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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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
number of stems
development stage · main tree species
regional demarcation: economic region
unit: n/ha
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	49	36	50	47	49	60	17	54	99	37	94	22	152	23	55	43	105	22	56	86	118	73	59	44	71	29	0	.	72	11
	fir	17	51	3	94	38	89	36	57	23	65	59	55	118	40	0	.	14	76	0	.	7	80	13	73	3	95	0	.	28	25
	pine	20	97	22	94	0	.	36	74	5	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	50
	larch	0	.	13	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	95	28	55	16	62	10	60
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	95	0	.	1	99
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	34	44	53	23	23	71	60	50	61	41	33	92	48	51	29	83	60	79	0	.	125	85	0	.	11	58	0	.	36	20
	maple	9	66	51	40	10	90	17	94	9	96	7	97	24	64	22	68	11	62	8	86	0	.	4	96	0	.	0	.	13	25
	ash	31	54	49	88	29	90	25	94	22	81	32	97	9	95	37	75	10	93	0	.	281	85	23	67	0	.	0	.	27	32
	oak	32	74	15	50	0	.	8	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	95	0	.	10	92	7	46
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	73	0	.	0	.	1	78
	other broadleaves	28	44	121	35	0	.	10	54	3	96	12	70	25	55	56	73	6	93	0	.	77	85	30	53	14	95	37	52	27	21
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	219	20	377	18	149	53	208	33	223	21	237	25	376	20	199	51	207	21	64	72	608	61	213	30	139	13	63	24	228	9
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	28	49	7	96	39	55	71	55	34	55	187	35	74	30	73	35	126	28	115	57	52	70	15	36	70	35	25	45	56	13
	fir	26	73	0	.	7	93	43	45	3	75	27	94	16	98	11	52	8	67	0	.	6	94	1	98	7	84	5	98	10	26
	pine	0	.	14	58	0	.	1	97	10	64	0	.	0	.	0	.	0	.	0	.	0	.	9	76	0	.	0	.	3	39
	larch	6	97	4	96	0	.	3	97	1	98	0	.	0	.	0	.	0	.	0	.	0	.	17	37	15	47	17	51	7	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	4	96	0	.	3	97	0	.	0	.	0	.	0	.	3	97	0	.	0	.	11	98	0	.	0	.	2	66
	beech	32	48	5	96	0	.	25	54	12	41	13	94	0	.	24	47	7	97	7	82	0	.	0	.	0	.	8	49	9	22
	maple	0	.	13	96	0	.	20	57	2	98	0	.	20	61	24	73	3	97	0	.	13	93	2	98	10	68	2	98	8	28
	ash	11	96	3	96	0	.	16	67	2	75	7	94	7	97	2	96	0	.	0	.	0	.	9	98	0	.	0	.	4	37
	oak	0	.	2	96	10	93	0	.	4	98	0	.	4	95	0	.	0	.	0	.	0	.	7	54	0	.	0	.	2	40
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	65	1	67
	other broadleaves	13	75	2	96	0	.	7	66	5	55	0	.	33	39	1	97	17	51	0	.	0	.	22	49	37	58	30	37	16	23
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	117	28	52	42	56	49	189	27	73	38	234	23	154	16	136	25	163	24	122	51	71	59	92	22	139	21	97	16	118	8
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	232	24	192	34	181	35	367	21	307	16	533	24	488	24	355	29	235	26	248	41	383	38	197	24	284	22	23	53	227	7
	fir	89	25	33	52	55	70	177	35	19	34	52	57	31	42	73	31	12	77	129	40	6	95	11	90	7	99	4	95	41	15
	pine	10	68	45	60	38	68	20	80	9	39	0	.	46	98	0	.	0	.	19	97	0	.	187	27	73	39	0	.	35	20
	larch	1	99	4	68	1	98	12	81	14	47	0	.	0	.	0	.	15	79	0	.	47	63	84	27	36	44	27	34	21	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	51	15	67	0	.	2	43
	other conifers	1	99	32	75	0	.	11	52	6	61	0	.	0	.	2	98	0	.	9	97	13	96	0	.	1	98	0	.	4	37
	beech	241	16	302	18	102	33	81	32	104	24	108	50	123	48	121	39	70	77	126	36	117	59	48	52	14	99	110	24	120	9
	maple	34	25	26	33	15	64	8	98	56	25	23	63	30	37	70	29	77	44	46	40	71	51	6	54	3	99	4	45	26	12
	ash	43	39	59	37	72	34	27	48	68	22	84	43	67	66	83	31	34	72	42	49	15	95	6	72	50	62	19	32	42	12
	oak	44	48	14	59	184	36	36	45	5	42	7	66	4	69	2	97	21	83	3	96	0	.	14	64	0	.	37	36	29	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	81	0	.	205	16	47	17
	other broadleaves	33	47	16	49	124	37	71	46	52	30	46	61	54	42	123	43	31	39	103	52	30	34	77	27	120	30	211	12	100	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	727	7	725	8	773	9	812	9	641	6	853	12	843	11	829	13	494	13	725	14	683	26	650	9	604	10	639	6	694	3
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	132	16	155	32	242	24	226	26	197	17	440	20	326	13	324	24	259	27	448	22	398	29	293	22	390	22	93	39	240	6
	fir	147	17	45	42	47	57	86	35	73	31	112	43	63	27	102	25	51	38	60	50	36	60	5	85	0	.	5	71	71	10
	pine	20	40	56	31	6	87	8	47	29	41	0	.	13	95	2	98	2	67	57	51	0	.	17	52	133	38	0	.	21	19
	larch	0	.	3	74	0	.	0	.	5	78	0	.	0	.	0	.	6	84	9	94	0	.	86	26	50	35	68	35	15	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	58	17	80	0	.	1	56
	other conifers	2	73	0	.	0	.	8	98	4	50	0	.	0	.	2	98	0	.	8	94	4	96	0	.	0	.	0	.	2	38
	beech	220	12	247	18	178	23	235	18	122	16	82	32	119	28	226	26	90	36	140	66	173	34	23	73	70	54	161	23	158	7
	maple	38	16	18	41	15	66	9	57	32	26	64	39	26	27	36	44	76	41	5	96	62	70	12	86	2	98	3	99	28	12
	ash	18	46	11	61	27	61	15	49	35	32	0	.	36	39	70	43	55	39	5	96	9	77	12	60	0	.	7	58	23	16
	oak	17	48	32	38	21	40	9	58	22	36	12	98	3	99	0	.	8	70	0	.	0	.	0	.	0	.	15	50	12	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	121	29	11	32
	other broadleaves	32	28	22	31	14	70	6	73	37	41	21	47	13	47	22	45	84	33	0	.	12	96	21	47	10	98	91	28	31	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	626	5	589	11	551	10	602	8	555	6	731	11	599	8	783	10	630	10	731	11	694	16	475	11	672	11	564	7	614	2
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	109	13	58	38	232	18	169	11	138	10	407	15	215	10	278	11	315	14	287	23	121	29	246	16	323	10	172	18	206	4
	fir	108	15	28	56	64	33	113	16	49	17	59	60	126	15	81	22	43	52	103	46	71	33	10	72	13	39	38	50	71	7
	pine	13	36	13	48	49	48	8	42	27	22	0	.	6	88	3	87	4	98	0	.	0	.	21	67	23	31	7	99	14	14
	larch	0	70	1	98	9	71	1	99	5	40	0	.	0	.	1	99	5	62	0	.	7	58	81	33	44	25	17	40	11	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	98	11	40	0	.	2	38
	other conifers	1	100	0	.	6	98	2	74	2	100	0	.	0	99	1	99	0	.	0	.	0	.	0	.	0	.	0	.	1	45
	beech	150	10	177	16	73	24	115	12	122	12	42	53	162	15	81	25	57	35	64	42	132	23	6	99	23	38	44	42	99	5
	maple	22	26	56	32	2	99	4	49	8	43	6	53	16	32	24	30	24	48	27	45	32	52	2	98	2	61	3	99	14	12
	ash	12	35	9	45	24	52	9	38	16	22	2	98	9	33	17	44	0	.	22	67	15	67	4	68	1	100	3	98	10	13
	oak	5	40	12	49	34	47	12	36	14	25	3	97	0	.	0	.	0	.	1	98	0	.	0	.	2	100	4	69	7	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	43	58	2	58
	other broadleaves	21	27	27	49	14	47	10	35	13	31	14	58	14	65	12	37	13	55	17	54	40	47	3	98	7	46	25	40	15	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	442	5	381	11	506	7	443	4	394	5	533	15	548	5	498	6	460	9	521	16	417	15	379	11	451	7	357	10	450	2
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	97	14	32	51	134	35	98	18	110	16	302	35	104	17	192	16	240	21	223	12	218	24	118	22	161	11	126	19	135	5
	fir	81	16	21	54	63	66	56	25	44	25	46	65	126	17	69	20	64	43	25	78	9	97	3	98	8	59	10	47	47	9
	pine	4	53	0	.	7	59	8	56	13	39	0	.	0	.	1	69	0	.	0	.	0	.	3	79	14	48	7	64	6	23
	larch	0	99	0	.	3	97	1	99	1	69	0	.	1	98	2	70	1	96	0	.	0	.	72	25	33	28	27	28	13	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	98	18	49	0	.	3	46
	other conifers	1	99	0	.	0	.	4	69	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	1	51
	beech	93	16	148	12	109	27	117	13	82	16	3	91	115	25	89	23	50	48	72	64	43	56	20	98	22	37	21	62	71	8
	maple	27	43	8	49	3	96	7	51	14	35	0	.	8	72	16	46	11	97	8	93	61	53	0	.	2	70	0	.	11	21
	ash	3	65	14	47	11	66	14	42	14	61	0	.	0	.	9	53	3	97	0	.	6	77	0	.	0	.	0	.	5	24
	oak	0	.	7	57	29	65	4	42	19	35	0	.	0	.	3	72	0	.	3	93	0	.	2	98	1	99	0	.	4	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	56	4	58
	other broadleaves	14	57	18	68	33	57	23	48	32	36	6	96	3	80	10	51	14	85	11	64	1	97	32	68	4	84	14	62	15	19
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	321	8	247	10	394	18	332	8	329	8	356	33	358	7	393	9	384	15	342	19	338	18	255	13	263	7	244	12	315	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	141	12	38	48	32	29	75	33	65	24	263	22	203	12	222	22	185	16	204	20	183	30	210	12	258	9	86	20	175	5
	fir	88	15	4	95	47	63	161	25	39	39	109	33	125	15	47	30	30	33	67	64	39	78	22	37	5	41	11	51	49	9
	pine	11	34	5	94	5	61	5	98	7	50	0	.	4	71	0	.	18	71	92	63	0	.	64	26	55	26	5	88	25	16
	larch	0	.	0	.	0	.	0	.	4	99	0	.	1	99	0	.	5	48	0	.	0	.	75	13	36	17	51	18	25	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	20	29	31	26	0	.	8	20
	other conifers	1	70	5	95	21	94	0	.	15	89	0	.	0	.	2	98	0	.	0	.	0	.	0	100	1	100	0	.	2	48
	beech	107	13	217	35	280	30	104	37	113	20	53	39	52	19	126	29	86	30	18	39	50	38	14	44	6	63	95	23	68	8
	maple	20	25	39	43	0	.	28	44	57	29	28	33	11	27	22	40	19	41	0	.	45	36	9	59	3	84	3	66	15	12
	ash	18	38	29	65	113	41	26	38	71	26	31	49	13	52	33	48	11	67	0	.	33	45	2	83	0	.	4	48	15	15
	oak	14	62	5	95	0	.	14	45	19	33	3	97	0	.	5	99	0	.	11	93	0	.	3	47	0	.	11	53	6	27
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	41	5	42
	other broadleaves	19	34	70	41	48	75	20	32	111	24	40	40	18	36	45	50	19	43	54	61	57	43	15	36	4	49	45	21	28	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	417	5	412	20	546	14	434	8	501	10	528	11	426	7	502	12	373	10	446	16	406	13	434	6	398	6	346	8	420	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	132	8	96	20	179	13	168	10	153	8	345	10	230	7	244	9	224	9	261	14	220	15	187	9	245	6	73	11	182	2
	fir	99	8	25	26	52	25	104	12	40	13	76	21	101	8	66	11	33	21	80	26	32	27	13	29	7	24	10	28	52	4
	pine	12	20	27	27	24	36	10	29	17	16	0	.	9	55	1	57	6	60	25	43	0	.	64	19	41	17	3	51	19	9
	larch	0	55	3	41	3	61	2	59	5	29	0	.	0	72	1	58	5	36	1	99	8	56	72	10	36	11	36	14	16	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	11	23	19	19	0	.	3	15
	other conifers	1	40	8	69	3	70	4	35	4	46	0	.	0	100	1	44	0	100	3	74	2	76	1	85	1	78	0	.	2	20
	beech	151	6	191	10	115	13	116	8	100	8	56	21	104	11	111	13	63	18	78	26	94	19	19	31	17	23	86	14	91	3
	maple	26	11	30	18	8	40	9	23	25	15	23	25	17	15	30	16	31	22	21	30	46	25	6	37	3	36	3	32	17	6
	ash	18	19	24	26	38	22	16	19	32	13	23	33	18	28	34	20	15	32	17	40	24	47	5	31	5	61	8	25	18	7
	oak	15	27	14	24	58	29	13	23	13	15	4	56	1	54	2	59	3	64	3	58	0	.	5	38	1	75	17	27	10	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	60	0	100	100	13	12	13
	other broadleaves	24	15	30	20	41	28	21	25	35	15	24	25	18	21	34	26	26	20	39	40	27	26	29	18	20	23	98	10	35	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	478	3	448	6	521	6	463	4	426	4	551	7	498	4	525	5	407	6	528	9	452	10	416	5	394	4	433	4	456	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. 1284033. Birmensdorf, Swiss Federal Research Institute WSL

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