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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NFI5
number of stems
development stage · main tree species
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	103	87	132	47	28	79	0	0	380	38	99	66	742	26
	fir	7	100	67	78	0	.	7	100	0	0	0	.	81	65
	pine	55	66	0	.	0	.	7	100	14	71	0	.	76	50
	larch	0	.	0	.	0	.	7	100	89	86	14	100	110	71
	Arolla pine	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	166	73	30	100	61	72	0	.	0	.	0	.	257	52
	maple	17	100	9	100	18	100	0	.	0	.	0	.	44	60
	ash	7	100	0	.	0	.	0	.	0	.	0	.	7	100
	oak	16	72	0	.	0	.	0	.	0	.	0	0	16	72
	sweet chestnut	0	.	7	100	0	.	0	.	0	.	0	0	7	100
	other broadleaves	7	100	0	.	0	.	0	.	0	.	0	0	7	100
	indeterminable	0	.	0	0	0	.	0	.	0	.	0	.	0	0
	total	378	51	245	42	107	56	21	58	483	39	113	59	1348	22
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	750	29	1333	24	460	35	86	66	689	29	304	54	3622	14
	fir	897	44	195	38	112	44	0	0	0	.	0	.	1204	34
	pine	66	45	0	0	7	100	0	.	64	89	0	.	137	47
	larch	111	79	44	86	18	100	33	71	54	60	39	61	299	36
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	47	61	15	100	36	100	0	.	0	.	0	.	98	49
	beech	570	20	468	35	188	38	0	.	14	100	26	100	1267	17
	maple	141	39	127	44	170	52	0	.	105	85	0	.	544	27
	ash	63	67	42	100	67	60	43	100	0	.	0	.	215	39
	oak	86	47	0	.	9	100	0	.	0	.	0	.	95	44
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	222	41	278	45	50	76	0	0	69	100	114	85	733	27
	indeterminable	0	.	0	0	0	.	0	.	0	.	0	.	0	0
	total	2954	21	2502	19	1118	28	162	55	996	28	491	42	8221	11
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	11402	13	5414	21	3354	26	1238	31	4427	25	934	54	26768	9
	fir	4559	19	1733	41	806	47	123	87	24	77	141	51	7387	16
	pine	430	40	104	100	17	100	1059	52	1827	36	0	.	3437	25
	larch	1050	45	453	44	0	.	846	38	138	52	934	39	3421	21
	Arolla pine	0	.	0	.	0	.	49	87	130	88	0	.	180	68
	other conifers	224	38	24	77	0	.	156	62	380	49	0	.	784	29
	beech	8743	12	1560	34	647	57	446	60	52	100	2552	31	14001	11
	maple	4594	16	1894	22	844	35	557	40	0	.	591	44	8480	11
	ash	2270	17	711	34	503	41	422	51	35	100	1004	32	4946	13
	oak	1213	40	128	85	0	.	670	45	0	.	835	53	2845	26
	sweet chestnut	0	.	0	.	0	.	15	100	0	.	3773	29	3788	29
	other broadleaves	3099	17	1160	25	648	32	2257	25	2062	37	5977	21	15203	11
	indeterminable	0	0	0	0	0	.	0	.	0	0	0	0	0	0
	total	37583	7	13181	14	6818	20	7838	17	9075	18	16743	14	91238	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8108	14	5836	23	2810	26	3650	32	3870	25	766	48	25039	10
	fir	2642	19	1446	33	865	38	7	100	144	63	292	70	5396	15
	pine	624	32	124	71	0	.	1964	37	799	50	181	87	3692	24
	larch	160	40	70	100	99	100	1327	27	561	38	833	49	3050	20
	Arolla pine	0	.	0	.	0	.	307	65	412	54	49	100	768	39
	other conifers	639	41	0	.	57	58	17	100	17	100	0	.	731	36
	beech	6047	15	1243	36	1899	33	291	70	179	49	4648	25	14307	12
	maple	2068	16	1393	33	839	33	0	.	255	63	235	57	4789	14
	ash	897	23	815	46	574	39	128	50	53	75	816	52	3283	20
	oak	1026	43	107	56	0	0	140	56	0	.	633	36	1906	26
	sweet chestnut	0	.	0	.	0	.	68	84	18	100	2568	28	2654	27
	other broadleaves	1854	19	481	36	537	47	704	35	786	62	3187	22	7549	13
	indeterminable	0	0	0	0	0	0	0	.	0	.	0	0	0	0
	total	24065	9	11513	16	7680	20	8604	20	7094	19	14207	13	73164	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6342	12	4735	20	4385	19	2247	28	5546	17	1601	33	24856	8
	fir	4890	14	1296	30	924	24	342	52	77	91	204	62	7734	11
	pine	926	26	50	100	76	90	367	46	652	40	14	100	2085	19
	larch	48	38	0	0	0	.	602	39	508	34	1077	33	2234	21
	Arolla pine	0	.	0	.	0	.	0	.	181	49	0	.	181	49
	other conifers	317	49	0	.	59	76	0	.	0	.	0	.	376	43
	beech	10876	9	2292	22	2357	19	453	67	228	59	1583	34	17790	7
	maple	2029	15	517	31	677	28	18	100	33	71	97	52	3371	11
	ash	1690	19	423	44	504	32	80	74	0	.	56	64	2753	15
	oak	923	25	55	74	0	.	68	62	0	.	119	51	1165	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	647	45	647	45
	other broadleaves	1794	17	407	31	190	31	156	46	81	65	1181	31	3809	13
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	29834	7	9775	15	9174	14	4332	22	7307	15	6579	16	67001	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5397	10	8100	11	5892	14	4191	17	6602	14	2977	18	33159	5
	fir	7360	9	5017	13	2037	18	458	41	232	44	597	43	15703	7
	pine	448	22	90	100	133	90	25	77	250	37	0	.	946	21
	larch	101	39	35	72	62	44	1453	24	1212	21	627	27	3491	13
	Arolla pine	0	.	0	.	0	.	189	49	318	39	0	.	507	30
	other conifers	325	41	0	.	54	57	0	.	0	.	0	.	378	36
	beech	10941	7	4261	15	2796	16	18	100	542	34	1137	34	19696	6
	maple	2302	13	595	21	910	22	261	52	85	61	0	.	4153	10
	ash	1346	15	249	44	446	30	71	62	30	72	0	.	2142	13
	oak	724	19	8	100	6	100	16	100	35	100	46	61	835	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	1121	34	1121	34
	other broadleaves	1921	22	538	27	321	28	134	52	199	60	668	35	3781	14
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	.	0	0
	total	30865	5	18894	9	12657	10	6816	15	9506	12	7174	13	85912	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6229	12	11273	11	4705	20	6298	15	8991	15	4332	28	41828	6
	fir	6833	12	5125	14	1471	25	563	43	157	40	327	48	14478	8
	pine	522	27	692	70	165	73	1200	31	561	40	28	79	3168	21
	larch	72	61	51	76	98	100	2674	21	1203	24	1778	24	5877	13
	Arolla pine	0	.	10	100	0	.	1046	31	1540	36	0	.	2596	25
	other conifers	243	35	35	70	133	74	0	.	121	77	44	70	575	29
	beech	6072	11	3994	17	2355	24	1008	42	600	50	2143	25	16172	8
	maple	1941	15	1641	18	705	35	368	43	184	44	199	47	5038	10
	ash	1222	20	499	28	294	38	167	62	472	64	364	32	3018	15
	oak	999	30	46	60	97	53	187	100	55	100	346	42	1730	23
	sweet chestnut	0	.	0	.	0	.	7	100	28	100	2190	26	2225	26
	other broadleaves	2567	16	1336	25	1028	31	1029	24	507	33	2835	26	9302	11
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	26699	7	24703	9	11050	15	14549	12	14420	12	14586	14	106007	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	38330	5	36822	6	21635	8	17710	10	30504	7	11014	13	156015	3
	fir	27189	6	14880	9	6216	12	1501	24	634	26	1563	25	51983	4
	pine	3070	13	1060	48	398	46	4621	22	4169	20	223	72	13541	11
	larch	1543	32	653	34	277	52	6942	12	3766	13	5302	15	18482	7
	Arolla pine	0	.	10	100	0	.	1592	24	2581	24	49	100	4231	17
	other conifers	1795	20	74	46	339	36	173	56	518	40	44	70	2942	15
	beech	43415	4	13848	9	10304	10	2216	28	1615	24	12090	13	83489	3
	maple	13092	7	6176	11	4162	13	1205	25	663	31	1122	28	26420	5
	ash	7495	8	2739	19	2389	16	912	29	589	52	2240	24	16364	7
	oak	4987	15	344	39	112	47	1080	34	91	72	1978	26	8591	12
	sweet chestnut	0	.	7	100	0	.	90	65	45	72	10308	14	10450	13
	other broadleaves	11464	8	4199	12	2774	17	4280	15	3705	25	13962	11	40383	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	152379	2	80813	4	48605	6	42322	7	48881	6	59893	5	432892	2

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. 1283934. Birmensdorf, Swiss Federal Research Institute WSL

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