Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
number of stems
development stage · main tree species
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	56	54	508	30	181	66	110	52	331	39	117	56	1303	19
	fir	4	100	208	49	11	100	31	100	4	100	0	.	257	41
	pine	0	.	0	0	0	.	26	100	0	.	0	.	26	100
	larch	8	71	0	.	0	.	76	60	20	53	9	100	114	42
	Arolla pine	0	.	0	.	0	.	23	100	0	0	0	.	23	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	84	41	89	45	20	71	0	.	4	100	8	100	205	27
	maple	35	71	21	82	19	71	0	.	0	.	0	.	76	44
	ash	4	100	0	.	10	100	0	.	16	100	0	.	30	64
	oak	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	34	58	4	100	41	61	0	.	13	100	0	0	92	38
	indeterminable	0	.	0	0	0	0	0	.	0	0	0	.	0	0
	total	229	31	830	25	283	54	267	40	388	35	133	53	2130	15
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	872	20	1328	17	675	23	248	30	1018	21	147	42	4288	9
	fir	622	19	336	27	246	33	30	100	32	69	11	100	1277	14
	pine	112	37	0	.	10	100	38	78	95	51	0	.	255	28
	larch	51	52	38	58	38	78	209	32	133	31	144	40	614	17
	Arolla pine	0	.	17	100	0	.	0	.	34	72	0	.	51	58
	other conifers	11	100	9	100	93	81	41	100	61	62	0	.	214	44
	beech	1109	14	165	35	239	29	21	100	42	79	36	63	1611	11
	maple	333	21	241	29	198	44	29	54	10	100	14	77	824	16
	ash	249	25	161	35	24	82	40	79	0	.	19	100	493	19
	oak	95	35	0	.	0	.	0	0	0	.	4	100	98	34
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	59	90	59	90
	other broadleaves	502	18	338	30	306	28	276	35	200	55	276	41	1898	13
	indeterminable	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	total	3955	10	2633	13	1828	17	933	23	1624	18	710	24	11684	6
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14658	10	6505	16	4511	17	2562	20	7009	16	1492	27	36737	6
	fir	3906	16	1295	26	795	34	286	61	111	84	249	75	6641	12
	pine	746	33	512	51	79	89	1747	30	2419	23	14	100	5517	15
	larch	507	40	371	40	85	78	2622	19	1893	26	2180	20	7657	11
	Arolla pine	0	.	0	.	48	100	185	55	331	49	10	100	573	34
	other conifers	376	23	138	86	10	100	61	62	406	34	9	100	1000	21
	beech	5753	11	779	32	1012	29	616	37	115	59	3689	20	11964	9
	maple	4362	12	1085	21	1365	26	516	26	253	55	954	25	8536	9
	ash	3080	13	574	30	1023	29	606	28	268	34	1260	20	6810	9
	oak	1371	27	210	75	0	.	772	34	4	100	924	30	3281	17
	sweet chestnut	0	0	0	.	0	.	61	79	0	.	4573	19	4634	19
	other broadleaves	3669	16	1457	22	1572	27	3271	16	3491	20	8449	12	21908	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	38428	6	12926	11	10498	12	13306	10	16301	10	23801	8	115260	3
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8452	10	5651	15	4069	18	2747	26	4629	17	1202	31	26750	7
	fir	3003	14	933	33	834	27	207	64	80	63	250	52	5307	11
	pine	983	24	24	72	82	80	1367	26	1029	30	93	100	3578	15
	larch	243	34	4	100	65	76	987	24	783	27	1117	25	3199	13
	Arolla pine	0	.	0	.	0	.	261	44	682	38	0	.	942	30
	other conifers	519	35	13	100	44	50	10	100	10	100	10	100	606	31
	beech	8032	9	1612	26	2199	21	604	43	328	45	3606	20	16380	8
	maple	1607	14	1324	24	1035	24	229	41	58	74	297	39	4551	11
	ash	2061	15	526	36	597	28	67	54	159	59	953	33	4362	12
	oak	1721	21	98	41	15	74	137	40	0	.	550	32	2521	16
	sweet chestnut	4	100	0	.	0	.	30	100	0	.	1946	23	1980	22
	other broadleaves	1931	16	667	36	536	31	804	29	504	33	3036	16	7476	9
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	28556	6	10851	12	9477	13	7449	14	8262	13	13060	10	77655	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7004	8	6411	12	5746	13	2746	18	7471	12	2240	21	31618	5
	fir	5218	10	2081	17	1669	17	323	38	229	40	333	38	9854	7
	pine	930	14	52	66	303	47	263	37	693	27	153	74	2395	13
	larch	215	30	15	60	132	64	942	26	829	19	894	26	3028	13
	Arolla pine	0	.	96	100	0	.	122	76	399	32	0	.	617	30
	other conifers	319	48	9	100	41	50	0	.	126	78	0	.	496	37
	beech	12120	7	3134	19	2435	15	239	65	423	35	1453	25	19804	6
	maple	1881	10	836	20	745	19	47	60	49	45	36	69	3594	8
	ash	1666	15	432	27	699	22	4	100	38	79	121	51	2960	11
	oak	897	18	126	73	25	61	14	78	86	77	65	43	1212	16
	sweet chestnut	0	.	4	100	0	.	4	100	0	.	620	38	628	38
	other broadleaves	1591	14	744	20	408	24	324	40	86	62	526	30	3679	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	31843	5	13939	10	12203	10	5027	15	10430	10	6440	12	79883	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7396	7	8301	8	5633	10	3257	15	5891	11	2219	15	32697	4
	fir	7579	7	4200	10	2024	14	145	48	231	35	228	52	14407	5
	pine	523	17	81	85	34	65	227	50	173	37	22	100	1060	16
	larch	144	29	91	43	107	50	1579	16	1184	20	594	21	3700	10
	Arolla pine	0	.	0	.	0	.	124	45	191	34	0	.	315	27
	other conifers	209	31	35	67	62	52	0	.	0	.	0	.	307	25
	beech	11039	5	3724	12	3104	12	74	61	420	31	836	30	19197	5
	maple	2086	10	691	16	557	20	197	50	62	55	93	70	3687	8
	ash	1730	12	447	26	334	23	67	51	62	59	11	100	2652	10
	oak	930	15	47	46	34	46	0	0	0	.	38	64	1049	14
	sweet chestnut	20	71	0	.	0	.	0	.	0	.	393	29	413	28
	other broadleaves	1852	14	539	24	388	25	169	42	55	51	549	23	3552	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	33507	4	18157	7	12277	8	5839	11	8271	9	4983	12	83034	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7762	9	14122	8	5619	12	7349	10	10981	10	4686	13	50520	4
	fir	6706	9	5069	11	1532	19	868	26	283	32	592	28	15050	6
	pine	472	20	652	49	461	40	962	26	868	24	120	75	3536	14
	larch	58	42	50	62	120	74	3726	12	1770	15	3064	13	8787	8
	Arolla pine	0	.	9	72	72	100	1467	19	1598	20	52	64	3198	13
	other conifers	561	36	51	66	181	46	10	100	226	64	30	100	1058	25
	beech	7773	9	4639	13	2370	18	572	40	507	38	3502	17	19362	6
	maple	2236	13	1749	16	865	24	358	32	366	41	329	35	5903	9
	ash	1861	13	770	23	684	27	390	50	308	59	639	26	4650	10
	oak	635	19	74	44	74	42	320	42	71	61	475	27	1649	14
	sweet chestnut	76	100	0	.	0	.	134	97	17	100	2996	16	3223	16
	other broadleaves	2436	13	1495	19	1100	27	957	19	598	25	3810	12	10396	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	30576	6	28681	6	13076	10	17112	8	17593	8	20296	7	127334	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	46201	4	42826	4	26433	6	19020	7	37330	5	12103	8	183914	2
	fir	27038	4	14122	6	7111	8	1890	18	971	19	1662	19	52794	3
	pine	3766	10	1322	32	970	26	4630	15	5277	13	402	43	16367	7
	larch	1227	19	569	28	547	29	10142	8	6613	10	8002	8	27100	5
	Arolla pine	0	.	122	80	119	72	2182	15	3235	14	62	56	5720	10
	other conifers	1993	16	255	50	431	28	122	47	830	27	49	68	3680	12
	beech	45910	3	14142	7	11378	7	2125	21	1839	17	13128	9	88523	3
	maple	12541	5	5948	9	4784	11	1376	16	799	27	1724	17	27171	4
	ash	10649	6	2911	12	3372	12	1173	22	850	27	3003	14	21958	4
	oak	5653	10	555	34	148	27	1242	24	161	49	2056	17	9815	8
	sweet chestnut	99	78	4	100	0	.	229	62	17	100	10587	10	10937	10
	other broadleaves	12016	6	5242	10	4350	13	5800	11	4947	15	16645	7	49001	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	167092	2	88018	3	59643	4	49933	5	62869	4	69424	3	496978	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. 1283864. Birmensdorf, Swiss Federal Research Institute WSL

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