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
growing stock (stemwood)
development stage · main tree species
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/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 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	116	76	237	70	79	87	0	0	472	42	68	71	972	30
	fir	13	100	131	78	0	.	10	100	0	0	0	.	153	68
	pine	88	55	0	.	0	.	8	100	40	80	0	.	135	43
	larch	0	.	0	.	0	.	37	73	79	58	45	100	161	43
	Arolla pine	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	113	62	39	100	21	91	0	.	0	.	0	.	174	47
	maple	4	100	17	100	33	71	0	.	0	.	0	.	54	54
	ash	10	100	0	.	0	.	0	.	0	.	0	.	10	100
	oak	67	80	0	.	0	.	0	.	0	.	0	0	67	80
	sweet chestnut	0	.	6	100	0	.	0	.	0	.	0	0	6	100
	other broadleaves	34	100	0	.	0	.	0	.	0	.	0	0	34	100
	indeterminable	0	.	0	0	0	.	0	.	0	.	0	.	0	0
	total	445	41	430	56	133	59	54	55	590	38	113	58	1766	22
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	845	28	1270	25	298	31	79	58	1446	32	421	51	4359	15
	fir	348	33	237	43	137	51	0	0	0	0	0	.	722	23
	pine	118	48	0	0	8	100	0	.	76	73	4	100	206	39
	larch	45	69	127	78	1	100	42	87	168	54	169	63	551	32
	Arolla pine	0	.	0	.	0	.	0	.	27	100	0	.	27	100
	other conifers	19	68	2	100	5	100	0	.	2	100	0	.	27	51
	beech	678	31	570	61	201	36	0	.	19	100	17	100	1485	28
	maple	146	41	78	47	60	50	10	100	17	88	0	.	310	25
	ash	56	80	35	100	38	75	0	100	0	.	0	.	129	49
	oak	154	54	0	.	10	100	0	.	0	.	0	.	164	51
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	142	55	83	48	21	65	7	79	53	79	59	87	364	30
	indeterminable	0	.	0	0	0	.	0	.	0	0	0	.	0	0
	total	2549	18	2401	22	780	25	137	44	1807	28	678	46	8352	11
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4380	14	2449	19	1647	25	605	29	1607	22	476	38	11163	9
	fir	2203	19	873	31	594	35	73	71	47	81	128	56	3918	14
	pine	231	38	50	100	64	85	190	44	361	30	0	.	896	20
	larch	345	35	131	45	0	.	639	32	415	38	661	30	2191	16
	Arolla pine	0	.	0	.	0	.	37	79	146	43	0	.	184	38
	other conifers	93	48	127	70	5	100	13	60	29	44	0	.	266	38
	beech	2649	12	562	38	410	41	127	76	23	89	928	26	4699	11
	maple	1097	18	381	24	313	30	184	37	2	100	166	39	2143	12
	ash	732	19	192	36	216	43	215	45	39	74	205	42	1600	14
	oak	325	30	28	84	0	.	92	44	0	.	221	35	666	20
	sweet chestnut	0	.	0	.	0	.	26	100	0	.	953	28	979	28
	other broadleaves	837	26	319	25	256	39	592	31	577	29	1356	20	3937	11
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	12891	8	5111	14	3507	19	2794	17	3246	16	5093	12	32642	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6402	13	3547	21	1988	26	1776	31	2886	23	514	36	17112	9
	fir	2159	18	875	33	538	40	8	100	155	71	210	68	3945	14
	pine	586	27	45	92	0	.	733	36	407	52	78	86	1850	21
	larch	172	43	52	100	150	74	777	26	432	37	504	39	2087	17
	Arolla pine	0	.	0	.	0	.	131	76	287	47	8	100	426	39
	other conifers	355	33	0	.	8	68	1	100	2	100	0	.	365	32
	beech	4329	14	658	36	1084	36	66	61	69	50	2065	24	8271	11
	maple	1038	19	607	30	605	32	0	.	80	56	141	53	2472	14
	ash	761	23	575	51	316	36	46	52	41	76	386	59	2125	20
	oak	710	32	62	67	0	0	29	60	0	.	399	38	1199	23
	sweet chestnut	0	.	0	.	0	.	42	78	5	100	1088	26	1134	25
	other broadleaves	833	22	132	37	136	42	176	47	197	61	1464	23	2938	14
	indeterminable	0	0	0	0	0	0	0	.	0	.	0	0	0	0
	total	17345	9	6554	15	4826	19	3784	19	4559	18	6857	13	43925	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7941	12	5938	18	6257	19	2321	30	5689	17	1794	30	29940	7
	fir	5470	14	1522	26	1494	23	271	55	98	89	286	53	9142	11
	pine	1371	23	100	100	58	86	327	43	897	36	31	100	2785	17
	larch	160	41	0	0	0	.	774	37	994	27	1305	33	3233	18
	Arolla pine	0	.	0	.	0	.	19	100	222	48	0	.	240	45
	other conifers	303	55	0	.	7	79	0	.	0	.	0	.	310	54
	beech	10766	9	2211	25	2246	20	197	70	199	63	910	35	16529	8
	maple	1609	14	324	28	629	25	5	100	18	72	22	58	2607	11
	ash	2481	18	352	54	660	35	62	92	0	.	116	89	3671	15
	oak	1390	24	69	85	12	100	26	59	0	.	149	54	1647	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	637	44	637	44
	other broadleaves	1052	21	98	30	143	35	25	45	15	53	735	39	2069	18
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	32544	7	10614	14	11507	14	4027	22	8131	14	5986	16	72809	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14649	9	17174	10	14362	12	6099	16	11110	13	5229	18	68624	5
	fir	15395	9	8694	13	5266	18	730	44	780	36	1089	39	31953	6
	pine	1251	22	229	100	206	63	26	77	475	36	0	.	2187	19
	larch	364	37	112	71	277	44	3781	21	3272	19	1657	24	9464	11
	Arolla pine	0	.	0	.	0	.	249	53	324	45	0	.	574	34
	other conifers	1064	48	0	.	20	60	0	.	0	.	0	.	1084	47
	beech	18445	7	5344	17	4073	17	1	100	654	43	1078	33	29595	6
	maple	1774	15	458	25	986	22	76	56	50	67	0	.	3344	11
	ash	2919	17	437	38	855	31	11	64	45	95	0	.	4267	13
	oak	2287	23	11	100	19	100	33	100	64	100	34	60	2448	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	1404	33	1404	33
	other broadleaves	1245	23	486	61	313	33	74	67	126	73	205	41	2449	18
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	.	0	0
	total	59392	5	32945	8	26377	10	11081	14	16901	11	10697	12	157392	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6921	11	12287	10	5267	15	4479	15	7482	13	4093	30	40530	6
	fir	6155	12	5250	14	1681	26	550	44	316	39	355	40	14307	8
	pine	781	28	376	54	166	49	635	28	462	36	51	59	2472	16
	larch	144	57	40	70	39	96	3420	21	1911	18	2449	16	8002	11
	Arolla pine	0	.	12	100	0	.	610	26	762	27	0	100	1385	19
	other conifers	71	41	10	83	-13	160	0	.	9	73	82	98	159	56
	beech	5352	12	2867	17	2252	20	529	41	346	48	1640	22	12987	8
	maple	1440	16	1161	18	532	32	200	47	266	44	149	47	3747	10
	ash	1426	25	439	42	423	41	128	68	149	62	205	33	2770	17
	oak	1000	23	65	67	146	53	96	76	85	100	215	44	1607	18
	sweet chestnut	0	.	0	.	0	.	97	76	18	100	1323	26	1438	24
	other broadleaves	1244	20	454	30	434	30	302	28	140	38	1184	25	3757	12
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	24532	7	22962	8	10928	13	11046	12	11948	10	11747	14	93163	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	41253	5	42901	5	29898	7	15359	9	30693	7	12596	12	172699	2
	fir	31742	5	17581	8	9710	11	1642	26	1397	24	2068	24	64140	4
	pine	4427	11	801	41	503	34	1918	18	2717	17	165	49	10531	8
	larch	1230	18	463	33	466	36	9470	12	7270	11	6789	11	25688	6
	Arolla pine	0	.	12	100	0	.	1047	22	1768	18	9	94	2836	13
	other conifers	1904	29	138	64	32	81	13	58	42	35	82	98	2211	25
	beech	42332	4	12251	10	10289	10	920	30	1310	27	6638	11	73741	3
	maple	7106	7	3026	11	3159	12	475	26	432	30	479	25	14678	5
	ash	8385	9	2030	21	2509	16	463	31	274	40	913	30	14573	7
	oak	5934	12	236	37	187	43	276	33	149	71	1017	20	7799	10
	sweet chestnut	0	.	6	100	0	.	165	51	23	82	5412	13	5605	12
	other broadleaves	5386	9	1571	22	1304	16	1175	19	1108	21	5004	11	15548	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	149699	2	81017	4	58057	6	32923	7	47182	5	41172	4	410050	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. 1354531. Birmensdorf, Swiss Federal Research Institute WSL

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