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: biogeographical region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		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	41	100	39	70	669	46	25	100	239	42	139	72	1152	30
	fir	0	.	0	.	93	73	0	.	0	.	0	0	93	73
	pine	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	larch	0	.	8	100	0	.	85	92	35	53	23	75	151	55
	Arolla pine	0	.	0	.	0	.	42	100	0	0	0	.	42	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	45	86	47	61	70	59	0	.	7	100	0	.	169	38
	maple	27	100	0	.	54	63	0	.	0	.	0	.	81	53
	ash	0	.	0	.	17	100	0	.	0	.	0	.	17	100
	oak	0	.	8	100	0	.	0	.	0	.	0	.	8	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	19	100	20	100	45	63	0	.	0	.	0	.	84	47
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	0	0	0
	total	131	58	121	45	948	40	152	81	282	40	162	68	1797	25
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	142	44	897	24	1861	19	295	35	780	32	211	50	4185	12
	fir	247	48	389	31	447	31	55	100	66	71	19	100	1223	19
	pine	23	100	59	100	7	100	0	0	167	52	0	.	255	42
	larch	0	.	55	54	43	72	259	41	92	50	62	63	510	25
	Arolla pine	0	.	0	.	0	.	0	.	36	100	0	.	36	100
	other conifers	0	.	20	100	15	100	69	100	72	79	0	.	176	53
	beech	294	37	1103	19	358	32	35	100	53	100	61	63	1904	14
	maple	71	61	259	34	558	32	17	100	17	100	26	76	948	22
	ash	89	54	243	31	177	43	55	100	0	.	35	100	600	22
	oak	7	100	90	55	0	.	0	0	0	.	7	100	104	49
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	11	100	11	100
	other broadleaves	100	42	550	27	553	29	159	53	341	55	434	46	2136	17
	indeterminable	0	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	972	27	3664	14	4018	14	944	33	1625	24	867	31	12089	8
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4416	25	12557	13	9663	16	2048	33	5962	21	2140	29	36786	8
	fir	1292	34	3481	23	1441	32	130	70	25	78	277	60	6646	16
	pine	278	60	539	54	283	67	2000	41	2135	31	25	100	5261	21
	larch	17	100	645	56	490	45	1592	29	928	37	2504	24	6176	15
	Arolla pine	0	.	0	.	0	.	178	91	458	57	0	.	636	48
	other conifers	149	52	339	34	232	86	17	100	483	44	51	75	1271	26
	beech	3058	24	3198	15	1961	30	144	71	35	100	2876	32	11272	12
	maple	1571	26	2321	23	2192	20	512	38	265	61	999	35	7859	12
	ash	779	32	1893	22	1730	28	730	38	180	52	1012	30	6325	13
	oak	669	62	709	54	141	88	422	56	0	.	1125	42	3067	26
	sweet chestnut	0	.	0	0	15	100	10	100	0	.	6184	22	6209	22
	other broadleaves	835	36	2212	19	3363	23	2592	24	3237	29	10315	16	22554	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	13065	14	27895	9	21510	11	10376	17	13709	14	27506	10	114061	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3167	25	5176	17	9712	15	2402	43	4953	23	887	40	26297	9
	fir	1389	23	1977	28	1992	27	0	.	193	56	302	70	5853	15
	pine	635	41	376	43	153	73	1359	36	1110	42	0	.	3632	21
	larch	21	75	134	55	35	100	812	33	1048	32	1103	33	3153	18
	Arolla pine	0	.	0	.	0	.	130	48	754	47	0	.	883	40
	other conifers	85	100	243	46	229	84	17	100	17	100	0	.	591	40
	beech	4745	16	3116	19	4308	21	404	61	440	67	4138	26	17150	10
	maple	702	22	993	24	2706	23	45	100	133	63	210	49	4791	14
	ash	481	30	1693	23	1463	28	54	75	184	74	650	61	4525	16
	oak	782	47	1045	30	151	40	155	49	0	.	416	32	2548	20
	sweet chestnut	0	.	7	100	0	.	55	100	0	.	1709	35	1771	34
	other broadleaves	698	34	1231	24	733	25	695	42	522	35	2701	24	6580	13
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	total	12705	13	15991	11	21481	12	6130	23	9354	18	12114	14	77775	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3428	15	4669	15	11109	12	2412	25	7302	15	2396	26	31315	7
	fir	3838	17	1832	18	3512	16	259	59	85	61	144	51	9670	10
	pine	477	29	641	22	264	56	331	48	455	39	14	100	2183	16
	larch	7	100	205	38	19	58	862	39	992	27	921	37	3006	19
	Arolla pine	0	.	0	.	0	.	0	.	372	36	0	.	372	36
	other conifers	174	74	106	46	36	71	0	.	0	.	0	.	316	44
	beech	7336	12	5933	12	5003	15	340	81	476	48	1154	38	20242	7
	maple	975	16	901	22	1403	17	18	100	33	71	18	100	3347	10
	ash	552	31	1545	21	1160	24	0	0	75	80	70	71	3402	14
	oak	377	30	546	25	16	71	18	100	35	100	76	54	1068	17
	sweet chestnut	0	.	0	.	7	100	0	.	0	.	727	45	734	45
	other broadleaves	733	26	723	22	1028	22	238	58	107	62	476	41	3305	13
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	.	0	0
	total	17897	10	17101	9	23556	9	4478	22	9932	13	5996	17	78960	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2668	15	4774	11	13701	9	2153	21	5207	14	1995	22	30499	5
	fir	3722	14	4140	12	4805	11	94	94	308	38	245	81	13315	7
	pine	77	39	351	24	123	95	187	87	141	44	0	.	879	26
	larch	29	50	35	52	93	51	1214	25	1143	24	787	29	3301	14
	Arolla pine	0	.	0	.	0	.	192	49	169	53	0	.	361	36
	other conifers	76	60	159	45	18	100	0	.	0	.	0	.	253	34
	beech	3642	12	6964	9	5762	12	0	.	749	32	734	46	17851	6
	maple	908	18	662	23	1426	18	55	100	157	58	0	.	3208	11
	ash	434	31	1443	19	843	24	18	100	12	100	0	.	2750	13
	oak	74	41	731	25	6	100	0	.	0	.	8	100	819	22
	sweet chestnut	17	100	0	.	0	.	0	.	0	.	235	48	252	45
	other broadleaves	500	32	1310	26	794	26	95	60	73	60	448	38	3220	14
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0
	total	12146	10	20569	7	27571	7	4010	18	7959	12	4453	16	76708	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5949	13	1780	19	20748	9	7386	15	10844	13	4956	16	51663	5
	fir	5751	15	2620	19	5638	13	454	50	343	37	659	36	15465	8
	pine	238	34	201	45	774	63	1259	31	824	37	334	59	3631	20
	larch	21	100	38	68	223	72	3675	17	1496	19	3358	18	8811	11
	Arolla pine	0	.	0	.	10	100	1191	28	1601	27	148	75	2949	19
	other conifers	219	38	143	63	167	60	18	100	139	68	0	.	685	27
	beech	5651	13	3053	17	6416	15	689	56	897	41	3433	21	20140	8
	maple	1753	19	625	31	2683	18	154	68	597	47	145	53	5957	11
	ash	667	29	1031	26	1609	22	380	85	524	64	444	37	4655	15
	oak	207	52	491	31	215	36	440	52	0	.	697	32	2050	18
	sweet chestnut	0	.	0	.	239	100	8	100	0	.	3541	22	3788	21
	other broadleaves	1135	23	1195	25	3340	20	776	29	621	33	3046	17	10113	10
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	0	0	0
	total	21592	9	11175	12	42062	7	16431	11	17886	10	20761	9	129907	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19811	8	29892	7	67462	5	16721	10	35288	7	12722	10	181897	3
	fir	16238	7	14440	8	17926	7	993	31	1021	21	1646	25	52265	4
	pine	1729	20	2167	18	1603	35	5137	20	4832	18	373	53	15841	10
	larch	94	37	1119	34	904	31	8498	11	5735	12	8758	11	25108	6
	Arolla pine	0	.	0	.	10	100	1734	22	3389	19	148	75	5280	14
	other conifers	702	28	1010	20	697	42	121	62	712	34	51	75	3293	15
	beech	24771	6	23414	5	23877	7	1611	34	2659	22	12396	13	88727	3
	maple	6006	10	5760	11	11023	9	802	29	1202	29	1398	26	26191	5
	ash	3002	13	7848	9	6999	11	1238	35	975	39	2211	24	22274	6
	oak	2116	27	3620	16	528	30	1035	32	35	100	2329	23	9664	11
	sweet chestnut	17	100	7	100	261	92	74	77	0	.	12406	13	12765	13
	other broadleaves	4021	13	7241	10	9855	11	4554	16	4902	20	17420	10	47992	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	78507	3	96517	3	141146	3	42520	7	60748	5	71859	4	491296	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. 1283815. Birmensdorf, Swiss Federal Research Institute WSL

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