Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
growing stock (stemwood)
development stage · main tree species
regional demarcation: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-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	27	100	2	609	673	27	102	66	394	40	142	77	1340	21
	fir	0	.	9	72	169	50	3	100	22	100	0	0	203	43
	pine	0	.	0	.	0	0	28	100	0	.	0	.	28	100
	larch	0	.	20	74	0	.	89	48	78	50	52	91	239	32
	Arolla pine	0	.	0	.	0	.	15	100	0	0	0	.	15	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	15	75	29	87	40	50	0	.	13	100	15	100	112	35
	maple	7	100	5	100	18	59	0	.	0	.	0	.	30	46
	ash	0	.	14	100	2	100	0	.	11	100	0	.	27	66
	oak	0	.	5	100	0	.	0	.	0	.	0	.	5	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	1	100	7	77	5	73	0	.	13	82	0	0	26	48
	indeterminable	0	.	0	.	0	0	0	.	0	0	0	0	0	0
	total	51	76	91	42	907	24	237	44	531	32	209	75	2025	17
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	90	56	722	23	1628	19	150	41	1145	24	178	39	3913	12
	fir	165	43	381	31	553	32	8	100	147	59	0	100	1255	19
	pine	70	53	66	66	9	94	33	98	39	78	0	.	217	34
	larch	0	.	41	68	81	60	179	43	119	57	118	39	539	23
	Arolla pine	0	.	0	.	29	100	0	.	6	83	0	.	34	85
	other conifers	0	.	1	100	7	69	3	100	5	71	0	.	17	42
	beech	249	35	824	20	467	27	1	100	11	79	10	72	1562	14
	maple	24	51	85	51	260	27	1	100	1	100	13	84	384	22
	ash	27	81	64	42	53	39	10	81	0	.	7	100	160	26
	oak	7	100	149	48	0	.	0	0	0	.	3	100	159	45
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	30	77	30	77
	other broadleaves	19	39	145	44	86	23	57	52	42	47	28	53	378	20
	indeterminable	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	total	650	24	2479	14	3173	14	443	30	1514	21	389	27	8647	8
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1614	19	3844	12	3712	12	645	24	1723	16	482	23	12020	6
	fir	583	28	1511	15	529	23	70	59	28	70	121	57	2842	11
	pine	109	44	234	29	144	43	393	34	426	23	8	100	1313	15
	larch	46	63	164	30	91	44	619	20	524	24	677	23	2122	12
	Arolla pine	0	.	0	.	22	100	64	68	66	56	0	100	153	40
	other conifers	34	59	79	34	30	75	1	100	30	36	31	88	205	24
	beech	790	28	1142	14	731	21	11	75	45	85	972	20	3691	10
	maple	260	26	988	16	571	18	59	35	67	59	172	25	2118	10
	ash	210	33	725	17	399	23	156	38	61	37	315	23	1865	10
	oak	96	40	431	26	58	52	63	36	12	100	254	28	913	16
	sweet chestnut	0	.	0	0	21	74	5	100	0	.	1197	20	1223	19
	other broadleaves	200	26	726	19	642	17	407	19	495	23	1714	13	4184	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	3942	13	9845	7	6950	9	2492	14	3476	11	5945	8	32649	4
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2064	18	4670	13	5823	12	1247	26	2480	17	484	31	16768	7
	fir	969	19	1381	20	1254	22	100	80	65	54	122	52	3891	12
	pine	572	33	327	34	59	49	677	28	647	35	78	100	2360	16
	larch	29	61	241	36	26	81	687	26	441	26	752	25	2176	14
	Arolla pine	0	.	0	.	0	.	116	50	252	35	0	.	368	29
	other conifers	100	78	324	39	20	57	0	100	1	100	1	100	446	33
	beech	3410	12	1998	15	1664	17	190	47	191	50	1500	20	8952	7
	maple	342	19	508	25	1044	17	68	46	61	59	108	41	2130	11
	ash	404	27	1181	18	707	26	20	58	37	68	452	33	2800	12
	oak	520	30	649	23	71	44	32	44	0	.	291	32	1562	15
	sweet chestnut	0	.	3	100	0	.	14	100	0	.	821	22	839	21
	other broadleaves	262	29	556	23	265	32	274	36	127	42	1311	17	2794	11
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	8672	9	11839	9	10930	10	3425	15	4301	13	5919	10	45086	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4372	11	6643	11	13479	9	2235	19	6859	11	2395	20	35983	5
	fir	3575	13	2889	14	4400	12	172	59	349	37	432	35	11816	7
	pine	650	21	974	17	296	43	230	37	714	25	193	71	3056	11
	larch	5	100	473	28	66	40	934	25	1467	17	1144	23	4089	11
	Arolla pine	0	.	0	.	48	100	40	74	379	30	0	.	466	27
	other conifers	55	67	148	52	7	63	0	.	9	90	0	.	218	39
	beech	7784	8	5759	10	4541	13	90	68	287	34	927	23	19387	5
	maple	831	15	675	17	855	13	3	100	25	53	19	69	2408	8
	ash	678	24	2005	18	911	22	0	0	49	69	113	52	3755	12
	oak	401	25	871	20	121	60	3	100	109	69	78	55	1584	15
	sweet chestnut	0	.	0	.	7	71	0	.	0	.	440	43	448	42
	other broadleaves	433	26	562	22	467	22	62	54	47	58	300	36	1871	12
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	18785	7	20997	7	25197	7	3767	16	10293	9	6040	12	85080	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7308	11	12634	8	29752	6	3564	16	11266	10	4279	15	68804	4
	fir	6965	11	10088	9	10696	9	145	55	789	31	434	46	29118	5
	pine	367	39	1141	19	230	76	295	50	332	32	33	100	2398	15
	larch	63	50	442	33	551	41	3065	16	2753	19	1850	18	8724	9
	Arolla pine	0	.	0	.	0	.	135	50	178	38	0	.	312	30
	other conifers	106	96	426	52	8	74	0	.	0	.	0	.	541	45
	beech	6725	9	13652	7	7353	10	14	93	571	36	859	27	29174	5
	maple	854	15	844	17	978	14	13	71	74	71	59	68	2823	9
	ash	469	23	2684	14	913	19	23	81	43	62	12	100	4144	10
	oak	543	33	2055	18	97	43	0	.	0	.	27	71	2722	15
	sweet chestnut	1	100	2	100	0	.	0	.	0	.	777	37	780	37
	other broadleaves	389	37	1284	31	688	30	33	47	19	67	196	35	2610	18
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	23790	7	45253	5	51267	5	7286	12	16025	9	8528	11	152150	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5692	9	2697	15	17976	6	4600	12	7739	10	3757	14	42460	4
	fir	4452	11	2268	16	5933	10	576	37	586	26	729	29	14544	6
	pine	370	25	360	33	431	30	391	26	751	25	115	47	2418	12
	larch	28	97	80	49	32	64	3378	13	1394	14	2491	12	7404	8
	Arolla pine	0	.	0	.	26	86	732	19	709	21	45	70	1511	14
	other conifers	18	88	231	53	8	183	1	100	19	69	1	100	278	45
	beech	4072	12	2272	13	4492	11	202	53	373	31	2039	19	13450	6
	maple	768	16	424	29	1322	13	39	70	205	50	71	42	2829	10
	ash	744	21	1200	16	823	18	61	67	74	53	275	32	3176	10
	oak	226	36	787	22	116	37	77	52	26	67	347	28	1579	14
	sweet chestnut	0	.	71	100	56	100	17	100	0	.	2096	16	2240	16
	other broadleaves	331	18	906	22	667	19	175	35	179	30	1269	14	3526	9
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	16700	7	11295	9	31882	5	10249	9	12055	8	13234	7	95415	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21167	5	31211	5	73044	3	12544	7	31605	5	11716	8	181288	2
	fir	16709	6	18527	6	23533	5	1073	25	1986	17	1838	18	63668	3
	pine	2138	13	3102	10	1169	22	2048	15	2908	13	427	40	11791	6
	larch	171	31	1461	15	847	28	8951	8	6776	9	7085	8	25291	4
	Arolla pine	0	.	0	.	125	51	1102	15	1589	14	45	69	2861	10
	other conifers	313	43	1210	24	80	38	5	67	64	30	33	83	1705	19
	beech	23045	4	25676	4	19286	5	508	30	1491	18	6323	9	76329	2
	maple	3087	7	3530	8	5047	6	182	26	433	30	442	18	12721	4
	ash	2532	11	7872	7	3807	9	269	28	275	25	1173	16	15928	5
	oak	1792	15	4948	9	463	22	175	28	146	53	1000	15	8524	7
	sweet chestnut	1	100	76	93	85	69	36	63	0	.	5363	10	5559	9
	other broadleaves	1635	13	4186	12	2820	10	1007	14	923	15	4818	7	15389	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	72589	2	101798	2	130306	2	27900	5	48196	4	40265	3	421054	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. 1231061. Birmensdorf, Swiss Federal Research Institute WSL

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