Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI1
number of stems
development stage · main tree species
regional demarcation: economic region
unit: n/ha
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
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	37	30	39	37	41	41	12	57	83	30	85	21	187	21	78	25	112	21	57	49	51	49	42	35	134	42	6	68	71	11
	fir	26	29	10	85	19	85	27	58	18	58	42	56	102	32	6	51	8	63	0	.	6	72	6	77	2	68	0	.	21	20
	pine	10	82	15	77	0	.	25	76	6	57	0	.	0	.	4	98	0	.	0	.	0	.	2	98	0	.	0	.	4	38
	larch	0	.	7	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	77	35	60	7	56	7	44
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	86	6	98	0	.	1	66
	other conifers	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100
	beech	57	30	119	23	25	49	51	42	75	29	23	93	25	55	12	74	44	56	23	63	48	66	0	.	5	63	30	91	36	15
	maple	20	45	37	35	13	49	12	96	27	48	13	47	21	55	23	49	22	68	4	94	13	96	7	78	0	.	10	70	16	17
	ash	22	40	28	87	19	72	33	56	18	72	22	98	11	69	27	45	7	69	0	.	76	92	20	42	0	.	16	60	20	23
	oak	18	60	22	47	19	73	7	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	88	0	.	3	98	5	32
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	77	0	.	6	60	1	51
	other broadleaves	23	36	69	40	9	64	21	34	12	81	14	48	14	58	37	46	38	42	6	93	54	50	26	35	23	57	37	28	27	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	213	13	348	13	146	32	187	26	239	17	198	23	360	17	186	22	231	13	90	33	249	47	148	21	206	34	116	25	210	6
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	25	35	6	80	33	47	48	45	46	33	174	32	58	29	77	29	95	28	126	47	42	57	25	29	52	27	25	31	48	10
	fir	30	38	0	.	15	75	25	44	4	52	25	73	10	98	9	46	5	69	5	95	4	96	7	56	3	85	4	72	9	18
	pine	3	99	9	59	1	97	1	98	5	58	0	.	0	.	0	.	8	98	0	.	0	.	6	61	4	99	0	.	3	31
	larch	3	99	3	98	0	.	9	80	1	73	0	.	0	.	0	.	0	.	0	.	0	.	15	28	14	35	12	37	6	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	99	0	.	0	100
	other conifers	0	.	3	98	0	.	2	98	0	.	0	.	0	.	0	.	2	98	6	94	0	.	5	99	1	99	0	.	1	55
	beech	17	46	14	54	8	80	24	36	15	40	11	80	0	.	22	33	4	98	2	94	17	87	3	73	0	.	11	41	10	16
	maple	1	99	15	68	0	.	11	60	4	48	5	96	11	64	17	67	8	58	0	.	14	57	1	99	5	69	2	69	6	22
	ash	5	88	23	88	4	97	9	70	4	39	5	96	6	74	4	70	0	.	0	.	4	96	4	99	0	.	10	73	5	30
	oak	0	.	1	97	5	97	1	98	3	68	0	.	2	97	0	.	0	.	0	.	0	.	12	54	0	.	0	.	2	39
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	10	47	1	47
	other broadleaves	25	43	15	84	0	.	3	69	16	39	0	.	34	43	3	74	13	52	12	63	15	96	28	33	30	46	30	24	20	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	109	20	87	45	66	46	131	23	98	20	219	23	122	18	132	20	135	22	152	36	95	41	107	15	113	16	104	13	113	6
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	190	20	157	31	176	27	357	17	322	13	497	19	439	18	324	21	223	21	261	29	450	24	131	21	256	19	29	36	205	6
	fir	84	20	22	48	45	48	161	29	26	31	121	43	43	33	90	34	16	58	88	40	37	73	28	51	4	99	2	95	42	12
	pine	13	59	42	47	20	64	15	67	13	33	0	.	46	69	0	.	2	98	50	59	0	.	155	21	106	32	0	100	34	15
	larch	3	85	8	58	2	79	10	64	20	53	0	.	0	.	1	99	9	80	0	.	24	67	81	21	52	30	25	28	23	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	49	24	65	0	.	4	40
	other conifers	1	100	20	76	9	99	11	47	3	62	0	.	0	.	3	69	0	.	7	98	8	98	0	.	1	99	0	.	3	33
	beech	240	13	319	14	116	25	106	27	98	20	109	38	128	34	138	31	75	53	112	33	156	49	47	37	15	62	105	17	120	7
	maple	32	20	30	29	48	53	24	47	48	25	23	47	21	35	56	26	63	35	48	32	75	44	11	30	8	55	7	35	27	10
	ash	45	29	57	29	68	25	31	47	69	19	56	44	58	51	77	25	35	47	48	40	27	53	6	47	34	55	27	21	41	9
	oak	61	33	36	67	123	32	32	40	21	44	5	68	3	58	2	76	53	52	2	98	0	.	13	46	0	.	38	23	32	14
	sweet chestnut	0	.	0	.	11	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	97	5	67	0	.	178	12	45	13
	other broadleaves	40	32	24	33	98	27	51	40	70	22	36	53	89	35	133	35	56	38	85	43	85	48	131	20	104	24	207	9	111	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	710	5	717	6	717	7	798	7	690	5	846	9	826	8	824	9	532	9	702	11	866	15	627	7	604	8	618	4	687	2
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	141	11	119	25	206	18	198	23	199	12	447	13	312	11	315	18	365	18	375	17	440	20	315	15	372	13	84	31	243	4
	fir	123	12	51	29	57	33	101	28	68	24	88	31	92	20	89	20	31	37	66	38	41	43	7	49	16	68	13	78	69	7
	pine	21	27	33	28	12	59	5	48	30	27	0	.	8	84	2	76	2	56	56	44	0	.	73	44	98	28	17	70	25	14
	larch	0	.	4	72	12	85	1	99	6	44	0	.	0	.	2	99	4	69	4	97	0	.	102	19	39	28	50	30	16	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	45	17	53	0	.	4	37
	other conifers	1	60	1	99	0	99	5	99	4	53	0	.	1	100	3	73	0	.	3	98	2	98	0	.	0	.	0	.	1	30
	beech	215	8	217	12	167	16	256	18	142	11	71	28	126	18	215	19	91	25	127	42	138	32	36	44	35	49	161	18	152	5
	maple	40	15	32	24	18	46	27	56	24	23	42	34	25	20	30	35	68	32	11	63	42	63	7	69	2	69	3	70	27	9
	ash	19	32	17	32	30	38	16	34	33	23	14	55	21	39	59	35	37	34	17	59	11	59	6	61	0	.	11	43	21	12
	oak	17	34	25	28	18	29	6	58	25	37	8	85	2	77	2	76	14	74	2	98	0	.	1	99	0	.	14	42	12	16
	sweet chestnut	0	.	0	.	2	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	99	23	8	26
	other broadleaves	31	18	41	39	34	71	10	42	25	35	18	37	16	38	23	39	87	23	12	61	12	68	18	37	8	62	79	23	31	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	608	3	541	7	555	7	624	7	555	5	687	8	602	5	739	7	699	9	673	8	686	12	601	7	588	8	531	6	608	2
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	114	10	42	28	217	12	162	9	149	8	372	12	222	7	255	9	322	11	256	21	198	19	229	11	320	7	159	14	203	3
	fir	108	10	31	33	52	23	108	12	68	12	61	32	116	12	99	15	45	36	101	36	65	31	20	48	10	32	30	34	71	5
	pine	15	29	11	39	27	42	7	37	23	17	0	.	3	89	4	49	2	99	8	98	14	99	22	43	19	24	8	54	13	11
	larch	0	57	0	99	6	53	1	69	4	29	0	.	1	86	1	59	3	62	0	.	30	45	95	19	37	19	36	27	14	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	50	11	37	0	.	2	30
	other conifers	1	100	0	.	4	76	3	54	2	68	0	.	1	85	1	100	0	.	0	.	0	.	0	.	0	.	0	.	1	32
	beech	140	8	175	10	95	17	109	10	116	10	55	34	125	13	108	15	53	29	117	29	140	26	6	62	25	29	28	34	95	4
	maple	23	17	46	23	2	70	12	37	13	25	17	37	14	24	24	22	23	36	20	46	21	50	1	99	2	48	1	99	14	9
	ash	10	26	14	30	27	35	11	30	17	17	10	63	7	28	27	30	0	.	26	46	9	69	1	70	1	100	2	73	11	10
	oak	6	29	19	34	25	35	12	27	12	22	2	71	2	70	1	84	0	.	1	98	0	.	0	.	1	90	4	52	7	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	51	34	3	35
	other broadleaves	21	20	19	37	9	41	11	26	15	22	12	42	10	52	9	35	17	46	26	35	26	46	4	51	4	46	20	33	13	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	438	4	357	7	466	5	436	4	419	3	528	10	501	4	528	5	465	7	555	11	502	12	391	8	431	5	339	7	446	1
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	97	11	28	41	100	28	98	12	106	12	243	20	150	13	199	11	265	15	193	12	210	20	127	17	178	8	103	16	139	4
	fir	99	12	18	42	64	40	59	19	40	20	61	36	108	14	81	21	35	41	13	83	12	69	11	82	12	35	13	36	49	7
	pine	5	42	9	98	4	63	8	39	19	26	0	.	0	.	1	70	0	.	0	.	0	.	7	78	13	48	3	65	7	19
	larch	0	100	4	98	1	98	1	99	2	57	7	97	0	99	1	71	1	98	0	.	0	.	86	20	27	22	31	21	14	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	70	17	37	1	99	3	33
	other conifers	1	100	0	.	0	.	3	63	2	99	0	.	1	99	1	99	0	.	0	.	0	.	0	.	0	.	0	.	1	43
	beech	84	13	147	10	112	18	115	10	84	11	49	40	77	24	74	19	41	35	37	69	51	42	13	81	13	37	47	30	65	6
	maple	20	34	10	44	4	68	4	53	12	30	3	97	5	69	20	33	15	53	21	78	47	51	0	.	1	70	0	99	9	16
	ash	12	38	12	36	16	41	10	37	13	40	1	97	2	75	9	42	2	98	0	.	4	78	0	.	0	.	0	.	6	17
	oak	3	73	11	38	34	38	11	26	22	27	1	97	0	.	2	72	0	.	2	96	0	.	1	99	1	100	0	.	6	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	38	4	40
	other broadleaves	16	32	18	49	24	45	13	48	28	30	2	98	2	81	9	47	17	47	44	59	1	98	26	50	3	71	11	39	14	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	336	5	258	8	359	12	321	5	327	6	366	15	345	6	396	7	376	10	310	16	325	14	276	9	265	6	249	10	316	2
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	131	9	57	39	48	28	82	22	62	19	301	14	186	9	217	14	201	11	270	12	185	20	194	9	241	7	80	15	170	3
	fir	89	11	21	69	27	51	122	21	27	35	133	23	117	11	62	16	25	30	55	43	31	51	20	30	6	30	8	40	48	6
	pine	9	26	15	43	10	53	13	81	17	42	2	99	10	38	7	80	10	67	67	48	24	80	57	19	61	18	3	89	26	11
	larch	0	.	3	97	0	.	0	.	3	82	0	.	0	100	0	.	4	38	0	.	2	98	76	10	39	13	50	13	25	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	3	100	2	99	0	99	10	97	0	.	17	22	25	20	0	100	7	15
	other conifers	5	69	3	97	8	98	3	99	9	79	0	.	0	.	1	99	0	.	0	.	0	.	0	100	8	51	0	.	3	33
	beech	103	11	185	25	237	20	94	26	139	20	58	31	67	16	125	18	73	26	30	60	78	27	8	41	5	40	80	17	66	6
	maple	18	18	24	43	30	53	20	39	40	26	32	31	9	24	38	26	29	32	11	97	43	35	7	46	3	56	4	39	15	9
	ash	17	28	34	48	83	32	21	33	64	25	22	41	12	33	31	32	9	56	0	.	30	37	6	53	1	58	3	41	15	11
	oak	8	54	17	69	16	53	11	37	22	27	5	76	0	100	4	73	2	99	5	97	0	.	3	42	3	77	12	55	6	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	29	5	29
	other broadleaves	21	22	79	33	46	49	23	47	111	18	39	26	19	26	42	30	29	30	28	60	39	36	18	28	11	31	55	14	31	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	401	4	438	13	504	8	388	6	495	8	592	9	422	5	529	7	382	8	475	10	432	10	406	5	402	4	333	6	417	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	123	6	78	16	161	10	156	8	155	6	337	7	225	5	230	6	241	7	251	10	238	11	170	6	240	4	67	9	175	2
	fir	96	6	28	18	47	16	97	9	44	9	86	14	100	7	74	9	27	17	62	21	33	21	17	21	8	18	9	21	51	3
	pine	13	15	22	21	16	29	9	25	18	11	1	100	9	36	3	41	5	47	32	29	8	63	59	14	44	13	4	40	19	6
	larch	1	58	4	34	5	50	2	42	6	30	1	100	0	62	1	44	3	31	1	100	10	38	75	7	36	9	34	10	17	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	100	0	100	2	100	0	.	16	19	18	15	0	82	4	12
	other conifers	2	49	4	64	4	59	4	29	3	35	0	.	0	57	1	39	0	100	2	64	1	78	1	85	3	48	0	.	2	17
	beech	143	5	192	7	121	9	115	7	102	6	60	15	92	8	111	9	59	14	82	19	98	16	17	23	14	18	80	10	87	2
	maple	25	8	31	13	18	32	14	21	22	12	24	18	14	12	30	12	33	16	20	24	38	21	6	25	3	27	4	23	17	4
	ash	18	14	26	18	38	15	16	17	31	11	19	25	14	22	33	14	12	25	18	28	20	34	5	26	4	47	12	16	17	5
	oak	15	20	21	24	43	22	12	18	16	15	4	44	1	38	2	38	8	42	2	50	0	.	5	27	1	57	17	20	11	8
	sweet chestnut	0	.	0	.	3	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	1	53	0	83	87	10	10	9
	other broadleaves	25	10	33	17	37	22	17	19	37	11	21	19	20	18	33	20	35	14	35	26	32	23	38	14	18	17	94	7	36	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	459	2	440	4	493	4	442	3	436	3	551	5	476	3	519	4	423	4	507	6	479	7	409	3	388	3	408	3	446	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. 1284087. Birmensdorf, Swiss Federal Research Institute WSL

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