28	23771	5
	ash	10045	8	2999	16	3538	15	1019	38	807	45	1987	26	20396	6
	oak	5554	14	382	39	139	41	975	34	91	72	2085	25	9224	11
	sweet chestnut	23	76	7	100	0	.	89	66	28	100	11972	13	12120	13
	other broadleaves	10246	8	4217	13	3580	17	4325	16	4177	25	13749	11	40295	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	162183	2	81621	4	54139	6	42475	7	50265	6	60076	4	450759	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. 1356662. Birmensdorf, Swiss Federal Research Institute WSL

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