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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NFI3
number of stems
diameter at breast height (in 4 cm classes)
regional demarcation: forest district (2024)
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
	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
diameter at breast height (in 4 cm classes)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	919	20	953	21	682	28	1157	29	499	29	594	36	5370	10	2961	10	3301	12	2976	13	238	33	507	29	653	20	0	.	320	30	851	24	1277	19	1077	26	184	45	1368	20	2090	16	1544	18	3668	16	5562	10	4363	11	3602	12	1303	22	1314	22	1924	15	347	34	937	24	266	35	550	38	1035	30	542	30	775	24	1528	23	809	25	997	28	1541	19	836	27	1029	22	1080	23	763	25	628	29	489	36	633	24	1159	23	811	25	535	28	1134	23	655	27	1226	27	1019	31	1594	20	1093	20	1773	20	1377	23	2636	17	2794	14	1727	18	1876	18	539	30	525	38	648	25	819	24	841	28	1460	22	608	34	194	51	848	23	383	36	451	39	740	22	640	32	551	24	757	28	498	37	191	49	52	67	217	42	4436	12	2848	13	4465	11	630	28	439	37	398	28	719	27	494	27	476	31	323	39	478	27	0	.	114123	2
16-19 cm	769	22	730	23	698	26	756	21	422	30	483	27	3774	10	2051	12	2730	11	1892	13	191	53	265	31	486	23	0	.	275	28	616	21	945	21	815	23	108	41	971	20	1313	17	1390	17	2441	15	3716	10	3353	11	2294	12	705	21	944	21	1423	16	316	29	516	25	127	33	272	49	560	26	648	29	624	25	968	22	696	25	942	24	1030	21	589	25	808	27	854	27	478	29	594	23	390	31	528	22	669	20	776	22	420	27	702	22	367	32	918	26	493	27	1158	18	752	19	1252	21	981	21	2027	17	2131	13	1159	20	1423	18	533	27	445	43	501	27	520	24	553	26	711	23	383	33	200	49	633	24	245	52	157	42	651	22	486	32	474	29	409	32	356	43	164	41	84	59	211	51	2928	10	2391	13	3590	11	365	25	318	35	334	35	612	27	415	32	465	30	247	33	379	34	0	.	82488	2
20-23 cm	688	21	587	26	391	22	532	24	279	30	491	22	2760	10	1723	13	1901	12	1657	15	184	36	200	30	462	27	0	.	275	36	650	21	861	20	639	26	107	57	1100	26	1091	17	904	18	1368	17	2796	10	2540	11	1709	12	645	22	685	21	971	19	135	40	324	24	204	39	150	33	470	24	530	27	470	29	806	20	382	29	480	26	810	20	380	31	559	26	725	19	369	27	582	25	375	28	383	25	539	22	655	23	366	28	459	28	429	29	541	29	392	28	669	19	452	23	753	22	897	19	1486	15	1378	17	1040	20	1282	17	278	30	259	30	359	25	493	32	294	32	559	24	283	31	92	43	406	26	218	36	251	33	437	22	369	26	442	29	269	29	282	40	138	43	67	66	183	42	2243	12	2319	12	2470	13	364	27	336	41	231	36	500	33	336	24	398	29	207	33	418	31	0	.	63167	2
24-27 cm	497	20	255	30	399	25	523	22	331	28	500	26	2497	10	1395	12	1631	12	1538	14	99	37	174	34	481	23	0	.	239	38	478	31	689	18	393	26	149	43	760	23	1010	17	818	17	885	17	2216	11	1864	11	1325	14	455	22	580	21	923	18	157	42	396	25	118	39	64	47	152	33	411	28	389	29	604	21	385	25	509	22	678	21	387	31	737	19	592	21	276	28	262	30	223	31	509	22	404	23	472	24	246	32	466	30	259	30	255	39	188	34	730	19	336	29	520	22	519	21	985	17	947	15	619	21	783	17	356	35	202	37	265	30	589	26	236	32	396	32	351	31	66	52	588	23	68	42	233	37	359	27	295	29	226	28	254	27	210	32	133	43	131	61	90	40	1643	13	1330	13	1880	13	302	25	179	34	150	27	428	27	383	25	233	32	109	47	216	36	0	.	49584	2
28-31 cm	379	20	194	34	343	29	335	27	267	33	494	25	2395	12	1282	14	1252	13	1171	15	59	48	170	37	380	24	10	.	221	33	468	23	585	18	503	22	79	39	679	25	643	18	694	20	673	19	1813	11	1498	11	1341	13	349	24	394	25	777	20	143	41	242	31	181	41	174	38	277	25	308	29	276	25	530	24	390	26	302	27	711	17	377	28	642	19	451	22	283	32	362	25	169	29	429	23	378	23	318	27	116	36	371	27	202	30	264	32	171	43	414	21	284	25	388	26	381	22	647	18	672	20	277	26	516	21	274	34	191	33	219	32	256	30	217	33	409	30	189	35	19	71	435	23	156	49	147	39	359	23	200	29	299	26	264	29	105	35	130	44	43	61	162	43	1427	13	1013	14	1253	13	128	36	176	32	159	36	307	27	287	27	265	30	89	43	166	33	0	.	40436	2
32-35 cm	412	31	149	33	256	25	321	28	298	27	297	27	1745	11	1152	14	1161	12	1035	16	90	43	203	31	292	25	20	.	152	33	341	23	602	28	370	23	77	53	365	28	623	18	643	18	505	19	1521	11	1280	12	1092	14	334	24	307	23	466	20	59	47	232	31	139	36	93	41	271	25	252	34	178	37	333	28	329	26	325	28	441	21	245	24	488	23	406	21	177	25	302	25	147	33	350	23	274	26	136	38	195	33	291	26	145	32	249	28	115	37	257	29	198	35	278	24	349	24	414	22	426	20	282	25	443	21	138	30	100	49	196	31	161	31	167	30	338	24	94	37	30	74	292	29	120	39	88	50	254	25	231	28	265	29	173	31	117	39	102	45	32	57	139	54	963	14	931	16	1116	13	119	36	149	33	210	36	300	24	249	28	88	46	121	47	79	40	0	.	32294	2
36-39 cm	339	19	193	23	147	25	284	19	229	25	378	21	1426	9	899	11	701	12	706	13	59	39	145	29	246	24	8	.	108	30	327	22	416	18	264	21	48	40	380	18	515	14	375	16	399	17	1104	9	1064	10	1102	11	319	27	344	21	395	17	88	29	151	23	142	28	84	33	198	22	154	30	124	25	215	20	230	20	247	24	401	17	215	22	357	21	245	19	136	27	241	21	162	29	270	20	228	24	231	19	195	23	175	26	112	33	166	24	101	35	264	21	165	25	180	28	223	24	245	21	303	16	175	21	216	24	147	28	82	39	113	30	185	23	119	30	244	22	102	35	30	63	198	24	84	34	107	32	228	18	170	23	259	22	204	22	98	37	139	36	31	64	94	34	869	10	610	14	800	12	138	25	115	34	103	32	170	22	203	21	100	33	123	44	95	30	0	.	25521	2
40-43 cm	309	20	102	27	125	28	224	19	174	28	290	21	1229	9	802	11	784	11	651	13	74	37	93	32	210	21	0	.	114	29	210	24	291	20	251	23	23	70	491	20	542	16	337	16	343	18	949	10	980	10	806	11	227	21	237	20	389	16	90	29	112	26	127	30	66	33	212	20	93	27	114	26	276	22	268	24	219	29	276	19	172	27	326	20	272	20	151	27	170	24	119	26	296	19	188	23	190	26	155	27	165	25	103	36	124	26	27	38	218	22	200	25	80	29	142	23	150	26	243	18	103	23	169	21	63	38	73	34	132	28	170	26	120	28	234	21	83	42	13	58	161	23	131	33	97	30	235	18	107	27	177	24	179	27	99	34	75	37	45	49	118	29	752	12	395	14	593	13	133	39	87	32	149	25	202	22	179	25	90	32	115	31	59	37	0	.	21940	2
44-47 cm	237	20	154	27	83	28	213	20	151	25	241	22	1057	10	660	12	673	12	590	13	49	36	89	31	153	23	16	.	99	36	193	24	240	20	232	22	24	59	189	20	341	18	272	17	379	15	776	11	729	10	675	11	202	22	215	20	422	17	87	30	131	25	60	38	39	42	166	23	75	29	95	30	204	22	166	21	106	28	247	20	194	26	311	17	212	21	154	23	156	25	79	28	132	24	176	22	173	23	88	34	124	25	86	38	135	25	35	40	164	22	175	24	70	31	89	24	93	31	158	22	106	23	93	27	47	45	73	42	100	28	123	30	118	29	168	23	80	31	26	51	163	23	112	34	56	35	154	23	73	29	156	23	122	25	87	35	118	43	34	56	95	42	510	14	395	16	502	13	138	27	92	32	112	27	153	25	185	21	77	29	79	33	76	33	0	.	17954	2
48-51 cm	152	27	153	25	79	28	183	19	119	31	196	24	904	10	725	11	542	12	363	14	52	41	47	35	148	23	4	.	94	30	124	25	245	19	166	23	21	67	199	20	288	17	250	18	263	19	609	11	565	11	529	15	202	35	201	22	255	21	78	29	127	28	64	32	28	56	93	26	48	35	67	31	118	32	180	24	105	32	184	20	148	27	313	25	144	22	139	24	73	35	94	29	133	24	166	24	155	21	117	28	118	30	69	38	56	32	30	53	105	24	62	36	47	40	82	39	83	35	166	21	68	29	110	28	45	41	33	58	37	37	126	30	51	35	153	24	77	40	4	100	120	26	62	41	40	37	122	22	100	27	124	27	141	25	51	57	46	49	31	54	55	35	409	13	347	17	347	16	83	29	76	32	80	29	132	23	157	23	55	32	63	36	94	30	0	.	14513	2
52-55 cm	92	27	80	32	59	29	178	25	77	30	160	26	728	11	500	12	508	12	243	17	41	42	54	42	74	32	0	.	47	33	85	29	95	26	160	22	4	100	143	22	236	18	156	20	247	17	456	12	355	13	307	14	134	24	177	21	189	19	21	45	59	35	48	37	40	41	127	24	31	51	96	34	138	24	111	25	52	36	158	23	84	30	198	24	74	27	132	27	76	34	39	42	118	29	132	27	124	30	70	29	71	33	48	46	80	29	42	35	139	22	79	28	31	39	45	32	34	39	93	21	57	31	66	29	43	35	24	53	39	51	66	31	85	30	136	25	87	34	11	73	69	28	37	42	42	40	58	30	63	28	95	29	75	30	17	61	41	40	50	56	93	44	424	14	240	18	258	16	40	42	72	31	65	26	97	27	101	26	69	35	52	37	43	38	0	.	10889	2
56-59 cm	86	26	75	27	36	33	133	21	78	30	187	37	603	13	436	14	363	14	244	26	37	40	24	53	81	30	0	.	61	36	73	33	115	28	114	26	8	100	113	23	177	19	151	21	222	18	358	13	331	14	234	19	94	31	177	23	174	21	23	47	90	32	32	50	36	51	88	25	43	39	28	38	71	28	56	32	24	51	121	24	93	30	212	24	61	31	106	28	40	63	44	45	34	36	67	33	53	34	87	32	69	36	60	41	56	32	45	39	87	25	80	28	29	42	43	44	41	35	98	26	33	35	51	41	32	39	37	36	24	41	88	32	47	37	103	26	66	30	11	73	52	33	37	41	21	53	37	33	41	40	78	30	78	31	13	75	21	53	17	50	35	36	268	15	142	19	230	17	57	37	60	41	66	30	108	28	106	28	57	36	24	58	74	40	0	.	8913	3
60-63 cm	70	32	30	43	32	47	77	25	34	39	109	29	307	14	298	14	348	15	85	25	38	33	27	38	48	33	4	.	25	41	50	34	44	39	65	27	0	.	85	35	116	22	77	23	109	21	224	15	158	19	159	18	86	27	52	32	121	26	26	47	20	45	16	61	15	50	79	29	11	58	23	48	52	30	45	38	19	52	58	30	59	33	166	25	64	34	61	33	12	58	16	50	45	46	77	31	51	35	45	50	31	50	17	50	40	37	16	50	51	30	23	41	8	71	39	40	26	51	23	40	11	57	45	37	12	58	16	50	20	45	39	37	35	33	64	34	16	50	0	.	74	43	19	52	13	57	37	33	49	40	26	41	12	56	4	100	33	43	4	100	43	47	179	23	134	25	154	22	30	42	54	33	41	36	44	34	28	52	16	62	12	58	27	47	0	.	5399	3
64-67 cm	55	38	20	45	12	74	58	30	15	50	76	34	190	19	172	17	161	18	118	32	12	58	16	50	19	51	0	.	12	58	12	58	36	36	25	47	4	100	39	37	82	24	75	27	108	21	157	17	111	21	66	25	98	27	49	31	57	29	16	60	21	44	8	100	4	100	61	27	12	58	20	45	27	61	38	43	25	43	38	34	11	58	84	37	20	53	24	41	0	.	12	58	4	100	31	39	30	40	29	48	22	62	9	72	33	44	15	50	37	33	21	45	12	58	8	71	13	58	41	34	14	58	30	49	12	58	8	71	20	53	30	42	35	37	29	43	30	47	7	100	22	46	28	42	13	58	4	100	33	35	22	45	19	53	0	.	21	45	4	100	30	53	128	20	77	27	70	26	35	46	12	58	36	43	42	35	20	53	13	100	8	71	28	43	0	.	3458	4
68-71 cm	28	38	11	74	34	43	33	46	5	100	31	38	176	19	125	20	123	22	41	41	10	72	16	61	28	66	0	.	5	100	16	50	27	42	25	42	4	100	39	35	74	29	60	33	65	28	156	20	60	29	56	28	34	39	42	34	38	41	20	45	32	58	8	71	27	48	26	51	8	71	27	38	44	35	33	50	20	52	25	49	37	44	81	29	23	47	30	39	33	77	8	71	11	73	31	42	23	47	35	49	16	62	12	74	17	50	44	39	42	37	46	35	8	71	15	60	0	.	29	38	21	54	16	50	11	58	4	100	11	74	23	47	23	41	29	51	8	71	0	0	13	59	16	81	4	100	4	100	25	63	17	62	8	71	4	100	25	47	4	100	9	100	154	22	40	32	95	29	20	45	8	71	12	58	21	52	8	71	16	50	16	61	12	58	0	.	2849	4
72-75 cm	12	74	4	100	0	.	32	43	8	71	0	.	100	22	82	22	58	28	24	48	4	100	0	.	8	71	0	.	0	.	16	50	19	61	14	59	0	.	39	37	33	35	20	45	31	35	66	29	55	29	56	30	8	71	22	46	34	40	12	75	0	.	4	100	9	71	16	50	4	100	4	100	8	72	21	45	21	66	16	60	39	42	24	48	9	71	16	50	0	.	12	58	0	.	21	45	7	71	25	53	9	71	14	58	29	47	4	100	23	47	20	45	0	.	4	100	13	58	8	71	8	71	8	71	4	100	27	64	20	45	28	38	16	50	28	38	25	53	0	.	18	59	20	52	0	.	0	.	13	74	9	100	4	100	0	.	13	75	4	100	4	100	77	28	32	56	53	33	8	71	4	100	12	58	4	100	16	61	4	100	4	100	8	71	0	.	1608	6
76-79 cm	10	73	7	100	8	71	4	100	4	100	0	.	52	30	66	28	81	26	12	58	0	.	0	.	6	100	0	.	8	71	4	100	7	100	17	51	0	.	23	48	40	35	23	47	36	39	49	29	35	33	21	45	0	.	9	71	10	71	4	100	5	100	4	100	8	71	4	100	8	71	17	71	8	71	0	.	4	100	15	50	8	71	54	40	10	72	20	45	8	71	0	.	0	.	18	51	7	100	8	71	0	.	4	100	8	71	16	50	28	42	20	52	0	.	17	63	0	0	28	38	4	100	16	50	4	100	4	100	8	71	4	100	12	58	0	.	12	58	0	.	4	100	12	58	9	100	4	100	0	.	9	71	4	100	0	.	4	100	4	100	4	100	64	29	15	50	32	40	6	100	8	71	8	71	9	71	16	50	0	.	12	58	4	100	0	.	1158	6
80-83 cm	7	100	4	100	0	.	0	.	0	.	0	.	20	45	33	39	25	47	16	50	0	0	0	.	0	.	0	.	4	100	0	.	12	58	4	100	0	.	8	71	11	58	20	67	20	45	59	29	36	37	26	100	5	100	0	.	0	.	0	.	4	100	0	.	0	.	12	73	0	.	4	100	16	78	6	100	0	.	5	100	11	71	16	62	0	.	4	100	24	100	0	.	0	.	8	71	0	0	18	60	0	.	4	100	4	100	4	100	26	46	0	0	4	100	0	.	0	.	9	72	4	100	17	71	5	100	4	100	0	.	8	100	12	58	5	100	4	100	0	.	0	.	8	71	9	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	81	31	12	58	8	71	4	100	0	.	8	71	4	100	0	.	0	.	0	.	0	.	0	.	686	10
84-87 cm	0	.	0	.	4	100	4	100	4	100	6	100	14	59	25	48	13	58	4	100	0	.	0	.	0	.	0	.	0	.	4	100	4	100	4	100	0	.	4	100	5	100	12	58	15	61	32	35	20	53	4	100	0	.	7	100	6	100	4	100	0	.	4	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	4	100	4	100	17	61	0	.	6	100	0	.	0	.	0	.	0	.	4	100	9	71	0	.	0	.	4	100	0	.	20	54	17	50	0	.	4	100	8	71	13	58	0	.	4	100	0	.	4	100	4	100	9	71	8	71	4	100	0	.	0	.	0	.	4	100	4	100	4	100	0	.	4	100	0	.	0	.	0	.	4	100	0	.	16	50	13	58	24	41	0	.	4	100	0	.	0	.	6	100	7	71	4	100	0	.	0	.	440	10
88-91 cm	8	71	0	0	0	.	8	71	0	.	0	.	20	52	21	45	10	72	4	100	0	.	4	100	0	.	0	.	8	100	0	.	4	100	0	.	0	.	12	58	4	100	8	71	4	100	4	100	8	71	8	71	0	.	0	.	0	.	0	.	4	100	0	.	0	.	12	73	0	.	0	0	4	100	0	.	0	.	4	100	0	.	8	100	0	.	6	100	0	.	0	.	4	100	4	100	0	.	0	.	0	.	0	.	0	.	11	58	8	71	0	.	0	.	0	.	8	71	19	66	0	.	0	.	0	.	4	100	0	.	0	.	8	71	4	100	0	0	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	8	71	0	.	0	.	29	38	4	100	4	100	0	.	0	.	4	100	0	.	0	.	4	100	0	.	0	.	0	.	300	13
92-95 cm	0	.	4	100	0	.	4	100	0	.	0	.	16	77	8	71	15	58	0	0	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	4	100	11	58	9	71	12	58	4	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	0	12	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	13	57	4	100	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	4	100	0	.	0	.	4	100	0	.	0	.	4	100	0	.	4	100	0	.	171	17
96-99 cm	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	6	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	8	71	0	.	0	.	4	100	8	71	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	10	72	0	.	0	.	0	.	0	.	0	0	0	0	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	8	71	4	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	93	23
100-103 cm	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	4	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	71	4	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	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	29
104-107 cm	0	.	4	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	4	100	0	.	4	100	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	28
108-111 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	13	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	36
112-115 cm	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	41
116-119 cm	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	58
120-123 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	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100
124-127 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	.
128-131 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	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
132-135 cm	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	53
136-139 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
140-143 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	.
144-147 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	.
148-151 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	.	9	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	71
152-155 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	.
156-159 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	.	9	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	76
160-163 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	.
164-167 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	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100
168-171 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	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
172-175 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	.
176-179 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	.
180-183 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	.
184-187 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	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
188-191 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	.	19	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100
192-195 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	.
196-199 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	.
=200 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	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
total	5070	15	3708	17	3387	17	5058	15	2994	21	4533	17	25403	7	15426	8	16391	8	13374	10	1241	28	2039	23	3775	17	61	.	2067	23	4518	17	6513	15	5143	16	841	36	7032	16	9239	12	7838	12	11800	11	22665	7	19435	7	15419	8	5203	16	5757	15	8574	12	1627	23	3404	18	1551	25	1661	27	3860	18	3176	23	3337	21	5952	16	4150	17	4403	20	6765	14	3888	19	6454	16	5242	15	3329	18	3562	18	2377	21	3877	17	4570	16	4216	16	2772	19	4222	18	2604	21	4205	19	2783	22	6071	14	4027	15	5451	16	5236	15	8916	13	9613	11	5708	16	7193	14	2543	24	2115	26	2717	21	3737	19	3005	21	5074	18	2498	22	701	40	4098	18	1763	27	1741	25	3687	16	2900	20	3236	19	2972	19	1939	29	1404	29	642	37	1582	27	17614	8	13304	10	17957	9	2600	20	2188	24	2183	20	3853	19	3190	17	2438	22	1608	24	2261	22	0	.	498258	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. 2294258. Birmensdorf, Swiss Federal Research Institute WSL

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