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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NFI2
growing stock (stemwood)
development stage · main tree species
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		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	67	100	32	100	0	0	3	100	62	100	10	100	175	56
	fir	0	.	0	0	0	.	0	0	0	.	0	0	0	0
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	10	100	29	100	72	73	110	55
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	93	72	0	.	0	.	0	.	0	.	31	79	124	58
	maple	13	100	0	.	14	100	0	.	0	.	0	.	27	71
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	26	100	26	100
	other broadleaves	0	0	0	.	0	.	0	.	0	.	0	.	0	0
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	173	55	32	100	14	100	13	100	90	75	140	52	463	31
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	362	29	755	32	244	39	89	64	583	42	107	69	2140	18
	fir	170	49	362	47	109	50	1	100	122	75	6	100	769	28
	pine	196	55	0	.	0	.	0	.	93	75	0	.	289	45
	larch	28	74	3	74	31	72	79	63	94	52	41	75	276	30
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	3	75	0	.	0	.	0	.	4	100	0	.	7	65
	beech	313	43	106	56	181	49	1	100	74	76	82	53	756	25
	maple	30	50	23	95	24	54	8	63	23	100	27	96	135	34
	ash	19	86	3	100	52	84	0	.	0	.	0	.	74	63
	oak	4	78	0	.	0	.	3	100	0	.	0	.	7	62
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	30	100	30	100
	other broadleaves	9	56	18	51	27	45	25	55	17	62	80	53	175	28
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1133	27	1269	28	666	28	206	45	1011	31	373	32	4658	13
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7579	12	3618	17	1550	25	823	31	827	29	557	39	14954	8
	fir	2120	19	410	35	710	28	38	91	68	72	287	64	3632	14
	pine	768	23	113	94	9	100	970	27	318	39	2	100	2179	16
	larch	252	34	31	77	45	72	426	25	249	38	392	30	1395	15
	Arolla pine	0	.	0	0	0	.	189	46	23	59	0	.	211	42
	other conifers	148	46	6	72	-13	154	18	90	6	69	12	100	177	41
	beech	2947	14	747	32	641	30	180	46	51	100	1638	22	6204	10
	maple	578	20	301	24	519	30	7	265	18	71	92	38	1515	14
	ash	1170	20	284	51	654	32	38	52	61	62	160	41	2368	15
	oak	1070	26	44	48	8	71	22	52	0	.	497	31	1641	19
	sweet chestnut	0	.	0	.	0	.	100	57	0	.	1656	19	1756	18
	other broadleaves	716	20	143	28	406	31	346	30	361	32	1679	14	3650	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	17347	8	5697	14	4529	16	3157	15	1982	19	6971	10	39682	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	9468	12	7868	14	5335	21	1778	26	5218	17	592	47	30259	7
	fir	5131	14	2103	24	1229	26	135	51	112	67	175	65	8885	11
	pine	1305	21	120	62	93	74	309	40	695	33	54	100	2576	16
	larch	192	49	38	100	104	70	758	26	1212	22	773	40	3076	15
	Arolla pine	0	.	6	100	0	.	35	81	382	40	0	.	424	37
	other conifers	249	56	0	.	5	74	0	.	0	0	0	.	254	55
	beech	10564	9	2232	22	2083	25	300	53	337	51	588	37	16105	8
	maple	1674	12	409	33	249	33	84	52	12	71	0	.	2428	11
	ash	2475	18	373	41	494	35	60	63	0	.	11	100	3413	15
	oak	939	23	120	72	40	100	41	51	0	.	37	55	1177	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	692	40	692	40
	other broadleaves	1023	22	318	37	158	39	106	51	35	59	316	52	1957	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	33021	7	13587	12	9790	15	3607	18	8004	14	3239	19	71248	5
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	24143	8	14018	11	9010	15	3264	20	7851	14	2419	25	60704	5
	fir	11571	10	4064	18	2513	24	272	61	90	69	440	48	18949	8
	pine	2736	18	374	73	188	61	278	58	631	37	0	.	4207	15
	larch	459	44	245	52	46	100	1607	26	1508	21	473	31	4337	14
	Arolla pine	0	.	0	.	0	.	153	51	466	38	0	.	619	31
	other conifers	170	75	59	100	3	71	0	.	0	.	0	.	232	61
	beech	15255	8	3572	18	2780	24	10	100	101	43	269	42	21987	7
	maple	1330	17	708	25	759	25	9	100	30	74	0	.	2836	12
	ash	2773	20	452	33	431	41	4	100	51	100	40	79	3751	16
	oak	2198	16	0	0	61	82	0	.	42	100	82	100	2383	16
	sweet chestnut	1	100	0	.	0	.	0	.	0	.	253	75	254	75
	other broadleaves	1178	20	481	36	302	31	29	64	55	42	119	62	2164	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	61815	5	23972	10	16092	12	5625	16	10825	12	4094	19	122423	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18519	10	9857	15	8849	15	2349	25	6787	16	3058	24	49421	6
	fir	7136	13	4436	17	1659	26	174	81	382	45	125	84	13913	9
	pine	1160	22	112	100	58	64	40	71	629	43	51	75	2049	19
	larch	317	42	131	55	226	56	1786	25	1461	29	814	33	4736	15
	Arolla pine	0	.	0	.	0	.	28	71	264	42	0	.	292	38
	other conifers	309	62	0	.	0	.	0	.	0	.	0	.	309	62
	beech	11834	10	2879	26	1939	25	81	80	598	46	302	54	17634	9
	maple	599	25	245	46	303	32	7	100	21	172	0	.	1174	18
	ash	1044	25	74	63	149	67	20	78	9	100	0	.	1296	22
	oak	1454	23	39	100	41	73	2	100	0	.	15	71	1551	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	742	44	742	44
	other broadleaves	813	30	187	69	81	50	14	100	37	71	114	36	1247	23
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	43187	7	17961	12	13305	14	4502	19	10189	13	5221	18	94364	5
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3398	15	7892	13	3195	19	3055	18	3327	18	2127	24	22993	7
	fir	2564	18	4502	17	971	29	247	52	249	70	316	67	8848	11
	pine	313	34	0	.	44	100	268	45	322	42	90	72	1036	22
	larch	62	83	79	92	0	.	2065	26	465	26	1123	24	3794	16
	Arolla pine	0	.	8	100	0	.	314	38	182	42	1	100	504	28
	other conifers	27	58	1	100	3	100	1	100	0	.	0	.	33	50
	beech	2216	17	1159	21	795	25	62	72	72	60	905	30	5209	11
	maple	455	23	359	27	353	35	24	58	19	85	14	95	1225	16
	ash	866	28	193	39	269	46	72	69	91	100	53	52	1546	20
	oak	294	51	3	100	22	100	34	88	62	100	100	55	514	34
	sweet chestnut	0	.	0	.	0	.	0	.	17	100	1180	30	1197	30
	other broadleaves	394	29	158	27	138	42	67	52	27	47	461	32	1247	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	10588	12	14355	11	5789	17	6211	15	4833	16	6371	14	48147	5
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	63536	4	44040	6	28183	8	11361	10	24656	7	8870	12	180646	2
	fir	28692	6	15877	8	7190	12	867	30	1023	27	1348	28	54997	4
	pine	6478	10	718	45	390	37	1866	19	2688	17	196	47	12336	8
	larch	1310	21	526	32	452	35	6731	12	5018	12	3687	14	17724	6
	Arolla pine	0	.	14	71	0	.	718	23	1316	20	1	100	2050	15
	other conifers	907	31	66	89	-1	1590	19	85	10	58	12	100	1013	28
	beech	43221	4	10695	10	8419	12	633	31	1233	27	3816	13	68018	3
	maple	4680	8	2044	13	2221	13	139	37	124	43	133	34	9341	6
	ash	8346	10	1380	19	2048	17	195	34	212	52	265	29	12447	7
	oak	5959	10	205	47	172	43	103	37	104	72	731	25	7274	9
	sweet chestnut	1	100	0	.	0	.	100	57	17	100	4580	13	4698	13
	other broadleaves	4134	11	1306	19	1112	16	587	21	531	23	2770	11	10440	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	167264	2	76871	4	50186	6	23321	7	36933	6	26410	5	380985	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. 1230947. Birmensdorf, Swiss Federal Research Institute WSL

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