Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
growing stock (stemwood)
higher/lower altitude zone · diameter at breast height (main diameter classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
higher/lower altitude zone	diameter at breast height (main diameter classes)	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	±%	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	±%	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	±%	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	±%	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	±%	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	±%	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
lower altitude zone	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	90	33	134	31	100	42	102	28	15	59	22	41	165	28	200	24	359	13	155	23	22	40	63	30	66	21	0	.	34	93	55	41	40	35	147	39	16	44	57	53	28	39	37	41	5	.	128	38	1	100	310	18	84	26	141	23	59	24	46	37	7	83	16	71	13	60	14	43	17	38	8	63	44	44	54	23	34	42	46	36	49	34	35	41	106	23	63	27	40	38	93	43	42	31	60	38	3	.	10	46	66	28	123	35	103	42	5	90	54	37	11	41	148	27	108	23	223	17	191	18	154	22	146	20	16	56	2	71	13	72	46	40	35	45	16	54	34	32	22	44	29	50	45	44	42	42	11	84	32	38	15	77	55	32	17	45	18	52	0	.	34	45	47	37	47	32	98	21	28	35	61	40	15	182	100	42	72	26	56	53	33	56	53	39	0	.	5859	4
	16-23 cm	329	21	296	22	225	28	338	28	24	53	105	37	343	21	605	16	1246	12	391	19	56	38	125	28	230	23	0	.	104	28	180	28	202	34	364	33	45	46	115	34	54	52	123	31	20	.	275	22	13	90	913	13	271	24	356	23	221	23	81	36	21	72	42	37	46	38	27	39	103	43	71	61	145	32	286	25	156	37	148	32	220	35	114	33	377	20	166	30	113	35	138	36	192	21	183	32	4	.	36	52	254	22	194	28	416	31	7	84	256	25	45	47	453	21	464	21	633	17	603	17	491	20	450	20	56	62	14	67	35	59	106	32	53	47	76	52	142	36	85	43	98	34	96	40	74	40	15	72	123	42	98	29	128	27	114	49	85	49	8	71	63	39	106	33	94	33	365	19	106	36	111	32	171	29	219	28	229	28	181	34	145	28	210	33	0	.	17913	3
	24-35 cm	737	19	569	24	733	25	754	24	43	51	390	31	1017	19	1567	14	2628	11	1461	18	196	35	335	27	686	21	1	.	231	31	578	27	560	27	734	27	134	48	357	34	110	42	186	34	18	.	716	21	15	72	2171	12	590	22	933	22	719	26	99	44	11	100	248	37	106	38	95	34	235	50	100	74	360	34	703	23	313	37	290	28	469	32	474	31	1133	17	373	29	327	28	288	27	773	23	328	32	0	.	157	59	933	22	491	26	584	24	12	100	291	32	72	55	589	20	728	19	1122	16	725	20	724	21	864	18	148	66	62	67	79	64	232	38	141	56	73	67	445	34	109	55	354	34	203	39	276	38	78	83	258	34	271	34	459	28	183	34	183	46	93	67	280	33	107	42	174	35	623	22	125	38	346	37	318	26	597	23	799	20	422	27	298	33	358	27	0	.	41280	2
	36-51 cm	1746	16	730	22	945	21	1633	16	338	50	896	28	1907	19	3223	13	4998	9	2103	16	431	32	776	25	1151	19	60	.	456	39	913	26	820	25	1372	22	251	42	835	30	243	38	361	32	0	.	599	27	38	88	4719	10	1458	24	1564	17	1293	22	288	34	7	100	508	35	223	39	362	37	306	37	150	63	405	35	1440	20	576	35	698	25	799	28	1120	24	1893	16	655	22	1064	24	768	24	1459	17	438	31	52	.	363	41	1096	24	760	27	1029	21	9	100	285	24	94	50	415	23	741	19	955	18	692	18	571	23	791	20	132	56	110	71	98	53	533	34	105	53	182	56	442	30	245	40	408	32	579	29	346	32	46	87	325	34	812	25	699	25	405	34	550	33	158	52	341	32	152	37	82	38	534	22	491	31	850	27	787	23	1028	21	1436	19	665	26	736	31	615	25	0	.	70760	2
	=52 cm	1639	18	1223	23	999	22	2591	18	154	66	1218	35	2180	21	5170	14	7046	10	1592	21	682	33	809	32	1170	27	40	.	540	36	823	32	855	32	1424	26	94	62	517	33	146	46	538	27	12	.	660	30	27	75	2929	13	1509	25	1879	21	1285	26	242	41	0	.	439	38	364	44	399	38	145	52	8	100	233	53	1451	24	289	41	810	36	789	29	1350	28	1367	21	1627	24	433	33	744	27	903	27	525	47	27	.	776	42	943	28	807	37	1232	29	16	100	798	31	27	70	479	48	758	35	504	25	1017	22	478	31	776	23	111	71	42	76	20	100	381	44	161	84	151	66	638	41	119	72	202	46	815	32	240	53	32	100	317	44	470	31	646	33	158	39	684	31	101	56	845	33	112	53	41	100	462	28	541	36	1300	27	1195	25	1257	25	1121	25	753	36	654	32	1127	26	0	.	75205	3
	total	4543	14	2952	17	3002	17	5418	14	575	48	2631	28	5612	17	10765	11	16277	8	5702	14	1387	26	2107	22	3303	18	101	.	1366	30	2549	23	2476	23	4041	19	539	39	1882	28	581	34	1245	25	54	.	2379	20	93	73	11041	9	3912	18	4873	15	3577	21	756	29	46	71	1253	31	752	34	897	33	806	34	337	56	1187	31	3933	18	1369	29	1991	24	2326	23	3091	24	4876	14	2885	19	1976	21	2031	20	3369	17	1534	28	86	.	1342	34	3292	20	2375	23	3364	18	50	96	1685	23	248	46	2085	19	2799	17	3437	12	3229	14	2419	17	3028	15	463	53	229	61	246	53	1298	30	494	44	499	47	1701	27	580	38	1091	28	1738	27	978	28	181	71	1055	29	1666	23	1987	23	878	29	1520	28	360	55	1563	27	523	30	439	30	2083	17	1291	29	2668	22	2487	20	3200	18	3657	17	2077	23	1866	24	2363	21	0	.	211017	1
higher altitude zone	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	16	90	20	54	386	12	112	21	1	100	107	20	0	.	0	.	2	100	0	.	2	72	27	31	71	23	12	43	0	.	80	19	164	18	80	20	319	16	396	20	404	16	44	29	36	39	4	56	88	22	-2	343	56	24	5	66	14	44	55	37	31	34	71	28	89	23	1	.	28	39	61	27	31	54	44	31	0	.	17	53	30	42	0	.	4	100	97	39	64	44	32	53	0	.	0	.	0	.	95	35	66	29	74	27	3	47	0	.	0	.	41	27	18	62	25	69	24	33	44	46	46	26	24	32	44	32	123	27	9	74	0	.	45	39	0	.	1	71	73	24	18	42	27	44	10	38	16	65	0	.	3	81	0	.	301	14	236	18	263	18	28	45	8	.	0	.	5	57	0	.	0	.	0	.	0	.	0	.	4772	4
	16-23 cm	0	.	0	.	0	.	0	.	129	36	170	28	1050	10	401	17	7	75	442	18	0	.	0	.	10	100	0	.	30	72	153	39	269	25	77	50	0	.	259	26	440	18	395	19	930	17	995	11	1178	10	132	29	111	33	35	56	249	20	14	134	178	26	12	79	49	41	266	36	141	34	220	23	275	30	0	.	96	42	266	23	53	42	149	28	0	.	23	72	92	44	0	.	14	79	218	26	261	23	189	30	0	.	0	.	0	.	170	31	197	23	200	23	33	58	0	.	0	.	189	23	44	45	56	51	123	34	95	39	182	26	97	40	129	34	280	24	23	55	0	.	148	36	0	.	14	77	250	25	68	36	90	47	33	44	32	64	0	.	9	71	0	.	1039	11	876	14	881	15	75	44	29	.	0	.	36	78	0	.	0	.	0	.	0	.	0	.	15375	3
	24-35 cm	0	.	0	.	0	.	0	.	468	27	534	29	3096	10	1149	15	16	71	1000	19	0	.	0	.	34	89	0	.	198	57	423	37	823	23	298	40	0	.	682	28	1438	16	998	17	1438	14	2652	11	2950	9	513	27	174	35	123	50	672	21	161	57	555	26	84	66	107	48	483	24	436	32	524	26	725	22	0	.	349	31	749	23	188	34	613	23	0	.	107	48	190	42	0	.	56	100	430	26	702	22	332	28	0	.	0	.	0	.	228	30	497	26	510	24	71	44	0	.	0	.	344	27	80	47	104	57	448	30	247	36	421	30	261	29	300	29	743	26	45	54	0	.	645	35	0	.	44	71	615	23	195	39	198	42	101	49	56	59	0	.	40	60	0	.	2230	12	1925	13	1965	13	239	37	25	.	0	.	72	82	0	.	0	.	0	.	0	.	0	.	39121	2
	36-51 cm	0	.	0	.	0	.	0	.	901	25	1325	26	6247	9	2527	14	12	73	2133	17	0	.	0	.	38	86	0	.	259	49	718	29	1365	22	380	33	0	.	1393	19	2860	14	1911	15	2477	14	5106	9	5156	9	1092	25	354	36	254	51	1191	20	205	42	898	20	196	53	247	40	1103	22	393	31	600	24	1004	22	0	.	583	29	1147	21	389	27	1303	22	0	.	199	40	285	37	0	.	61	72	830	23	1130	23	726	25	0	.	0	.	0	.	328	30	694	22	947	21	88	43	0	.	0	.	493	24	108	40	101	47	467	28	372	41	648	29	465	37	572	29	1247	21	84	62	0	.	713	25	0	.	75	87	1253	19	419	32	577	33	345	30	151	100	0	.	53	61	0	.	3466	10	2597	12	3072	12	304	34	0	.	0	.	98	81	0	.	0	.	0	.	0	.	0	.	68734	2
	=52 cm	0	.	0	.	0	.	0	.	742	34	1361	26	7119	11	3693	15	27	100	1522	21	0	.	0	.	54	78	0	.	96	63	395	38	1282	22	516	42	0	.	1993	20	3481	16	2442	20	3750	13	5730	11	4824	15	981	25	629	47	279	65	1402	25	423	41	1059	25	99	87	361	44	1582	24	417	37	777	29	1611	22	0	.	472	36	1202	24	1069	30	2631	22	0	.	139	59	67	53	0	.	25	100	1427	26	1142	22	774	33	0	.	0	.	0	.	816	26	1041	23	1038	22	78	62	0	.	0	.	620	24	128	49	247	62	391	37	486	42	690	39	740	30	1189	26	1707	23	161	58	0	.	926	33	0	.	141	49	666	23	725	33	443	31	378	41	25	100	0	.	330	64	0	.	5274	11	2545	14	3068	15	381	37	0	.	0	.	205	56	0	.	0	.	0	.	0	.	0	.	82035	3
	total	0	.	0	.	0	.	0	.	2257	24	3409	22	17898	8	7882	12	63	76	5204	14	0	.	0	.	138	78	0	.	585	45	1716	27	3808	18	1283	31	0	.	4406	16	8383	12	5826	14	8915	10	14879	8	14512	8	2760	20	1305	31	695	53	3602	18	801	36	2747	18	395	51	779	36	3490	20	1417	27	2193	21	3704	18	1	.	1528	27	3424	19	1731	27	4740	19	0	.	486	38	664	33	0	.	159	72	3002	20	3299	18	2053	23	0	.	0	.	0	.	1638	20	2496	18	2768	18	274	34	0	.	0	.	1687	18	378	37	532	44	1453	26	1244	30	1987	24	1587	27	2234	22	4100	19	323	52	0	.	2478	24	0	.	276	55	2857	16	1425	28	1334	27	867	30	281	82	0	.	436	61	0	.	12309	8	8179	10	9250	10	1026	30	62	.	0	.	417	52	0	.	0	.	0	.	0	.	0	.	210037	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	90	33	134	31	100	42	102	28	32	55	42	33	551	12	312	17	360	13	262	16	22	40	63	30	69	21	0	.	36	88	82	30	111	20	159	36	16	44	137	25	192	16	116	19	323	16	525	18	405	16	354	16	120	21	144	22	147	16	44	43	63	23	21	58	27	37	69	31	47	26	78	26	133	21	55	23	62	29	107	22	80	30	79	25	106	23	80	24	70	28	93	43	46	30	157	28	67	42	42	42	66	28	123	35	103	42	100	34	121	23	84	24	151	26	108	23	223	17	232	15	173	21	171	20	40	30	46	44	59	25	70	28	78	26	139	24	43	30	22	44	75	31	45	44	43	41	83	24	50	28	42	40	65	27	34	39	18	52	3	81	34	45	348	13	284	16	362	15	56	28	69	37	15	182	105	40	72	26	56	53	33	56	53	39	0	.	10631	3
	16-23 cm	329	21	296	22	225	28	338	28	153	31	275	22	1392	9	1006	12	1253	12	833	13	56	38	125	28	240	23	0	.	134	27	333	24	471	20	441	28	45	46	374	21	494	17	517	16	951	16	1270	10	1191	10	1044	12	382	20	391	22	470	15	95	37	199	24	54	33	95	28	294	33	244	27	291	23	420	23	286	25	252	28	413	18	274	29	262	21	377	20	190	28	206	27	138	36	206	21	401	20	265	22	224	27	254	22	194	28	416	31	177	30	453	17	245	20	486	20	464	21	633	17	792	14	536	18	506	19	179	30	109	35	217	23	203	25	183	28	356	22	166	32	85	43	246	26	96	40	88	36	266	24	191	30	187	27	161	24	146	41	85	49	17	50	63	39	1145	11	970	13	1246	12	181	28	140	33	171	29	255	26	229	28	181	34	145	28	210	33	0	.	33289	2
	24-35 cm	737	19	569	24	733	25	754	24	511	25	924	21	4114	9	2716	10	2644	11	2461	13	196	35	335	27	719	21	1	.	429	31	1001	22	1383	17	1032	23	134	48	1039	22	1549	15	1185	15	1456	14	3368	9	2965	9	2684	11	764	19	1056	21	1391	17	260	39	567	26	332	32	213	31	578	21	671	27	624	25	1085	19	703	23	662	24	1039	18	657	25	1087	19	1133	17	481	25	516	23	288	27	829	23	758	20	702	22	489	27	933	22	491	26	584	24	240	29	788	20	581	22	661	18	728	19	1122	16	1069	16	804	19	968	17	596	28	309	32	501	27	493	24	441	27	816	24	490	31	109	55	998	25	203	39	320	34	693	22	453	25	469	26	560	24	239	29	183	46	133	50	280	33	2336	11	2099	12	2588	11	364	28	371	35	318	26	669	22	799	20	422	27	298	33	358	27	0	.	80400	2
	36-51 cm	1746	16	730	22	945	21	1633	16	1240	23	2221	19	8154	8	5750	9	5010	9	4236	11	431	32	776	25	1189	19	60	.	715	30	1631	19	2184	16	1752	19	251	42	2228	16	3103	13	2272	13	2477	14	5704	9	5194	9	5810	9	1812	21	1818	17	2484	15	493	26	905	20	703	29	471	28	1466	19	699	24	750	23	1409	18	1440	20	1159	23	1845	16	1188	21	2422	16	1893	16	854	20	1348	20	768	24	1520	17	1268	19	1182	23	1089	21	1096	24	760	27	1029	21	338	29	979	17	1041	20	503	20	741	19	955	18	1185	14	680	20	892	19	599	25	482	36	746	26	998	25	677	25	1429	20	525	27	245	40	1121	20	579	29	421	31	1299	18	743	23	1389	20	1044	20	556	36	550	33	211	42	341	32	3617	10	2679	12	3606	11	795	23	850	27	787	23	1126	20	1436	19	665	26	736	31	615	25	0	.	139493	1
	=52 cm	1639	18	1223	23	999	22	2591	18	896	30	2579	22	9299	9	8863	10	7073	10	3114	15	682	33	809	32	1224	26	40	.	637	32	1218	25	2136	18	1941	22	94	62	2510	17	3627	15	2980	17	3761	13	6390	10	4852	15	3909	12	2138	22	2158	20	2687	18	665	30	1059	25	538	35	725	31	1980	21	562	31	786	29	1843	20	1451	24	762	27	2012	20	1858	21	3981	17	1367	21	1766	23	500	29	744	27	928	26	1952	23	1169	21	1551	27	943	28	807	37	1232	29	832	25	1839	19	1065	22	558	42	758	35	504	25	1638	16	605	27	1023	23	502	33	527	39	710	38	1121	25	1350	25	1859	22	799	34	119	72	1128	28	815	32	382	38	698	23	1043	26	912	22	1025	25	183	37	684	31	431	50	845	33	5386	11	2586	14	3531	14	922	26	1300	27	1195	25	1462	23	1121	25	753	36	654	32	1127	26	0	.	157240	2
	total	4543	14	2952	17	3002	17	5418	14	2832	22	6041	17	23510	7	18647	8	16340	8	10906	10	1387	26	2107	22	3441	18	101	.	1951	25	4265	17	6285	14	5324	16	539	39	6288	14	8964	11	7070	12	8969	10	17257	7	14606	8	13802	8	5216	16	5567	15	7180	14	1558	23	2793	18	1648	26	1531	25	4387	17	2223	21	2530	20	4890	16	3934	18	2897	19	5416	15	4057	18	7831	14	4876	14	3370	17	2641	18	2031	20	3528	16	4536	16	3385	18	3395	19	3292	20	2375	23	3364	18	1687	20	4181	14	3016	17	2359	17	2799	17	3437	12	4916	11	2797	16	3560	15	1916	23	1474	27	2233	22	2885	20	2729	19	4599	17	2024	24	580	38	3569	19	1738	27	1254	25	3039	16	2480	20	3000	17	2855	18	1159	29	1520	28	796	42	1563	27	12832	8	8618	10	11333	9	2317	21	2730	21	2487	20	3617	17	3657	17	2077	23	1866	24	2363	21	0	.	421054	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. 2293404. Birmensdorf, Swiss Federal Research Institute WSL

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