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: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/06
		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	300	60	644	34	424	49	-4	100	133	67	143	94	1640	24
	fir	124	71	146	78	46	91	0	.	0	.	0	0	317	47
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	39	100	0	0	0	0	32	100	86	76	156	53
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	201	38	129	77	6	100	0	.	18	100	21	100	374	34
	maple	0	.	37	62	40	71	0	.	0	.	0	.	77	48
	ash	34	87	3	72	0	.	0	.	0	.	0	.	37	81
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	12	71	0	.	18	82	0	.	0	.	0	.	30	56
	indeterminable	0	.	0	0	0	.	0	.	0	0	0	0	0	0
	total	671	38	997	31	534	45	-4	100	183	53	250	81	2631	20
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	637	29	640	50	311	52	109	71	503	43	48	100	2248	21
	fir	349	45	88	107	219	48	0	0	4	100	0	.	660	32
	pine	290	44	0	.	0	.	29	100	40	100	0	.	359	38
	larch	42	71	0	.	22	100	310	47	138	51	52	94	564	31
	Arolla pine	0	.	0	.	0	.	60	99	6	100	0	.	66	91
	other conifers	0	.	0	.	0	.	13	100	0	.	0	.	13	100
	beech	1848	19	82	84	122	78	1	100	0	.	65	63	2119	18
	maple	161	33	8	100	17	62	8	100	0	.	2	100	197	29
	ash	248	39	25	84	5	75	0	.	0	.	0	.	278	36
	oak	213	41	0	.	0	.	18	100	0	.	2	100	233	38
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	90	63	90	63
	other broadleaves	159	42	21	57	16	64	5	77	21	84	13	80	235	30
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	3948	15	866	42	712	40	553	34	712	34	273	42	7064	12
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5126	14	2144	22	1289	30	921	41	1326	26	109	42	10914	10
	fir	1142	24	368	39	307	41	20	64	84	54	265	64	2187	17
	pine	394	29	11	100	0	.	386	45	340	38	0	.	1131	22
	larch	179	39	67	67	22	100	285	30	258	45	285	33	1095	17
	Arolla pine	0	.	0	.	0	.	84	100	47	97	0	.	131	73
	other conifers	185	44	6	100	0	.	7	100	16	56	42	100	257	36
	beech	1326	22	312	72	142	41	154	61	3	100	977	31	2915	17
	maple	629	20	144	41	162	46	42	47	29	67	100	45	1107	15
	ash	715	29	82	40	301	37	64	54	13	58	109	45	1283	19
	oak	331	38	35	65	0	.	88	71	0	.	208	44	662	26
	sweet chestnut	0	.	0	.	0	.	30	76	0	.	1036	30	1066	29
	other broadleaves	409	26	218	51	227	40	354	25	385	34	1135	21	2728	12
	indeterminable	0	0	0	0	0	.	0	0	0	0	0	0	0	0
	total	10437	10	3386	19	2450	22	2435	21	2501	19	4267	14	25476	6
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6769	13	3089	22	2624	34	1409	26	1174	32	683	39	15747	10
	fir	2501	20	579	34	660	33	181	68	48	92	21	93	3990	15
	pine	1082	26	19	100	0	.	556	36	352	50	0	.	2009	19
	larch	220	42	0	.	34	84	901	27	335	50	317	44	1806	19
	Arolla pine	0	.	0	.	0	.	116	69	20	100	0	.	135	61
	other conifers	272	58	0	.	8	77	2	100	0	.	0	.	282	56
	beech	6569	12	684	35	1653	26	219	65	49	69	1069	30	10244	10
	maple	728	20	351	31	510	29	14	73	7	76	21	62	1630	14
	ash	1835	21	587	47	967	46	55	72	50	65	196	63	3690	18
	oak	1042	25	19	63	5	100	44	78	0	.	253	37	1364	21
	sweet chestnut	0	.	0	.	0	.	28	100	0	.	1355	27	1383	27
	other broadleaves	1025	22	60	57	481	50	173	59	142	48	1390	23	3271	15
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0
	total	22044	8	5387	17	6942	22	3696	18	2176	24	5306	14	45551	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14498	9	9480	14	5715	17	3889	20	6445	15	2491	25	42519	6
	fir	8620	11	2463	19	1462	23	416	51	204	58	436	41	13602	9
	pine	2003	20	261	53	216	57	224	51	1180	33	61	100	3944	15
	larch	452	38	36	60	55	100	977	27	1205	26	1314	30	4039	15
	Arolla pine	0	.	0	.	0	.	40	71	245	49	0	.	285	43
	other conifers	96	77	0	.	4	100	1	100	0	0	0	.	101	73
	beech	15163	8	2021	19	2953	23	125	83	283	46	567	32	21112	7
	maple	2131	12	665	21	649	30	101	55	6	100	40	100	3592	10
	ash	3105	20	585	34	770	32	12	100	0	.	133	76	4606	16
	oak	1235	20	20	100	32	100	17	100	0	.	192	57	1495	19
	sweet chestnut	0	.	5	100	0	.	0	.	0	.	507	36	512	35
	other broadleaves	1140	19	301	37	262	42	169	48	8	61	370	36	2250	14
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	48444	6	15837	11	12118	13	5970	17	9577	13	6110	15	98057	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19696	9	14741	11	16140	11	3691	21	11235	13	4257	20	69760	5
	fir	12636	10	8088	13	4630	17	251	73	587	40	649	46	26841	7
	pine	1670	18	268	100	124	62	163	85	499	35	38	100	2761	17
	larch	323	42	270	52	258	46	3115	20	2668	21	1247	28	7881	12
	Arolla pine	0	.	0	.	0	.	230	48	200	46	0	.	430	33
	other conifers	431	65	69	100	6	71	0	.	0	.	0	.	507	57
	beech	14945	8	5774	16	4163	17	25	100	580	45	499	41	25985	6
	maple	1590	17	461	31	656	20	40	62	45	88	1	100	2793	12
	ash	3149	15	271	43	618	40	11	123	36	85	0	.	4085	14
	oak	2652	19	0	.	98	55	3	100	50	100	17	80	2819	18
	sweet chestnut	8	100	0	.	0	.	0	.	0	.	873	41	881	41
	other broadleaves	1095	23	589	45	256	30	32	54	66	57	59	44	2096	18
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	.	0	0
	total	58195	5	30531	9	26948	9	7560	15	15968	11	7639	15	146841	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4305	15	9306	11	2412	21	2886	18	4921	15	2290	24	26119	6
	fir	4321	16	3078	19	815	33	211	49	245	47	229	66	8899	11
	pine	594	36	143	63	35	94	553	26	450	36	94	68	1869	17
	larch	23	72	18	100	0	.	2124	29	1045	21	1320	23	4529	16
	Arolla pine	0	.	12	100	0	.	276	36	784	26	4	100	1076	21
	other conifers	414	57	3	100	-10	174	0	.	16	62	0	.	423	56
	beech	3105	16	2195	24	393	32	245	46	315	48	1710	24	7962	11
	maple	516	19	725	23	222	41	31	68	91	52	80	60	1666	13
	ash	684	30	185	48	383	44	99	73	129	73	60	55	1539	20
	oak	719	49	89	65	42	82	10	72	56	100	181	50	1097	34
	sweet chestnut	0	.	0	.	0	.	21	100	19	100	953	27	993	26
	other broadleaves	594	36	327	27	94	40	111	60	80	40	583	25	1791	16
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	0	0	0
	total	15272	11	16082	10	4385	19	6568	15	8152	12	7504	12	57964	5
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	51329	4	40045	6	28914	8	12900	10	25736	7	10022	11	168948	2
	fir	29694	6	14809	8	8138	11	1080	30	1173	25	1601	25	56495	4
	pine	6032	10	701	45	375	40	1911	18	2862	18	192	50	12074	8
	larch	1239	20	430	36	391	35	7710	12	5680	12	4621	13	20071	7
	Arolla pine	0	.	12	100	0	.	807	24	1302	20	4	100	2124	15
	other conifers	1399	29	79	89	9	201	23	67	32	42	42	100	1584	27
	beech	43157	4	11196	10	9432	11	769	30	1248	26	4909	13	70711	3
	maple	5755	7	2391	12	2256	13	237	29	179	37	244	32	11061	5
	ash	9770	9	1739	21	3043	20	240	38	229	45	498	34	15518	7
	oak	6192	11	163	41	176	41	180	42	106	71	853	22	7670	10
	sweet chestnut	8	100	5	100	0	.	78	52	19	100	4814	13	4925	13
	other broadleaves	4434	10	1516	21	1354	21	844	20	702	22	3550	11	12401	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	159010	2	73087	4	54090	6	26778	7	39268	6	31349	4	383583	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. 1230977. Birmensdorf, Swiss Federal Research Institute WSL

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