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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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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NFI2
growing stock (stemwood)
main tree species · development stage
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
main tree species	development stage	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	young growth/ thicket	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pole timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	young timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	medium timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	old timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	mixed	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
spruce	n/a	0	.	0	.	0	.	67	100	0	.	32	.	0	.	0	0	0	.	0	.	0	.	3	100	62	100	10	100	175	56
	young growth/ thicket	16	85	19	101	103	55	133	53	90	59	210	75	449	37	124	51	96	72	107	65	13	90	89	64	583	42	107	69	2140	18
	pole timber	2101	22	633	37	390	44	1721	27	2734	21	886	34	2165	23	1165	30	566	40	221	66	164	56	823	31	827	29	557	39	14954	8
	young timber	3366	18	517	44	1812	37	1835	26	1938	25	3228	25	3112	22	2716	27	1528	27	1345	44	1274	51	1778	26	5218	17	592	47	30259	7
	medium timber	6929	13	1815	30	3900	25	4275	19	7224	15	3952	24	5809	16	5773	17	4257	21	2030	37	1207	35	3264	20	7851	14	2419	25	60704	5
	old timber	3894	18	940	47	1670	35	5943	19	6072	18	1530	36	6190	18	5303	20	2138	32	1259	36	2287	30	2349	25	6787	16	3058	24	49421	6
	mixed	2172	18	20	100	379	54	607	38	220	39	1485	31	3634	17	1942	25	2774	25	535	46	717	32	3055	18	3327	18	2127	24	22993	7
	total	18479	7	3944	19	8254	16	14581	10	18277	9	11323	13	21359	8	17023	10	11358	12	5498	20	5662	18	11361	10	24656	7	8870	12	180646	2
fir	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0
	young growth/ thicket	44	59	3	100	0	.	122	65	0	.	14	100	254	62	70	54	94	71	0	.	39	100	1	100	122	75	6	100	769	28
	pole timber	830	32	48	67	286	58	639	39	318	32	191	49	205	52	507	35	14	72	201	47	2	100	38	91	68	72	287	64	3632	14
	young timber	3005	18	312	56	141	47	1360	32	313	44	764	44	1250	29	898	30	90	72	320	54	11	100	135	51	112	67	175	65	8885	11
	medium timber	4433	15	524	41	643	44	3451	21	2519	18	760	54	2742	21	1684	29	562	37	495	58	334	62	272	61	90	69	440	48	18949	8
	old timber	2639	22	118	63	592	48	2167	24	1621	30	347	52	3424	19	1272	29	665	47	232	66	155	91	174	81	382	45	125	84	13913	9
	mixed	1308	23	3	100	68	63	768	39	416	48	789	41	3253	21	566	41	460	41	38	100	367	43	247	52	249	70	316	67	8848	11
	total	12259	8	1007	28	1730	25	8507	12	5187	13	2865	22	11128	10	4995	14	1884	23	1287	30	908	33	867	30	1023	27	1348	28	54997	4
pine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	young growth/ thicket	0	.	0	.	72	104	70	100	54	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	93	75	0	.	289	45
	pole timber	225	48	204	42	91	86	78	47	170	40	0	.	105	100	9	100	7	100	0	.	0	.	970	27	318	39	2	100	2179	16
	young timber	535	37	323	42	115	54	32	61	299	44	0	.	102	71	0	.	17	101	93	74	0	.	309	40	695	33	54	100	2576	16
	medium timber	441	33	209	51	531	58	382	44	1175	26	0	.	305	87	93	71	69	100	94	100	0	.	278	58	631	37	0	.	4207	15
	old timber	206	52	16	100	56	56	321	46	562	31	0	.	112	100	58	64	0	.	0	.	0	.	40	71	629	43	51	75	2049	19
	mixed	143	52	30	100	26	100	0	.	114	58	0	.	0	.	0	.	0	.	44	100	0	.	268	45	322	42	90	72	1036	22
	total	1550	19	782	24	890	38	882	27	2374	16	0	.	624	50	160	48	94	76	231	54	0	.	1866	19	2688	17	196	47	12336	8
larch	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	29	100	72	73	110	55
	young growth/ thicket	0	.	0	.	20	100	0	.	8	71	0	.	0	.	12	100	3	74	18	100	0	.	79	63	94	52	41	75	276	30
	pole timber	31	71	64	73	15	100	10	100	131	51	0	.	0	.	0	.	31	77	0	.	45	72	426	25	249	38	392	30	1395	15
	young timber	0	.	3	100	54	74	101	81	35	71	0	.	0	.	58	100	38	100	0	.	45	93	758	26	1212	22	773	40	3076	15
	medium timber	19	100	30	71	63	100	57	100	290	62	0	.	37	100	46	100	208	58	0	.	0	.	1607	26	1508	21	473	31	4337	14
	old timber	38	100	0	.	69	100	31	100	180	57	0	.	39	100	67	87	92	66	0	.	159	71	1786	25	1461	29	814	33	4736	15
	mixed	0	.	0	.	0	.	0	.	62	83	0	.	0	.	0	.	79	92	0	.	0	.	2065	26	465	26	1123	24	3794	16
	total	88	54	97	53	221	47	198	53	706	32	0	.	77	71	183	52	450	36	18	100	250	50	6731	12	5018	12	3687	14	17724	6
Arolla pine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	young growth/ thicket	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pole timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	189	46	23	59	0	.	211	42
	young timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	35	81	382	40	0	.	424	37
	medium timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	153	51	466	38	0	.	619	31
	old timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	71	264	42	0	.	292	38
	mixed	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	314	38	182	42	1	100	504	28
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	0	.	718	23	1316	20	1	100	2050	15
other conifers	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	young growth/ thicket	0	.	0	.	0	.	2	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	7	65
	pole timber	1	100	20	72	13	100	64	84	49	72	1	100	1	100	5	100	4	100	1	100	-19	100	18	90	6	69	12	100	177	41
	young timber	5	72	96	91	97	100	49	100	2	71	0	.	0	.	3	100	0	.	0	.	2	100	0	.	0	0	0	.	254	55
	medium timber	3	100	2	70	0	.	146	87	20	100	0	.	59	100	3	71	0	.	0	.	0	.	0	.	0	.	0	.	232	61
	old timber	0	.	0	.	0	.	151	73	158	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	309	62
	mixed	14	58	0	.	0	.	0	.	14	100	0	.	1	100	0	.	0	.	3	100	0	.	1	100	0	.	0	.	33	50
	total	23	40	118	75	110	89	413	44	244	67	1	100	61	96	11	55	4	100	4	75	-17	112	19	85	10	58	12	100	1013	28
beech	n/a	0	.	0	.	0	.	93	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	79	124	58
	young growth/ thicket	13	213	129	81	55	77	18	92	97	69	55	89	51	66	107	59	0	.	0	100	73	87	1	100	74	76	82	53	756	25
	pole timber	1354	24	281	36	118	39	589	31	604	30	173	58	402	46	408	41	172	67	126	47	106	75	180	46	51	100	1638	22	6204	10
	young timber	5494	13	1783	23	1076	31	1116	25	1096	24	578	45	1361	30	1662	29	293	48	164	58	257	71	300	53	337	51	588	37	16105	8
	medium timber	4956	13	2359	20	1542	32	2485	21	3912	16	343	51	2006	26	1964	30	1223	28	321	57	496	44	10	100	101	43	269	42	21987	7
	old timber	2252	22	2282	26	693	41	3428	20	3179	18	215	67	2434	29	1288	33	230	59	187	48	464	42	81	80	598	46	302	54	17634	9
	mixed	963	24	51	75	217	61	365	54	620	33	153	61	807	26	497	34	199	42	44	61	253	42	62	72	72	60	905	30	5209	11
	total	15033	7	6885	12	3702	18	8094	11	9508	9	1518	24	7061	14	5926	15	2117	19	843	27	1651	22	633	31	1233	27	3816	13	68018	3
maple	n/a	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	27	71
	young growth/ thicket	0	.	0	.	2	100	11	100	17	60	23	95	0	.	10	67	0	.	0	.	13	81	8	63	23	100	27	96	135	34
	pole timber	155	30	117	46	-15	144	102	46	218	37	63	53	147	34	211	39	92	43	75	48	232	54	7	265	18	71	92	38	1515	14
	young timber	855	18	291	31	35	64	51	54	442	24	177	58	127	54	159	46	105	50	17	71	73	53	84	52	12	71	0	.	2428	11
	medium timber	526	20	236	56	21	71	91	46	456	31	66	61	377	37	553	31	264	39	77	52	129	57	9	100	30	74	0	.	2836	12
	old timber	177	55	130	46	14	100	51	67	226	41	2	100	115	48	157	37	128	77	21	71	125	62	7	100	21	172	0	.	1174	18
	mixed	219	31	46	100	28	59	35	73	129	46	80	49	124	32	265	42	155	52	0	.	88	63	24	58	19	85	14	95	1225	16
	total	1945	11	821	22	85	47	341	24	1489	15	411	30	890	20	1356	17	742	23	204	29	660	27	139	37	124	43	133	34	9341	6
ash	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	young growth/ thicket	1	100	0	.	0	.	17	95	1	100	3	100	0	.	44	97	0	.	2	100	5	100	0	.	0	.	0	.	74	63
	pole timber	369	42	156	50	109	78	165	44	371	29	155	70	128	74	390	33	2	100	77	76	188	83	38	52	61	62	160	41	2368	15
	young timber	567	54	399	41	399	37	271	37	839	27	75	71	216	61	383	39	82	73	8	72	103	83	60	63	0	.	11	100	3413	15
	medium timber	581	31	201	52	296	43	585	75	1110	23	87	100	230	36	180	43	136	64	205	76	45	64	4	100	51	100	40	79	3751	16
	old timber	114	63	73	53	142	86	280	56	435	35	14	100	20	100	125	78	40	100	5	100	18	100	20	78	9	100	0	.	1296	22
	mixed	150	50	22	73	220	59	121	64	353	50	68	58	70	77	208	56	56	61	0	.	62	72	72	69	91	100	53	52	1546	20
	total	1781	22	852	25	1165	24	1439	34	3109	13	401	38	663	29	1330	19	315	37	297	56	421	44	195	34	212	52	265	29	12447	7
oak	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	young growth/ thicket	0	.	0	.	3	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	7	62
	pole timber	392	40	32	74	464	46	97	68	85	43	6	100	8	73	4	100	30	65	4	100	0	.	22	52	0	.	497	31	1641	19
	young timber	200	49	206	49	234	58	202	40	97	49	13	81	106	81	40	100	1	100	0	.	0	.	41	51	0	.	37	55	1177	20
	medium timber	448	39	227	61	452	40	243	41	827	24	0	.	0	0	48	100	0	.	13	100	0	.	0	.	42	100	82	100	2383	16
	old timber	64	72	209	53	162	89	160	43	859	32	39	100	0	.	16	100	0	.	25	100	0	.	2	100	0	.	15	71	1551	22
	mixed	0	.	10	100	0	.	187	74	97	57	3	100	0	.	0	.	0	.	22	100	0	.	34	88	62	100	100	55	514	34
	total	1105	23	682	30	1316	26	890	24	1966	17	60	68	114	76	108	60	32	62	64	56	0	.	103	37	104	72	731	25	7274	9
sweet chestnut	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	100	26	100
	young growth/ thicket	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	30	100
	pole timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	57	0	.	1656	19	1756	18
	young timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	692	40	692	40
	medium timber	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	253	75	254	75
	old timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	742	44	742	44
	mixed	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	1180	30	1197	30
	total	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	57	17	100	4580	13	4698	13
other broadleaves	n/a	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	young growth/ thicket	4	100	0	.	0	.	3	71	1	100	0	.	16	56	19	59	2	100	5	100	3	59	25	55	17	62	80	53	175	28
	pole timber	164	34	65	56	99	59	244	38	144	50	45	58	64	42	221	47	33	41	78	60	107	55	346	30	361	32	1679	14	3650	9
	young timber	236	33	94	54	242	54	37	47	413	37	42	47	113	42	105	40	163	64	1	1364	53	82	106	51	35	59	316	52	1957	16
	medium timber	274	31	237	52	124	67	85	61	459	36	37	59	78	61	183	39	366	45	21	103	98	57	29	64	55	42	119	62	2164	15
	old timber	107	44	125	72	16	67	256	69	310	42	11	52	41	56	11	75	135	94	43	68	27	100	14	100	37	71	114	36	1247	23
	mixed	89	44	70	65	89	72	42	55	105	65	36	47	47	60	75	63	76	37	16	100	48	64	67	52	27	47	461	32	1247	16
	total	873	16	590	29	570	31	668	31	1432	19	172	25	359	22	612	23	775	30	163	38	336	30	587	21	531	23	2770	11	10440	6
indeterminable	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	young growth/ thicket	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pole timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	young timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	medium timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	old timber	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	mixed	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
total	n/a	13	100	0	.	0	.	160	59	0	.	32	.	0	0	0	0	0	.	14	.	0	.	13	100	90	75	140	52	463	31
	young growth/ thicket	79	46	152	82	256	66	378	51	269	43	304	59	770	38	387	36	195	50	132	56	147	71	206	45	1011	31	373	32	4658	13
	pole timber	5622	14	1619	26	1571	27	3709	19	4825	15	1520	27	3226	19	2919	20	951	31	784	35	825	41	3157	15	1982	19	6971	10	39682	5
	young timber	14263	10	4024	19	4205	21	5054	19	5475	16	4877	22	6387	17	6024	19	2322	23	1948	34	1819	40	3607	18	8004	14	3239	19	71248	5
	medium timber	18611	9	5841	18	7573	18	11798	14	17992	10	5246	23	11642	14	10527	15	7084	16	3256	30	2309	30	5625	16	10825	12	4094	19	122423	4
	old timber	9491	14	3892	23	3412	26	12790	13	13601	12	2158	35	12375	15	8296	18	3427	27	1773	34	3236	27	4502	19	10189	13	5221	18	94364	5
	mixed	5057	15	251	60	1027	43	2123	29	2129	28	2613	27	7936	15	3553	23	3805	22	702	43	1535	26	6211	15	4833	16	6371	14	48147	5
	total	53137	4	15779	10	18044	10	36013	7	44291	5	16751	11	42335	6	31705	7	17785	10	8609	16	9871	14	23321	7	36933	6	26410	5	380985	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. 1354567. Birmensdorf, Swiss Federal Research Institute WSL

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