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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NFI1
number of stems
development stage · main tree species
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	932	26	1336	22	349	46	251	53	277	42	0	.	3146	14
	fir	361	36	783	37	7	100	56	78	10	100	0	.	1217	26
	pine	290	51	0	.	0	.	0	.	0	.	0	.	290	51
	larch	35	100	0	.	0	.	246	100	108	63	39	74	427	61
	Arolla pine	0	.	0	.	0	.	0	.	49	100	0	.	49	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	793	25	514	46	207	70	0	.	42	66	0	.	1555	22
	maple	295	36	164	49	72	66	17	100	0	.	0	.	548	26
	ash	510	39	205	72	386	77	100	73	0	.	0	.	1201	33
	oak	226	57	0	.	0	.	55	100	0	.	25	100	307	47
	sweet chestnut	0	.	0	.	0	.	58	79	0	.	0	.	58	79
	other broadleaves	513	35	175	44	232	64	127	61	55	100	89	63	1192	23
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3956	18	3177	22	1253	49	911	42	541	33	153	45	9990	13
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	964	32	1626	25	647	35	120	42	689	40	220	50	4265	15
	fir	398	39	191	55	77	50	7	100	65	87	43	100	781	26
	pine	162	45	0	.	0	.	69	79	0	.	0	.	231	40
	larch	75	56	0	.	0	.	138	42	147	51	145	54	506	26
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	35	71	17	100	0	.	87	100	0	.	0	.	138	66
	beech	438	30	75	72	129	51	0	.	0	.	68	53	710	23
	maple	178	49	113	58	161	64	18	100	102	71	18	100	590	29
	ash	180	48	50	76	12	100	71	100	0	.	0	.	312	38
	oak	65	65	21	100	0	.	56	58	0	.	0	.	142	40
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	98	68	98	68
	other broadleaves	162	41	274	37	7	100	175	53	370	61	262	41	1249	24
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2657	20	2366	21	1032	29	741	30	1372	28	854	26	9023	10
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17224	13	8326	20	5800	24	3489	28	3446	28	886	54	39170	9
	fir	4714	20	609	36	1382	30	202	91	89	100	156	95	7153	15
	pine	1290	32	378	100	104	100	3315	30	885	43	0	.	5973	20
	larch	449	37	91	83	161	71	1497	30	439	47	1059	35	3695	18
	Arolla pine	0	.	0	.	0	.	199	53	186	69	0	.	384	43
	other conifers	511	46	0	.	119	61	0	.	18	100	0	.	648	38
	beech	11092	12	2022	35	2157	28	847	54	166	100	4338	26	20622	10
	maple	2142	18	833	32	1108	26	114	56	37	100	168	46	4402	13
	ash	3508	17	1206	39	1010	30	106	73	605	64	729	32	7164	13
	oak	3061	27	201	57	39	71	252	66	0	.	1447	37	5001	20
	sweet chestnut	0	.	0	.	0	.	117	82	0	.	8044	18	8161	18
	other broadleaves	3557	20	883	31	1756	35	1371	30	1457	34	8282	14	17306	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	47548	8	14548	15	13635	17	11509	17	7328	20	25110	10	119678	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12501	12	10454	15	6421	21	2876	29	2905	31	1334	41	36491	8
	fir	7087	15	2229	24	1371	25	45	87	0	.	72	73	10804	11
	pine	1645	22	232	90	200	56	162	56	993	44	0	.	3233	19
	larch	92	62	43	87	28	100	842	32	372	41	969	39	2346	21
	Arolla pine	0	.	0	.	0	.	78	61	125	83	0	.	204	56
	other conifers	182	47	0	.	62	58	0	.	0	.	0	.	244	38
	beech	14322	10	3204	22	3465	25	227	75	520	58	2302	28	24040	8
	maple	1981	15	1436	24	650	42	115	88	15	100	39	100	4237	13
	ash	1556	24	992	31	751	44	117	63	0	.	103	60	3519	17
	oak	1380	23	188	57	0	.	0	.	0	.	219	53	1787	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	1729	33	1729	33
	other broadleaves	1912	21	972	28	273	44	208	50	74	100	1302	32	4740	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	42660	7	19748	12	13221	16	4672	22	5004	24	8069	17	93374	5
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15886	8	12444	12	7227	14	2518	24	8597	15	2043	24	48714	5
	fir	9322	10	4044	18	2447	21	107	75	345	40	451	52	16716	8
	pine	2176	18	182	71	48	88	213	70	605	33	85	100	3309	15
	larch	296	33	58	64	59	52	832	38	1175	27	207	42	2626	18
	Arolla pine	0	.	0	.	0	.	60	100	300	42	0	.	360	38
	other conifers	223	49	7	100	18	100	0	.	0	.	0	.	248	45
	beech	14576	8	4947	16	2594	19	62	100	621	40	517	45	23316	6
	maple	1633	18	723	27	783	25	18	100	47	62	35	100	3240	13
	ash	1494	17	229	33	519	34	36	71	28	100	40	100	2345	14
	oak	1414	18	21	100	6	100	0	.	58	100	42	71	1541	17
	sweet chestnut	0	.	0	.	0	.	0	.	28	100	515	61	543	58
	other broadleaves	1798	17	629	41	553	29	35	100	194	47	299	44	3507	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	48818	5	23283	10	14252	12	3881	21	11998	13	4233	19	106464	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5468	12	3301	19	3807	18	1126	29	3386	17	1671	24	18759	7
	fir	3256	14	2034	21	922	25	29	100	165	60	127	49	6533	10
	pine	391	28	0	.	15	71	32	81	303	49	88	66	829	24
	larch	47	47	16	71	25	72	683	31	683	31	363	32	1817	18
	Arolla pine	0	.	0	.	0	.	46	100	374	50	0	.	420	46
	other conifers	71	59	0	.	18	100	0	.	0	.	0	.	89	51
	beech	5704	11	1724	27	1419	25	195	100	462	38	278	64	9783	9
	maple	844	29	154	60	505	40	0	.	37	71	0	.	1541	21
	ash	579	29	15	100	131	47	0	.	0	.	0	.	726	25
	oak	499	29	0	.	40	64	18	100	28	100	0	.	587	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	541	59	541	59
	other broadleaves	1274	24	124	57	145	44	307	71	89	85	181	63	2120	19
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	18134	9	7368	15	7028	15	2437	23	5528	14	3249	20	43744	6
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6431	13	9420	12	4223	21	7497	15	9875	13	2652	22	40098	6
	fir	4785	14	4273	16	964	29	789	38	205	42	340	52	11357	9
	pine	523	28	336	56	263	72	2271	27	2105	28	144	88	5643	16
	larch	50	100	82	45	0	.	2667	16	1369	19	1560	21	5727	11
	Arolla pine	0	.	8	100	0	.	710	31	1182	28	0	.	1901	21
	other conifers	281	60	0	.	22	100	17	100	52	100	0	.	372	48
	beech	7273	12	2828	18	1901	29	494	45	216	63	2916	25	15628	9
	maple	1678	18	733	22	473	31	308	60	100	84	105	67	3397	13
	ash	2007	20	688	33	570	41	86	84	0	.	111	49	3461	15
	oak	811	39	21	100	91	75	103	47	0	.	329	54	1356	28
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	1112	42	1112	42
	other broadleaves	2457	19	997	24	979	36	544	38	141	50	1370	24	6488	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	26296	8	19386	10	9487	16	15486	11	15245	11	10639	13	96539	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	59406	5	46907	6	28474	8	17877	10	29175	7	8805	12	190643	3
	fir	29924	6	14163	8	7170	11	1237	29	879	25	1189	28	54562	4
	pine	6478	11	1128	43	630	39	6063	19	4891	17	317	52	19508	9
	larch	1043	21	289	34	272	45	6905	12	4292	12	4342	14	17144	7
	Arolla pine	0	.	8	100	0	.	1093	23	2216	19	0	.	3318	15
	other conifers	1303	25	24	77	238	37	104	85	70	78	0	.	1740	20
	beech	54198	4	15312	9	11872	11	1824	31	2028	23	10419	14	95653	3
	maple	8752	8	4156	12	3752	14	591	37	338	36	365	32	17953	6
	ash	9834	8	3384	18	3379	18	515	32	632	62	983	25	18728	7
	oak	7457	13	451	35	177	44	485	38	87	75	2063	27	10720	11
	sweet chestnut	0	.	0	.	0	.	175	61	28	100	12039	13	12242	13
	other broadleaves	11673	9	4054	13	3944	19	2766	19	2380	24	11785	10	36602	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	190068	2	89876	4	59908	6	39636	7	47016	6	52308	5	478813	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. 1356588. Birmensdorf, Swiss Federal Research Institute WSL

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