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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NFI3
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 2004/07
		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	212	48	750	31	529	66	7	100	94	64	76	91	1668	26
	fir	127	51	161	67	74	76	0	.	0	.	0	0	363	38
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	7	100	0	0	0	0	7	100	30	71	43	53
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	292	47	298	67	59	100	0	.	7	100	71	100	726	36
	maple	0	.	32	61	29	72	0	.	0	.	0	.	61	47
	ash	41	85	33	71	0	.	0	.	0	.	0	.	74	57
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	54	74	0	.	69	59	0	.	0	.	0	.	122	46
	indeterminable	0	.	0	0	0	.	0	.	0	0	0	0	0	0
	total	726	35	1280	29	760	55	7	100	108	57	177	71	3058	20
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	826	28	569	37	318	45	162	44	553	36	73	100	2500	16
	fir	337	30	267	48	84	52	0	0	35	100	0	.	723	24
	pine	103	42	0	.	0	.	381	100	58	100	0	.	541	72
	larch	20	72	0	.	7	100	240	44	51	56	33	72	350	32
	Arolla pine	0	.	0	.	0	.	67	78	17	100	0	.	84	66
	other conifers	0	.	0	.	0	.	65	100	0	.	0	.	65	100
	beech	1357	18	79	51	106	47	22	100	0	.	196	87	1760	17
	maple	310	30	22	100	57	52	7	100	0	.	18	100	413	25
	ash	252	37	78	71	35	71	0	.	0	.	0	.	365	31
	oak	86	38	0	.	0	.	106	100	0	.	18	100	210	54
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	291	60	291	60
	other broadleaves	454	29	93	56	109	69	35	71	83	72	99	84	872	22
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	3743	15	1107	27	715	31	1084	43	796	30	729	40	8173	11
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16306	13	4846	23	4122	25	2738	40	4994	24	617	53	33623	9
	fir	3003	23	717	47	708	45	112	80	152	59	233	67	4926	17
	pine	919	37	121	100	0	.	1696	40	1363	39	0	.	4100	23
	larch	418	39	376	45	124	100	1377	32	545	44	1367	37	4208	18
	Arolla pine	0	.	0	.	0	.	141	100	113	86	0	.	253	67
	other conifers	510	29	17	100	0	.	35	100	328	56	16	100	906	26
	beech	4829	18	809	54	627	46	492	69	37	100	3466	30	10259	15
	maple	3046	18	848	34	633	36	216	40	159	66	459	38	5360	13
	ash	2251	21	411	40	1000	36	317	56	104	58	629	39	4713	14
	oak	1398	37	186	66	0	.	495	50	0	.	1134	47	3213	25
	sweet chestnut	0	.	0	.	0	.	25	71	0	.	5226	25	5252	25
	other broadleaves	2083	23	939	34	1502	34	2623	26	2716	34	6418	20	16281	12
	indeterminable	0	0	0	0	0	.	0	0	0	0	0	0	0	0
	total	34763	8	9270	17	8716	19	10267	18	10511	17	19566	12	93094	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	9663	14	5787	23	3277	30	2463	28	2451	33	1315	37	24956	10
	fir	3666	19	781	35	904	33	359	62	151	80	90	82	5952	14
	pine	1024	30	36	100	0	.	1351	36	981	48	0	.	3393	22
	larch	192	50	0	.	131	78	1567	26	410	55	506	41	2806	19
	Arolla pine	0	.	0	.	0	.	254	67	58	100	0	.	311	58
	other conifers	190	41	0	.	40	71	17	100	0	.	0	.	247	34
	beech	10718	11	1792	35	3143	25	496	63	185	67	2731	30	19066	9
	maple	1375	20	883	32	1067	30	64	77	96	78	94	54	3580	14
	ash	2153	20	957	46	1331	42	120	60	175	77	879	45	5615	17
	oak	1547	34	61	58	21	100	92	54	0	.	632	36	2353	24
	sweet chestnut	0	.	0	.	0	.	55	100	0	.	2949	27	3004	27
	other broadleaves	2230	20	262	42	1157	40	584	44	419	43	3824	22	8476	13
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0
	total	32760	8	10560	18	11070	19	7423	18	4926	24	13020	14	79759	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	11275	10	7695	14	5202	17	3982	21	8135	15	2377	24	38666	6
	fir	7689	12	2381	19	1279	24	348	52	99	55	332	43	12128	9
	pine	1333	18	186	59	238	64	288	44	1089	36	50	100	3185	16
	larch	183	35	87	86	14	100	663	31	880	29	1470	32	3297	18
	Arolla pine	0	.	0	.	0	.	60	71	308	48	0	.	368	42
	other conifers	282	40	0	.	18	100	17	100	0	0	0	.	317	37
	beech	15557	8	2638	19	3502	21	252	76	291	44	1092	37	23333	7
	maple	2787	12	1126	21	1138	28	280	59	15	100	48	100	5395	10
	ash	2565	17	654	31	842	31	15	100	0	.	113	51	4189	13
	oak	1217	26	21	100	43	100	8	100	0	.	144	45	1433	23
	sweet chestnut	0	.	7	100	0	.	0	.	0	.	759	36	766	35
	other broadleaves	2046	16	1130	28	434	34	468	46	58	58	757	33	4893	12
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	44935	6	15927	11	12710	13	6382	17	10875	14	7142	15	97971	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8484	9	7987	11	8014	13	2727	22	7136	14	2658	20	37005	5
	fir	7256	10	5523	13	2595	18	251	67	241	43	458	52	16325	7
	pine	714	19	114	100	140	84	177	87	211	40	42	100	1398	20
	larch	84	42	92	52	63	45	1643	24	1193	22	506	26	3582	14
	Arolla pine	0	.	0	.	0	.	227	44	310	41	0	.	537	30
	other conifers	161	44	7	100	36	71	0	.	0	.	0	.	204	37
	beech	10808	8	5519	15	3776	15	37	100	548	35	731	39	21419	6
	maple	2002	16	598	25	796	21	238	60	138	49	16	100	3788	11
	ash	2053	15	382	44	372	32	49	73	68	84	0	.	2924	13
	oak	1349	18	0	.	38	53	15	100	28	100	26	76	1456	17
	sweet chestnut	66	100	0	.	0	.	0	.	0	.	596	41	662	38
	other broadleaves	2288	20	550	32	480	29	173	48	241	56	360	41	4091	14
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	.	0	0
	total	35266	6	20771	9	16310	10	5537	16	10113	11	5394	15	93392	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3925	14	11345	12	3456	23	4863	18	7015	15	2807	23	33411	7
	fir	4381	16	3456	19	809	35	318	51	184	49	322	63	9470	11
	pine	598	35	570	78	62	77	1336	30	848	34	190	74	3603	20
	larch	24	77	23	100	0	.	2064	27	1060	21	1684	25	4856	15
	Arolla pine	0	.	10	100	0	.	740	38	1574	28	32	100	2356	22
	other conifers	520	43	17	100	133	64	0	.	190	65	0	.	860	31
	beech	4195	15	3492	23	924	34	850	47	692	47	3178	23	13332	11
	maple	1173	19	1762	31	372	36	137	65	126	48	115	67	3685	17
	ash	830	33	357	42	508	41	381	84	487	72	139	50	2701	22
	oak	697	35	144	51	44	71	104	74	55	100	372	49	1417	23
	sweet chestnut	0	.	0	.	0	.	8	100	28	100	2226	27	2262	26
	other broadleaves	1160	23	1431	24	365	36	672	36	331	33	1943	21	5903	11
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	0	0	0
	total	17503	10	22607	10	6673	18	11473	14	12591	12	13010	12	83857	5
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	50691	5	38979	6	24917	8	16943	11	30377	7	9924	11	171831	3
	fir	26459	6	13286	8	6454	12	1389	27	862	25	1436	26	49886	4
	pine	4690	12	1028	47	440	45	5229	19	4551	19	282	55	16220	9
	larch	922	22	585	33	339	48	7555	12	4146	13	5597	15	19143	7
	Arolla pine	0	.	10	100	0	.	1488	25	2380	20	32	100	3910	16
	other conifers	1663	19	42	61	226	42	134	58	518	43	16	100	2600	15
	beech	47756	4	14626	10	12138	10	2149	30	1760	24	11465	13	89894	3
	maple	10694	7	5271	14	4091	13	942	27	535	29	750	27	22282	5
	ash	10145	8	2871	20	4087	18	882	43	834	46	1761	27	20580	7
	oak	6294	14	411	36	147	41	821	34	84	74	2326	26	10082	11
	sweet chestnut	66	100	7	100	0	.	89	66	28	100	12047	13	12238	13
	other broadleaves	10315	8	4405	14	4116	18	4554	18	3848	25	13402	11	40639	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	169695	2	81522	4	56955	6	42174	7	49921	6	59038	4	459304	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. 1283732. Birmensdorf, Swiss Federal Research Institute WSL
https://doi.org/10.21258/1902546

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