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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NFI3
growing stock (stemwood)
development stage · main tree species
regional demarcation: biogeographical region
unit: 1000 m³
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 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	29	74	382	35	899	25	68	66	672	33	88	88	2139	17
	fir	48	100	63	52	143	47	8	71	49	87	0	0	311	32
	pine	0	.	12	100	0	.	2	100	0	.	0	.	14	87
	larch	0	.	0	.	23	100	19	71	55	58	48	77	145	38
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	8	100	140	34	79	75	0	.	10	100	30	73	267	30
	maple	0	.	0	.	57	39	0	.	0	.	0	.	57	39
	ash	0	.	31	67	2	72	0	.	0	.	0	.	33	64
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	3	100	5	82	12	69	3	100	9	100	0	0	32	42
	indeterminable	0	.	0	.	0	0	0	.	0	0	0	0	0	0
	total	88	86	633	27	1216	22	99	59	795	31	166	70	2998	14
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	246	36	520	29	1295	26	65	65	888	24	75	52	3089	14
	fir	235	47	195	50	369	36	0	.	82	50	0	.	881	23
	pine	74	60	133	49	0	.	49	65	22	98	0	.	279	32
	larch	14	100	48	52	2	100	208	41	148	47	81	40	501	23
	Arolla pine	0	.	0	.	0	.	46	76	3	100	0	.	49	71
	other conifers	1	71	1	100	2	100	8	100	5	74	0	.	16	55
	beech	398	33	842	19	291	32	1	100	2	100	61	50	1595	14
	maple	43	57	53	34	168	42	0	.	1	100	2	71	267	29
	ash	45	63	67	47	57	64	1	100	0	.	32	58	202	29
	oak	26	77	131	38	0	.	20	71	0	.	1	100	179	31
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	61	53	61	53
	other broadleaves	34	43	75	47	36	35	14	49	20	59	102	40	280	21
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	0	0	0
	total	1116	24	2064	15	2219	21	413	27	1173	21	414	24	7400	9
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1066	21	4230	11	3073	13	633	33	1514	18	274	28	10790	7
	fir	421	33	869	19	604	43	12	65	44	58	235	52	2185	16
	pine	61	44	287	27	76	43	376	32	330	27	0	.	1131	15
	larch	55	63	134	34	67	49	367	21	380	26	498	25	1500	12
	Arolla pine	0	.	0	.	16	100	57	81	36	71	0	.	109	51
	other conifers	84	59	120	42	29	63	0	.	14	41	27	86	274	28
	beech	739	30	750	16	635	26	34	67	34	94	1359	34	3551	16
	maple	172	28	580	17	386	20	42	37	17	61	119	38	1317	11
	ash	141	35	720	20	364	24	69	43	35	39	332	28	1661	12
	oak	95	48	280	28	46	54	77	48	0	.	176	32	673	17
	sweet chestnut	0	.	0	.	20	72	5	100	0	.	906	21	930	21
	other broadleaves	139	40	449	19	501	21	372	21	298	28	1503	14	3263	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	2973	14	8420	8	5817	11	2043	15	2701	13	5430	11	27384	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2664	15	4001	13	5382	15	1953	19	1723	20	814	27	16538	7
	fir	1540	18	1387	19	1212	19	239	47	37	66	116	83	4531	10
	pine	488	29	356	32	104	64	486	26	462	33	81	100	1977	15
	larch	12	100	206	41	3	100	1049	20	357	32	483	29	2110	14
	Arolla pine	0	.	0	.	0	.	133	48	97	64	7	100	236	38
	other conifers	37	82	264	41	11	62	1	100	3	100	0	100	315	36
	beech	4094	11	2370	15	1854	17	171	52	169	47	1328	20	9987	7
	maple	395	21	374	22	613	18	34	51	40	71	90	47	1547	11
	ash	557	26	1289	19	953	33	47	65	60	43	239	37	3145	14
	oak	439	33	829	27	78	61	45	54	0	.	358	29	1748	17
	sweet chestnut	0	.	0	.	0	.	15	100	0	.	1271	19	1286	19
	other broadleaves	351	28	555	22	524	32	171	39	127	36	1303	16	3032	11
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	0	0	0
	total	10577	9	11632	9	10734	11	4344	13	3076	15	6090	9	46453	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7486	9	8498	10	15674	8	3300	16	6750	11	2179	20	43888	4
	fir	5156	11	4236	13	4246	13	219	49	368	33	377	31	14601	7
	pine	1043	21	1245	20	352	35	388	35	1003	24	154	78	4185	11
	larch	26	71	588	28	103	48	1687	18	1201	20	1229	21	4835	10
	Arolla pine	0	.	0	.	60	82	65	51	182	40	0	.	306	31
	other conifers	14	48	83	63	2	100	0	100	11	93	0	.	111	49
	beech	9946	7	7081	9	5486	12	15	100	322	35	686	25	23535	5
	maple	1425	12	994	15	1200	14	20	100	56	68	22	96	3716	8
	ash	1010	19	1957	18	1043	19	0	0	56	62	91	62	4157	11
	oak	494	21	1215	22	200	40	0	0	97	83	161	51	2167	15
	sweet chestnut	0	.	0	.	17	85	0	.	0	.	436	31	453	30
	other broadleaves	539	19	689	21	622	21	130	46	27	56	364	29	2371	11
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	27140	5	26587	6	29005	6	5823	12	10072	9	5699	11	104326	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6451	11	16878	7	29852	6	3233	17	11512	10	4178	14	72103	4
	fir	5463	13	9753	9	11255	8	433	43	755	29	715	32	28373	5
	pine	284	40	1608	16	233	71	239	49	517	34	61	75	2943	13
	larch	67	52	434	32	619	38	3009	15	2425	16	2049	16	8604	8
	Arolla pine	0	.	0	.	0	.	174	39	364	33	0	.	538	25
	other conifers	80	96	319	50	6	77	0	.	0	.	0	.	405	44
	beech	4872	11	13200	7	7551	9	11	93	588	34	730	27	26952	5
	maple	694	19	846	17	1066	14	18	54	69	61	16	89	2709	9
	ash	442	23	2806	13	702	23	28	89	87	69	0	.	4065	10
	oak	392	36	2438	15	145	38	0	.	36	79	118	83	3129	13
	sweet chestnut	5	100	51	89	0	.	0	.	0	.	1043	30	1099	28
	other broadleaves	270	27	1030	28	772	28	42	52	55	47	127	28	2297	16
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	19019	7	49363	5	52202	5	7186	12	16409	8	9038	10	153217	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3515	12	1698	19	14362	7	2724	14	5739	11	2821	15	30859	5
	fir	3243	14	1371	26	4063	12	99	48	390	29	247	42	9413	8
	pine	347	27	315	38	341	27	448	26	648	25	113	45	2211	12
	larch	6	88	38	70	37	60	1856	19	1385	15	1768	14	5089	9
	Arolla pine	0	.	0	.	27	60	475	21	744	21	35	84	1282	15
	other conifers	17	52	253	55	19	112	0	.	26	60	0	.	315	45
	beech	1848	14	1619	19	2325	15	158	46	316	34	1596	17	7861	8
	maple	375	20	342	25	864	15	18	62	88	40	97	54	1785	10
	ash	202	29	651	24	649	23	51	76	71	74	157	33	1781	13
	oak	111	39	517	39	72	51	23	57	27	56	202	33	953	23
	sweet chestnut	0	.	0	.	47	100	11	100	0	.	1296	20	1355	19
	other broadleaves	190	24	624	25	536	16	140	40	194	43	892	16	2576	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	9855	9	7427	12	23343	6	6002	11	9629	8	9225	8	65481	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21457	5	36207	4	70537	3	11976	8	28798	5	10430	8	179406	2
	fir	16105	6	17873	6	21893	5	1009	24	1725	16	1689	18	60295	3
	pine	2297	13	3957	10	1107	21	1987	14	2982	13	409	39	12739	6
	larch	180	31	1449	16	854	29	8194	8	5952	9	6155	8	22785	4
	Arolla pine	0	.	0	.	103	52	950	16	1426	15	42	73	2522	11
	other conifers	234	42	1039	24	69	43	9	86	58	35	28	84	1437	19
	beech	21905	4	26002	4	18221	6	390	30	1441	18	5790	11	73749	2
	maple	3104	7	3189	8	4354	7	131	26	272	27	348	24	11398	4
	ash	2397	11	7521	8	3770	11	197	32	309	30	851	17	15045	5
	oak	1557	15	5411	10	540	21	165	29	160	54	1015	18	8849	7
	sweet chestnut	5	100	51	89	84	61	31	62	0	.	5013	10	5184	9
	other broadleaves	1526	11	3427	11	3004	11	870	15	730	18	4292	7	13850	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	70768	2	106126	2	124537	2	25911	5	43855	4	36062	3	407258	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. 1230999. Birmensdorf, Swiss Federal Research Institute WSL

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