Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-35calculated with twice the standard error, i.e. 29 ± 2 × 3

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI3
number of stems
development stage · main tree species
regional demarcation: biogeographical region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/07
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-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	22	83	244	36	1104	24	72	56	382	33	72	68	1895	16
	fir	24	100	66	46	221	42	40	80	8	71	0	0	359	30
	pine	0	.	4	100	0	.	10	100	0	.	0	.	14	76
	larch	0	.	0	.	4	100	8	71	23	55	16	71	51	36
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	14	100	205	38	215	57	0	.	4	100	51	78	489	31
	maple	0	.	0	.	60	38	0	.	0	.	0	.	60	38
	ash	0	.	28	73	19	71	0	.	0	.	0	.	47	52
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	11	100	30	74	46	51	30	100	48	100	0	0	165	40
	indeterminable	0	.	0	.	0	0	0	.	0	0	0	0	0	0
	total	71	80	577	27	1669	21	160	44	465	33	139	54	3081	14
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	327	56	838	28	1301	18	122	40	794	23	110	46	3493	12
	fir	225	29	198	39	264	26	0	.	71	50	0	.	758	17
	pine	30	62	44	46	0	.	299	77	42	79	0	.	415	57
	larch	4	100	21	52	14	100	317	30	82	41	262	43	701	22
	Arolla pine	0	.	0	.	0	.	45	66	10	100	0	.	55	57
	other conifers	21	71	10	100	21	100	39	100	51	60	0	.	140	40
	beech	251	28	723	18	220	27	13	100	30	100	154	63	1391	13
	maple	85	42	135	32	215	29	0	.	10	100	19	71	464	18
	ash	31	74	128	37	84	43	10	100	0	.	131	58	385	26
	oak	18	71	54	37	0	.	82	76	0	.	10	100	164	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	182	53	182	53
	other broadleaves	144	32	200	33	243	32	140	49	124	41	658	37	1509	19
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	0	0	0
	total	1135	24	2352	15	2361	14	1067	29	1215	18	1526	23	9656	8
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4617	20	13259	10	8950	13	2070	31	6155	16	1194	26	36246	6
	fir	1095	25	2152	20	823	26	62	79	46	69	339	47	4517	13
	pine	249	57	581	32	453	44	1693	29	1603	26	0	.	4579	16
	larch	56	67	376	30	362	39	1769	24	1029	29	1990	21	5581	12
	Arolla pine	0	.	0	.	24	100	137	65	184	49	0	.	345	37
	other conifers	208	50	437	44	205	57	0	.	253	43	29	76	1132	24
	beech	2281	24	2513	14	1970	21	149	78	58	62	3886	19	10856	10
	maple	1099	22	2264	17	1828	19	249	33	96	60	663	30	6199	10
	ash	557	30	2443	17	1532	22	328	40	171	42	1290	24	6321	10
	oak	471	52	751	28	241	61	560	35	0	.	980	32	3002	17
	sweet chestnut	0	.	0	.	46	84	6	100	0	.	4354	19	4406	19
	other broadleaves	788	32	2135	18	2876	18	2612	21	2423	24	8474	12	19307	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	11421	12	26911	7	19309	9	9634	13	12018	12	23199	8	102491	4
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3912	16	5828	13	8245	14	3959	20	3293	19	1746	25	26982	7
	fir	2183	16	1769	21	1596	20	498	45	134	55	203	67	6384	10
	pine	531	33	291	34	168	50	1223	26	1134	33	106	100	3453	16
	larch	8	100	270	44	19	100	1847	21	572	33	839	25	3555	14
	Arolla pine	0	.	0	.	0	.	270	46	228	66	4	100	502	39
	other conifers	129	86	202	40	102	71	10	100	31	100	10	100	484	33
	beech	6590	11	3740	15	3781	16	568	44	591	42	3033	20	18304	7
	maple	869	20	705	22	1570	18	134	48	88	54	306	41	3673	11
	ash	924	25	1484	18	1526	28	136	50	235	46	832	30	5137	12
	oak	736	37	1154	29	155	62	125	44	0	.	841	28	3011	17
	sweet chestnut	0	.	0	.	0	.	30	100	0	.	3239	20	3269	20
	other broadleaves	1036	21	1269	19	1566	22	610	30	440	33	3854	15	8776	9
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	0	0	0
	total	16918	9	16713	8	18728	10	9411	14	6747	15	15013	9	83530	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6402	9	5941	10	14682	8	3764	16	8100	11	2039	19	40928	4
	fir	5317	10	2972	13	3847	12	217	52	240	32	327	31	12921	6
	pine	841	21	698	19	355	41	496	34	1013	25	143	81	3546	12
	larch	14	77	286	30	111	46	1614	21	904	20	1259	23	4187	12
	Arolla pine	0	.	0	.	120	86	162	51	269	38	0	.	551	30
	other conifers	198	50	66	44	9	100	10	100	145	86	0	.	428	38
	beech	10121	7	6733	9	6626	13	47	100	585	30	1204	29	25316	5
	maple	2140	13	1330	16	2226	15	52	100	138	67	37	76	5922	8
	ash	1310	20	1419	15	1184	17	0	0	122	52	70	46	4106	10
	oak	635	25	790	22	211	52	0	0	88	77	125	46	1849	15
	sweet chestnut	0	.	0	.	8	71	0	.	0	.	580	30	588	30
	other broadleaves	1404	16	1057	19	1513	16	392	39	102	42	867	27	5336	9
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	28382	6	21293	6	30892	6	6754	13	11706	10	6651	11	105678	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3107	12	7127	8	15565	7	2571	17	7087	10	2731	14	38188	4
	fir	3510	12	5396	9	6504	9	383	44	323	30	442	33	16559	5
	pine	120	40	666	16	156	63	219	47	302	39	46	71	1508	15
	larch	26	54	151	34	220	45	1537	17	1146	17	1028	19	4108	9
	Arolla pine	0	.	0	.	0	.	194	35	413	31	0	.	607	24
	other conifers	33	58	201	33	31	74	0	.	0	.	0	.	265	27
	beech	3520	10	9012	7	7243	9	14	77	669	30	949	29	21407	5
	maple	1042	17	1044	15	1680	13	137	56	138	43	23	61	4065	8
	ash	424	22	1713	13	767	21	68	51	103	65	0	.	3075	10
	oak	163	29	1055	15	106	37	0	.	19	82	220	83	1564	16
	sweet chestnut	40	100	95	90	0	.	0	.	0	.	664	27	799	25
	other broadleaves	639	23	1538	19	1125	19	199	44	186	44	506	25	4193	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	12623	8	27997	5	33399	5	5323	12	10387	9	6610	10	96339	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3478	14	1158	19	17066	7	4549	14	9513	11	3945	15	39709	5
	fir	3359	13	1207	22	4480	12	112	43	378	30	402	37	9938	8
	pine	435	31	211	41	796	38	1046	26	1367	23	201	55	4056	13
	larch	14	78	24	61	31	63	2133	17	1915	15	2623	14	6738	9
	Arolla pine	0	.	0	.	58	85	1083	23	1568	21	73	67	2782	15
	other conifers	69	47	173	47	369	46	0	.	320	55	0	.	931	28
	beech	2784	13	1959	19	4263	14	479	46	630	34	3244	17	13360	8
	maple	725	20	577	23	2320	17	76	46	303	42	387	29	4387	11
	ash	299	26	741	26	1261	20	198	88	328	59	449	32	3276	13
	oak	310	41	374	33	97	41	151	49	35	56	331	35	1299	17
	sweet chestnut	0	.	0	.	95	100	4	100	0	.	2143	18	2242	18
	other broadleaves	640	22	1043	25	2409	14	652	29	931	38	3090	14	8764	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	12112	9	7467	12	33244	6	10482	10	17289	9	16888	8	97483	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21865	6	34395	5	66913	4	17106	8	35325	5	11837	8	187441	2
	fir	15714	5	13760	6	17735	5	1313	24	1202	16	1714	18	51437	3
	pine	2205	14	2495	11	1928	21	4985	14	5461	13	497	39	17570	7
	larch	121	36	1128	17	761	24	9225	9	5670	9	8017	8	24922	5
	Arolla pine	0	.	0	.	202	58	1891	16	2672	15	77	64	4843	11
	other conifers	657	28	1090	22	736	30	59	70	800	30	39	62	3381	13
	beech	25562	4	24885	4	24319	6	1269	28	2567	16	12521	9	91123	3
	maple	5959	7	6055	8	9898	7	649	22	774	24	1434	18	24770	4
	ash	3545	11	7956	7	6374	10	741	31	959	26	2772	15	22346	5
	oak	2333	18	4179	11	810	26	918	24	142	51	2507	18	10889	8
	sweet chestnut	40	100	95	90	149	69	40	77	0	.	11161	10	11487	9
	other broadleaves	4661	9	7273	8	9778	8	4635	14	4254	17	17449	7	48050	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	82663	3	103309	2	139603	3	42832	5	59827	4	70025	3	498258	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. 1356649. Birmensdorf, Swiss Federal Research Institute WSL

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