Result query system

back

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 (“±”).

top

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).

top

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).

top

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.

top

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. “protective forest [2022]”) – is assigned to all measurement cycles.

top

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

top

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.

top

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.

top

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.

top

NFI5
growing stock (stemwood)
development stage · main tree species
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	117	76	240	70	79	87	0	0	481	43	68	71	985	30
	fir	13	100	133	79	0	.	10	100	0	0	0	.	155	68
	pine	90	55	0	.	0	.	8	100	41	80	0	.	138	43
	larch	0	.	0	.	0	.	37	73	79	58	46	100	162	43
	Arolla pine	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	113	62	39	100	21	91	0	.	0	.	0	.	174	47
	maple	4	100	17	100	33	71	0	.	0	.	0	.	54	54
	ash	10	100	0	.	0	.	0	.	0	.	0	.	10	100
	oak	67	80	0	.	0	.	0	.	0	.	0	0	67	80
	sweet chestnut	0	.	6	100	0	.	0	.	0	.	0	0	6	100
	other broadleaves	34	100	0	.	0	.	0	.	0	.	0	0	34	100
	indeterminable	0	.	0	0	0	.	0	.	0	.	0	.	0	0
	total	448	41	435	56	134	59	55	55	600	38	114	58	1786	22
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	848	28	1306	24	302	31	79	58	1463	32	481	47	4478	15
	fir	352	33	238	43	137	51	0	0	0	0	0	.	727	23
	pine	119	48	0	0	8	100	0	.	76	73	4	100	207	39
	larch	46	69	127	78	1	100	64	67	173	53	171	63	581	31
	Arolla pine	0	.	0	.	0	.	17	100	27	100	0	.	43	72
	other conifers	19	68	2	100	5	100	0	.	3	81	0	.	28	50
	beech	683	31	577	61	203	36	0	.	19	100	18	95	1500	28
	maple	146	41	79	47	62	48	11	88	17	88	0	.	315	25
	ash	56	80	35	100	38	75	2	100	0	.	0	.	131	48
	oak	157	54	0	.	10	100	0	.	0	.	0	.	168	51
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	143	55	83	48	27	55	15	50	53	79	61	86	382	29
	indeterminable	0	.	0	0	0	.	0	.	0	0	0	.	0	0
	total	2568	18	2448	22	793	25	187	36	1830	28	742	43	8568	11
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4433	14	2485	19	1749	24	612	29	1631	22	491	37	11400	9
	fir	2229	19	879	31	790	36	75	71	47	81	130	56	4150	14
	pine	235	38	50	100	65	85	191	44	361	30	0	.	902	20
	larch	346	35	135	44	0	.	719	30	420	38	681	30	2301	16
	Arolla pine	0	.	0	.	0	.	37	79	146	43	0	.	184	38
	other conifers	93	48	134	66	5	100	13	60	29	44	0	.	274	37
	beech	2680	12	571	39	416	42	129	75	23	89	983	25	4802	11
	maple	1113	18	393	24	320	30	190	37	2	100	166	39	2185	12
	ash	745	19	193	36	251	39	220	46	39	74	208	42	1656	14
	oak	329	30	28	84	0	.	94	44	0	.	224	35	676	20
	sweet chestnut	0	.	0	.	0	.	26	100	0	.	959	28	985	28
	other broadleaves	869	26	371	24	276	37	620	30	580	29	1387	20	4103	11
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	13072	8	5239	14	3872	19	2926	16	3279	16	5228	12	33617	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6513	13	3606	21	2021	26	1811	30	2942	23	517	36	17410	9
	fir	2196	18	885	33	549	40	8	100	157	71	213	68	4007	14
	pine	593	28	45	92	0	.	751	36	411	52	80	85	1880	21
	larch	174	42	52	100	152	74	1094	33	644	31	510	40	2628	18
	Arolla pine	0	.	0	.	0	.	134	76	292	47	8	100	434	39
	other conifers	359	33	0	.	8	68	1	100	2	100	0	.	369	32
	beech	4406	14	681	35	1091	36	68	61	69	50	2082	24	8397	11
	maple	1051	18	612	30	611	32	0	.	80	56	142	53	2497	14
	ash	772	23	589	50	316	36	46	52	41	76	391	59	2155	20
	oak	715	33	62	67	0	0	29	60	0	.	403	38	1209	23
	sweet chestnut	0	.	0	.	0	.	42	78	5	100	1091	26	1137	25
	other broadleaves	840	22	166	35	136	42	176	47	197	61	1482	23	2997	14
	indeterminable	0	0	0	0	0	0	0	.	0	.	0	0	0	0
	total	17619	9	6699	15	4886	19	4160	19	4838	18	6919	13	45120	5
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8250	12	6027	18	6632	18	2361	30	6023	17	1940	29	31232	7
	fir	5597	14	1550	26	1704	21	275	56	99	90	292	53	9516	10
	pine	1382	23	102	100	58	86	330	43	915	36	32	100	2819	17
	larch	161	41	0	0	0	.	785	37	1011	27	1321	33	3278	18
	Arolla pine	0	.	0	.	0	.	19	100	227	49	0	.	246	46
	other conifers	306	55	0	.	7	79	0	.	0	.	0	.	312	54
	beech	11030	9	2260	25	2434	19	198	70	201	63	988	34	17110	8
	maple	1635	14	327	28	649	25	5	100	18	72	23	59	2656	11
	ash	2503	18	358	54	667	35	62	92	0	.	116	89	3708	15
	oak	1411	24	107	66	12	100	26	59	0	.	151	54	1707	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	644	44	644	44
	other broadleaves	1073	21	98	30	143	35	25	45	17	49	748	39	2104	18
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	33347	7	10829	14	12306	14	4085	22	8510	14	6255	16	75332	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14921	9	17390	10	14775	12	6198	16	11247	13	5307	18	69838	5
	fir	15755	9	8813	13	5329	18	740	44	790	36	1103	39	32531	6
	pine	1260	22	231	100	207	63	26	77	478	36	0	.	2202	19
	larch	367	37	113	71	278	44	3821	21	3305	19	1673	24	9558	11
	Arolla pine	0	.	0	.	0	.	253	53	329	45	0	.	581	34
	other conifers	1080	48	0	.	20	60	0	.	0	.	0	.	1100	47
	beech	18986	7	5415	16	4160	17	1	100	661	43	1090	33	30312	6
	maple	1797	15	474	24	996	22	76	56	50	67	0	.	3393	11
	ash	2983	16	441	38	862	31	12	63	45	95	0	.	4342	13
	oak	2309	23	11	100	20	100	34	100	64	100	34	60	2471	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	1410	33	1410	33
	other broadleaves	1288	22	489	61	313	33	74	67	126	73	209	41	2499	18
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	.	0	0
	total	60746	5	33376	8	26961	10	11235	14	17093	11	10826	12	160237	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7255	11	13056	10	5331	15	4610	15	7669	13	4170	30	42090	6
	fir	6212	13	5328	14	1706	26	554	44	317	39	359	40	14477	8
	pine	788	28	380	54	170	49	641	29	513	34	53	59	2545	16
	larch	145	57	40	70	39	96	3489	20	1966	18	2519	16	8198	11
	Arolla pine	0	.	12	100	0	.	632	26	774	27	0	100	1418	19
	other conifers	71	41	10	83	-13	160	0	.	14	59	83	98	165	54
	beech	5434	12	2985	17	2278	20	537	41	348	48	1690	22	13271	8
	maple	1453	16	1189	18	537	31	207	46	266	44	162	44	3814	10
	ash	1434	25	446	42	434	40	152	60	152	62	205	33	2823	16
	oak	1048	23	66	68	147	53	96	76	87	100	217	44	1661	18
	sweet chestnut	0	.	0	.	0	.	97	76	18	100	1330	26	1445	24
	other broadleaves	1250	20	485	28	434	30	310	27	140	38	1222	24	3842	12
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	25091	7	23996	8	11063	13	11325	11	12264	10	12011	14	95749	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	42337	5	44111	5	30888	7	15670	9	31455	7	12974	12	177433	2
	fir	32355	5	17825	8	10215	11	1662	26	1410	24	2097	24	65564	4
	pine	4468	11	808	41	508	34	1947	19	2794	17	168	48	10693	8
	larch	1240	18	468	33	470	36	10010	11	7597	10	6921	11	26705	6
	Arolla pine	0	.	12	100	0	.	1091	21	1794	18	9	94	2906	13
	other conifers	1927	29	146	61	32	81	13	58	47	32	83	98	2249	25
	beech	43331	4	12529	9	10603	10	932	30	1320	27	6850	11	75566	3
	maple	7198	7	3091	10	3209	12	490	26	432	30	494	25	14914	5
	ash	8503	9	2062	21	2569	16	493	30	277	41	921	30	14824	7
	oak	6038	12	274	35	189	43	279	33	151	72	1028	20	7958	10
	sweet chestnut	0	.	6	100	0	.	165	51	23	82	5442	13	5635	12
	other broadleaves	5497	9	1692	20	1330	16	1219	18	1113	21	5109	11	15961	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	152892	2	83022	4	60013	6	33972	6	48414	5	42096	4	420409	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. 2308251. Birmensdorf, Swiss Federal Research Institute WSL
https://doi.org/10.21258/1951324