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 (“±”).

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	23-25calculated 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).

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 (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).

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. “protection 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

NFI3
growing stock (stemwood)
higher/lower altitude zone · diameter at breast height (10 classes)
regional demarcation: forest district (2024)
unit: 1000 m³
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
higher/lower altitude zone	diameter at breast height (10 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	.
	12-20 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	.
	21-30 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	.
	31-40 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	.
	41-50 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	.
	51-60 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	.
	61-70 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	.
	71-80 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	.
	81-90 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	.
	91-100 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	.
	>100 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	.
	12-20 cm	353	21	305	23	236	26	294	20	42	54	56	30	417	28	464	16	970	12	396	19	77	45	113	32	187	22	0	.	64	31	180	28	165	31	271	26	63	42	93	42	68	35	77	29	15	100	407	22	6	82	846	13	225	24	395	21	174	24	40	55	23	77	42	44	43	34	37	39	102	45	32	95	111	52	184	27	181	35	119	32	158	34	123	40	411	22	179	27	106	40	146	29	134	30	138	31	2	.	28	66	323	24	227	28	474	26	14	71	143	33	50	52	437	21	366	20	663	17	501	16	513	23	491	19	44	60	8	70	-2	310	101	32	67	49	29	62	112	41	46	57	77	35	125	49	84	44	19	74	116	44	74	32	144	32	126	43	85	43	16	59	75	49	93	32	86	34	356	19	77	32	97	36	163	34	109	49	203	32	182	34	115	33	188	35	0	.	16514	3
	21-30 cm	634	18	396	24	504	24	656	22	61	50	347	30	647	19	1232	15	2382	12	1099	18	168	33	269	30	506	21	8	.	201	42	333	27	306	29	521	24	169	46	508	40	85	47	156	44	11	100	491	22	16	71	1552	11	538	22	724	21	581	28	45	48	16	100	161	38	56	46	92	40	199	44	64	72	310	30	631	22	348	28	337	26	427	33	341	29	865	20	238	29	313	28	322	28	615	22	242	30	11	.	80	53	590	24	419	26	375	29	19	100	268	27	40	70	413	23	561	21	856	17	627	20	634	23	632	18	110	73	59	71	51	60	298	38	92	48	19	62	326	32	62	46	282	31	202	36	205	34	30	86	185	34	233	31	299	29	174	35	195	39	16	57	192	33	126	35	148	37	471	21	121	41	247	32	232	28	431	26	523	21	333	30	222	36	363	31	0	.	32498	2
	31-40 cm	1080	22	504	22	665	22	845	20	180	53	446	31	1016	20	1725	13	2960	11	1199	16	270	29	503	25	737	21	29	.	216	33	540	28	507	29	797	24	155	43	455	28	143	40	313	29	0	.	477	22	3	100	2742	11	736	25	917	22	664	27	140	40	-6	100	336	34	108	39	214	44	267	41	24	100	200	35	982	21	453	32	446	27	411	29	598	26	998	18	413	27	440	25	374	27	1072	21	315	37	12	.	168	44	799	22	376	29	579	23	12	73	146	31	46	51	368	28	491	22	693	19	364	26	287	22	570	22	80	53	33	71	64	68	333	30	97	52	84	63	367	41	60	56	225	34	297	33	261	37	45	73	229	35	441	28	410	30	262	32	409	34	57	60	398	40	150	36	108	37	511	24	221	31	447	29	544	29	643	23	818	22	360	28	430	36	268	31	0	.	42169	2
	41-50 cm	1181	17	745	24	553	26	1145	17	186	56	666	29	1090	21	2467	13	3916	10	1575	16	269	32	343	28	852	22	33	.	416	33	527	26	566	27	995	24	117	60	661	35	119	56	299	31	4	100	169	32	20	79	2908	11	1003	26	1119	20	984	22	201	39	17	72	302	36	92	54	259	42	157	41	23	79	252	35	1185	20	454	33	509	29	654	34	623	28	1166	20	638	23	520	26	527	26	884	20	421	36	35	.	247	36	775	26	530	28	611	23	4	100	179	29	29	71	168	36	410	23	401	30	319	26	335	26	411	24	13	282	84	82	45	58	334	37	116	56	193	67	468	31	102	50	194	39	509	31	218	37	18	82	170	45	468	27	411	27	319	35	456	38	122	52	479	31	57	43	58	55	211	32	376	34	494	28	622	23	793	21	929	20	467	27	488	28	453	30	0	.	47929	2
	51-60 cm	830	19	698	23	463	24	1494	18	81	83	956	40	1371	23	2722	15	4221	11	732	21	358	35	374	38	637	28	0	.	423	36	439	28	356	31	764	25	30	85	468	33	95	44	200	30	13	100	222	37	19	100	1731	14	676	26	1264	21	722	27	102	37	0	.	240	39	89	53	251	36	52	62	11	100	142	58	717	22	100	44	573	32	337	32	728	25	610	22	871	25	353	37	410	29	608	26	248	48	0	.	277	43	604	32	468	35	496	26	0	.	276	36	13	70	137	39	287	32	195	35	309	23	220	33	253	30	70	71	36	77	0	.	156	42	170	67	78	70	481	33	51	68	136	49	292	31	215	41	13	100	173	46	250	36	239	34	99	61	300	32	81	63	490	37	85	57	0	.	221	29	219	39	646	28	646	24	720	25	812	23	538	35	318	34	546	33	0	.	40416	3
	61-70 cm	578	25	178	34	245	37	603	27	26	100	407	35	623	29	1355	17	2296	13	399	29	265	39	253	40	370	40	18	.	155	44	136	57	189	58	278	45	31	71	0	.	46	58	184	40	11	100	193	49	7	100	652	20	478	31	596	27	443	28	49	76	0	.	18	100	63	59	122	52	24	100	0	.	25	100	496	31	137	75	208	50	103	54	362	39	347	36	424	36	102	57	71	51	148	45	64	59	0	.	299	46	258	45	179	45	352	33	14	100	127	44	8	100	48	51	127	32	67	52	149	31	75	42	168	40	31	72	0	.	0	.	90	69	19	100	26	100	277	41	24	100	16	100	289	38	81	61	14	100	72	65	93	45	71	59	19	100	322	36	20	100	348	43	9	100	18	100	81	45	153	43	305	40	296	36	346	32	209	40	144	49	117	41	302	36	0	.	19442	4
	71-80 cm	163	51	70	58	90	51	263	41	0	.	0	.	220	41	588	25	1117	19	72	59	25	100	0	.	108	52	0	.	59	71	125	55	51	71	166	65	0	.	28	100	22	100	78	58	0	.	71	53	0	.	317	35	27	100	180	43	112	50	19	100	0	.	61	59	49	100	81	58	29	100	0	.	0	.	145	41	41	71	45	72	74	58	143	59	119	45	321	34	29	100	86	61	0	.	0	.	0	.	225	46	61	72	133	53	284	46	0	.	75	47	12	100	0	.	86	46	29	59	112	40	46	71	65	47	17	100	24	100	9	100	63	58	0	.	16	100	233	51	0	.	24	100	230	46	54	100	0	.	0	.	19	100	28	100	0	.	150	62	0	.	43	71	0	.	0	.	51	60	43	100	97	58	209	47	81	58	208	42	48	71	119	53	93	51	0	.	8184	6
	81-90 cm	81	77	27	100	28	100	66	71	0	.	35	100	33	100	431	33	221	38	30	100	0	0	36	100	0	.	0	.	70	71	34	100	25	100	62	100	0	.	0	.	0	.	31	100	0	.	20	100	0	.	34	100	0	.	32	100	0	.	0	.	0	.	36	100	0	.	0	.	0	.	0	.	0	.	32	100	0	.	0	.	48	100	49	71	0	.	80	100	113	100	0	.	38	100	0	.	0	.	75	73	0	.	0	.	26	100	0	.	109	48	15	100	11	100	15	100	37	73	94	62	32	100	15	100	0	.	0	.	0	.	0	.	0	.	0	.	30	100	0	.	0	.	57	73	92	74	0	.	0	.	30	100	0	.	0	.	28	100	0	.	0	.	0	.	0	.	42	60	0	.	22	100	56	71	25	100	47	100	76	61	27	100	0	.	0	.	2656	12
	91-100 cm	24	100	33	100	0	.	75	71	0	.	0	.	0	.	83	71	245	46	38	100	50	100	0	.	0	.	0	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	60	0	.	39	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	167	46	0	.	0	.	0	0	16	100	0	0	0	0	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	81	72	0	.	0	.	38	100	0	.	0	.	33	100	0	.	27	100	0	.	1325	18
	>100 cm	0	.	31	100	0	.	0	.	0	.	0	.	0	.	37	100	0	.	0	.	0	.	47	100	0	.	0	.	0	.	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	0	.	0	.	0	.	55	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	71	0	.	432	100	143	100	52	78	333	57	0	.	95	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1359	38
	total	4924	15	2987	17	2785	18	5441	14	576	49	2913	26	5416	17	11104	11	18328	8	5541	14	1481	25	1938	23	3398	19	87	.	1640	27	2313	23	2163	23	3894	19	565	39	2212	31	577	32	1339	24	55	75	2117	18	72	73	10821	9	3683	19	5225	16	3679	21	596	30	50	71	1197	30	500	39	1056	31	831	33	154	68	1040	30	4423	17	1714	26	2238	22	2211	24	3116	23	4516	14	3165	20	1977	23	1936	21	3498	17	1429	28	61	.	1398	34	3411	19	2403	23	3196	18	63	81	1530	23	213	51	2014	25	2487	17	3010	13	2809	15	2142	17	2738	15	366	52	245	68	167	57	1375	28	560	43	444	61	2294	27	346	38	954	29	2001	25	1210	30	139	71	980	29	1607	24	1602	22	999	28	1979	29	311	52	2025	28	520	29	418	32	2025	17	1209	29	2353	22	2806	20	3148	18	3749	17	2182	22	1837	26	2241	24	0	.	212492	2
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	.
	12-20 cm	0	.	0	.	0	.	0	.	88	42	124	39	923	11	339	15	2	100	277	20	0	.	0	.	6	76	0	.	30	54	128	34	181	29	64	41	0	.	260	22	337	19	319	18	680	16	933	12	877	11	93	35	96	36	28	53	334	19	14	75	181	22	11	63	67	57	188	29	98	34	171	23	232	32	0	.	126	34	115	29	30	57	163	32	0	.	23	59	75	38	0	.	11	83	121	38	242	22	124	31	0	.	0	.	0	.	160	29	149	25	202	21	11	72	0	.	0	.	157	24	28	42	49	52	90	35	119	44	128	31	75	36	101	31	263	22	17	56	0	.	127	34	0	.	21	67	182	22	55	36	84	43	27	41	18	81	0	.	13	70	0	.	798	12	748	14	756	14	94	40	16	.	0	.	32	82	0	.	0	.	0	.	0	.	0	.	12633	3
	21-30 cm	0	.	0	.	0	.	0	.	318	31	399	31	2158	10	809	16	16	77	740	18	0	.	0	.	3	100	0	.	134	55	276	34	467	21	222	44	0	.	665	33	848	16	672	19	787	16	1769	11	1983	10	336	27	96	41	89	52	666	21	114	55	347	26	56	63	79	45	286	27	266	33	359	31	404	24	0	.	182	34	469	24	87	37	540	25	0	.	58	52	156	40	0	.	23	100	254	32	535	24	152	35	0	.	0	.	0	.	173	32	305	26	277	24	39	49	0	.	0	.	250	27	55	51	38	60	281	35	175	35	305	27	240	31	164	35	495	27	30	60	0	.	319	28	0	.	58	59	460	22	127	31	169	42	59	63	80	60	0	.	44	69	0	.	1491	12	1318	13	1452	15	172	38	7	.	0	.	87	74	0	.	0	.	0	.	0	.	0	.	26488	3
	31-40 cm	0	.	0	.	0	.	0	.	606	28	673	27	3160	11	1387	16	10	73	1107	17	0	.	0	.	20	86	0	.	130	50	445	28	956	28	200	32	0	.	677	29	1460	16	1021	19	1133	16	2947	11	2774	11	655	29	180	39	108	43	747	19	85	45	555	23	104	61	154	39	483	24	293	36	419	26	556	24	0	.	258	32	680	25	242	31	560	27	0	.	116	50	258	36	0	.	56	71	407	25	627	23	415	28	0	.	0	.	0	.	170	31	380	30	408	27	42	42	0	.	0	.	304	26	88	51	68	61	310	33	255	42	410	32	202	36	300	42	725	22	21	76	0	.	384	27	0	.	47	53	694	20	134	42	322	33	130	41	40	89	0	.	41	73	0	.	1956	11	1623	14	1785	13	158	41	0	.	0	.	74	69	0	.	0	.	0	.	0	.	0	.	37734	3
	41-50 cm	0	.	0	.	0	.	0	.	517	28	734	27	4199	10	1709	15	27	100	1162	17	0	.	0	.	38	100	0	.	124	54	360	38	708	23	290	35	0	.	780	22	1614	18	1062	17	1643	14	3278	10	3177	9	643	24	196	37	184	56	1015	21	160	40	628	22	132	52	115	41	552	25	138	36	368	25	616	26	10	.	248	34	674	25	257	31	1044	24	0	.	137	46	94	45	0	.	44	72	443	26	927	22	381	28	0	.	0	.	0	.	116	39	413	23	519	27	50	50	0	.	0	.	311	26	65	48	37	94	141	39	191	46	341	30	305	41	312	31	752	22	45	66	0	.	441	25	0	.	74	56	675	19	287	31	304	31	211	38	131	82	0	.	49	88	0	.	1889	11	1720	14	1787	13	274	38	0	.	0	.	88	83	0	.	0	.	0	.	0	.	0	.	41956	2
	51-60 cm	0	.	0	.	0	.	0	.	505	28	607	28	3847	13	1821	16	24	71	834	32	0	.	0	.	48	75	0	.	54	60	272	37	557	25	302	37	0	.	608	22	1548	16	964	19	1644	15	2690	11	2316	11	515	28	192	44	166	53	907	24	123	40	613	24	79	72	180	48	735	23	209	42	377	29	624	23	0	.	169	38	580	27	495	33	1061	23	0	.	129	59	52	67	0	.	11	100	462	25	694	27	277	39	0	.	0	.	0	.	263	27	386	24	393	26	34	62	0	.	0	.	193	29	66	60	97	64	162	39	181	41	235	33	418	34	375	33	865	22	71	58	0	.	407	32	0	.	51	59	419	22	205	32	384	31	302	40	12	100	0	.	193	52	0	.	2014	12	1174	16	1443	16	237	46	0	.	0	.	95	58	0	.	0	.	0	.	0	.	0	.	37971	3
	61-70 cm	0	.	0	.	0	.	0	.	120	42	443	36	1694	15	962	18	0	.	442	33	0	.	0	.	0	.	0	.	18	100	115	45	148	37	175	44	0	.	372	30	904	20	532	27	913	19	1461	15	794	18	257	39	420	40	38	72	277	29	51	58	237	32	57	100	116	60	472	25	99	42	274	31	412	36	27	.	84	53	183	37	146	35	766	27	0	.	36	100	15	100	0	.	0	.	404	30	360	29	181	53	0	.	0	.	0	.	214	39	239	31	258	30	25	71	0	.	0	.	92	43	12	100	67	84	111	64	56	50	192	52	216	38	262	33	409	36	12	100	0	.	230	45	0	.	37	71	49	58	333	42	144	48	51	59	0	.	0	.	21	100	0	.	1363	18	687	21	692	24	218	51	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	20010	4
	71-80 cm	0	.	0	.	0	.	0	.	72	77	0	.	784	21	688	23	0	.	209	42	0	.	0	.	0	.	0	.	0	.	0	.	155	54	37	73	0	.	401	34	450	28	376	40	524	23	803	23	491	23	175	40	108	60	60	100	149	49	131	63	120	71	0	.	89	59	121	45	29	100	153	56	107	48	0	.	93	69	192	46	250	49	429	32	0	.	0	.	23	100	0	.	0	.	224	36	97	62	128	53	0	.	0	.	0	.	175	36	174	48	167	41	0	0	0	.	0	.	67	50	35	76	68	59	56	59	148	64	184	46	119	48	179	37	142	42	18	100	0	.	117	45	0	.	0	.	50	71	62	72	67	72	22	100	0	.	0	.	65	100	0	.	874	19	194	40	492	28	60	71	0	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	11228	6
	81-90 cm	0	.	0	.	0	.	0	.	29	100	0	.	293	36	114	51	0	.	120	59	0	.	0	.	0	.	0	.	0	.	0	.	50	72	0	.	0	.	126	42	121	50	151	55	158	46	431	26	239	37	211	88	0	.	0	.	28	100	24	100	67	71	0	.	0	.	175	68	0	.	35	100	107	65	0	.	0	.	31	100	65	72	186	88	0	.	0	.	0	.	0	.	0	.	103	58	27	100	37	100	0	.	0	.	0	.	37	71	79	63	76	58	0	.	0	.	0	.	63	73	0	.	27	100	0	.	43	100	25	100	71	64	142	56	76	58	0	0	0	.	0	.	0	.	0	.	34	100	0	.	33	100	0	.	0	.	0	.	39	100	0	.	498	25	159	38	91	51	31	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4452	10
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	155	54	42	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	82	59	30	100	18	100	117	51	99	58	84	51	40	100	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	30	100	17	100	0	0	41	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100	0	.	0	.	0	.	37	76	22	100	22	100	0	.	0	.	0	.	42	100	0	.	0	.	0	.	26	100	0	.	0	.	63	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	59	69	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1212	16
	>100 cm	0	.	0	.	0	.	0	.	0	0	0	.	132	62	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	101	60	38	100	42	100	31	100	172	47	65	59	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	71	100	32	100	0	.	0	.	0	.	0	.	38	100	0	.	78	100	0	.	0	.	32	100	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	72	16	100	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1080	20
	total	0	.	0	.	0	.	0	.	2254	24	2981	22	17347	9	7926	12	80	72	4891	14	0	.	0	.	114	81	0	.	489	45	1595	26	3222	19	1289	29	0	.	4072	19	7350	12	5156	14	7631	11	14582	8	12802	8	2962	22	1314	33	673	49	4123	18	702	35	2750	18	439	51	801	34	3011	20	1133	28	2156	22	3089	20	37	.	1191	27	2969	19	1571	27	4789	20	0	.	500	38	674	34	0	.	145	71	2418	21	3509	18	1769	25	0	.	0	.	0	.	1418	20	2180	18	2321	18	201	41	0	.	0	.	1517	19	348	39	528	49	1151	27	1193	30	1852	25	1645	26	1919	23	3726	18	214	55	0	.	2025	22	0	.	287	50	2563	16	1203	27	1507	26	802	30	281	71	0	.	466	56	0	.	10997	9	7708	11	8525	10	1243	32	23	.	0	.	415	53	0	.	0	.	0	.	0	.	0	.	194766	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	.
	12-20 cm	353	21	305	23	236	26	294	20	129	33	180	28	1341	11	803	11	972	12	673	14	77	45	113	32	192	21	0	.	94	28	308	22	345	21	334	22	63	42	353	19	404	17	396	16	695	16	1340	11	883	11	939	12	321	20	422	20	508	15	54	45	204	21	53	38	111	37	225	25	200	29	202	25	343	27	184	27	307	25	233	22	188	30	286	25	411	22	202	24	181	28	146	29	145	28	259	24	244	22	153	28	323	24	227	28	474	26	174	27	293	20	252	20	448	20	366	20	663	17	658	14	541	22	540	18	134	31	126	41	126	32	176	24	168	27	291	21	129	37	46	57	205	25	125	49	105	38	201	21	171	32	159	27	171	28	144	39	85	43	29	45	75	49	891	12	834	13	1112	11	170	26	112	34	163	34	141	42	203	32	182	34	115	33	188	35	0	.	29147	2
	21-30 cm	634	18	396	24	504	24	656	22	379	27	746	21	2805	9	2041	11	2399	12	1839	13	168	33	269	30	509	21	8	.	335	33	609	22	773	17	742	22	169	46	1172	25	933	15	828	18	799	16	2260	10	1999	10	1888	10	633	20	813	20	1247	17	159	42	364	25	217	32	135	33	378	22	466	27	423	28	713	19	631	22	530	22	806	18	514	28	880	19	865	20	297	26	468	23	322	28	638	22	496	22	546	24	232	29	590	24	419	26	375	29	193	30	572	19	317	23	452	21	561	21	856	17	877	16	690	22	670	17	391	32	234	32	356	25	537	25	255	28	514	27	356	30	62	46	601	21	202	36	263	29	490	21	313	24	402	25	358	26	255	30	195	39	60	53	192	33	1616	11	1466	12	1924	12	292	28	254	32	232	28	518	25	523	21	333	30	222	36	363	31	0	.	58986	2
	31-40 cm	1080	22	504	22	665	22	845	20	786	24	1119	20	4176	10	3111	10	2970	11	2306	11	270	29	503	25	757	21	29	.	346	28	984	20	1463	21	997	20	155	43	1132	21	1603	15	1334	16	1133	16	3424	9	2777	11	3397	11	916	22	1025	20	1410	16	225	30	549	23	440	30	262	28	697	21	560	27	444	25	756	20	982	21	711	23	1126	18	653	22	1157	19	998	18	529	24	699	20	374	27	1128	20	722	22	639	23	583	24	799	22	376	29	579	23	182	29	526	23	454	25	410	25	491	22	693	19	669	18	374	21	638	21	390	28	288	38	473	29	535	23	397	34	809	21	387	39	60	56	608	21	297	33	308	32	739	19	363	27	762	21	540	24	303	30	409	34	97	46	398	40	2106	11	1731	13	2296	12	379	25	447	29	544	29	717	22	818	22	360	28	430	36	268	31	0	.	79903	2
	41-50 cm	1181	17	745	24	553	26	1145	17	703	25	1401	20	5289	9	4176	10	3943	10	2737	11	269	32	343	28	890	21	33	.	541	28	887	22	1274	17	1285	20	117	60	1441	20	1733	17	1361	15	1648	14	3447	10	3198	9	3551	10	1199	23	1303	19	1998	15	361	28	645	22	434	30	207	33	811	21	295	28	391	24	868	21	1195	20	701	25	1183	19	911	26	1667	18	1166	20	775	21	615	23	527	26	928	19	864	22	963	21	628	22	775	26	530	28	611	23	120	38	592	18	548	25	218	30	410	23	401	30	630	18	399	23	448	23	154	43	276	41	386	27	639	28	428	27	945	22	513	29	102	50	635	21	509	31	292	31	693	18	457	26	772	20	622	22	449	34	456	38	171	45	479	31	1946	11	1778	13	1998	12	649	25	494	28	622	23	881	20	929	20	467	27	488	28	453	30	0	.	89885	2
	51-60 cm	830	19	698	23	463	24	1494	18	586	27	1563	27	5218	11	4544	11	4246	11	1565	19	358	35	374	38	685	26	0	.	476	32	711	22	913	19	1066	21	30	85	1076	19	1643	15	1165	17	1657	15	2912	10	2335	11	2246	13	868	22	1429	20	1629	18	225	27	613	24	319	34	269	37	986	19	261	36	388	28	766	22	717	22	269	29	1154	21	831	23	1789	17	610	22	1000	23	406	33	410	29	619	25	710	23	694	27	554	29	604	32	468	35	496	26	263	27	662	20	406	25	171	33	287	32	195	35	502	18	286	29	350	28	232	35	218	36	235	33	573	28	546	31	943	21	552	29	51	68	543	27	292	31	266	35	432	22	378	28	633	23	542	26	111	56	300	32	274	41	490	37	2099	12	1174	16	1664	14	456	30	646	28	646	24	815	23	812	23	538	35	318	34	546	33	0	.	78387	2
	61-70 cm	578	25	178	34	245	37	603	27	145	39	850	25	2317	13	2318	12	2296	13	842	22	265	39	253	40	370	40	18	.	172	40	250	37	337	36	453	32	31	71	372	30	950	19	717	22	924	19	1653	15	802	18	910	18	898	25	633	26	720	21	100	47	237	32	75	80	179	44	594	22	124	39	274	31	436	34	523	30	221	51	391	32	248	30	1128	22	347	36	460	34	116	52	71	51	148	45	469	27	360	29	480	35	258	45	179	45	352	33	228	37	366	25	266	30	73	41	127	32	67	52	242	25	87	39	235	37	141	52	56	50	192	52	307	34	280	32	435	34	289	39	24	100	246	43	289	38	119	47	64	50	405	36	237	34	122	43	19	100	322	36	41	71	348	43	1372	18	705	21	773	22	372	35	305	40	296	36	360	31	209	40	144	49	117	41	302	36	0	.	39452	3
	71-80 cm	163	51	70	58	90	51	263	41	72	77	0	.	1003	18	1276	17	1117	19	281	35	25	100	0	.	108	52	0	.	59	71	125	55	206	44	202	55	0	.	428	33	472	27	454	34	524	23	874	22	491	23	492	27	135	52	240	40	261	35	150	57	120	71	61	59	138	52	201	36	58	71	153	56	107	48	145	41	134	52	238	40	324	40	572	28	119	45	321	34	53	71	86	61	0	.	224	36	97	62	352	35	61	72	133	53	284	46	175	36	249	36	179	38	0	0	86	46	29	59	180	31	81	52	134	38	74	51	172	57	193	44	183	37	179	37	158	39	252	48	0	.	141	41	230	46	54	100	50	71	62	72	86	60	50	71	0	.	150	62	65	100	43	71	874	19	194	40	543	26	103	59	97	58	209	47	106	50	208	42	48	71	119	53	93	51	0	.	19412	4
	81-90 cm	81	77	27	100	28	100	66	71	29	100	35	100	326	34	545	28	221	38	150	52	0	0	36	100	0	.	0	.	70	71	34	100	75	59	62	100	0	.	126	42	121	50	182	49	158	46	452	26	239	37	245	77	0	.	32	100	28	100	24	100	67	71	36	100	0	.	175	68	0	.	35	100	107	65	32	100	0	.	31	100	113	59	235	71	0	.	80	100	113	100	0	.	38	100	103	58	27	100	111	59	0	.	0	.	26	100	37	71	188	38	90	52	11	100	15	100	37	73	157	47	32	100	42	74	0	.	43	100	25	100	71	64	142	56	76	58	30	100	0	.	0	.	57	73	92	74	34	100	0	.	63	71	0	.	0	.	28	100	39	100	0	.	498	25	159	38	134	40	31	100	22	100	56	71	25	100	47	100	76	61	27	100	0	.	0	.	7108	8
	91-100 cm	24	100	33	100	0	.	75	71	0	.	0	.	155	54	126	58	245	46	38	100	50	100	0	.	0	.	0	.	36	100	0	.	0	.	0	0	0	.	82	59	30	100	18	100	117	51	165	42	84	51	78	71	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	30	100	17	100	0	0	135	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100	0	.	72	100	0	.	37	76	189	42	22	100	0	.	0	0	16	100	42	100	0	0	37	100	0	.	26	100	0	.	0	.	63	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	0	.	35	100	0	.	0	.	69	59	69	58	81	72	0	.	0	.	38	100	0	.	0	.	33	100	0	.	27	100	0	.	2538	12
	>100 cm	0	.	31	100	0	.	0	.	0	0	0	.	132	62	90	72	0	.	0	.	0	.	47	100	0	.	0	.	0	.	0	.	0	.	41	100	0	.	101	60	38	100	42	100	31	100	172	47	65	59	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	0	.	27	100	0	.	55	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	71	100	74	59	0	.	432	100	143	100	52	78	371	52	0	.	173	55	0	.	0	.	32	100	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	72	16	100	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2439	23
	total	4924	15	2987	17	2785	18	5441	14	2830	22	5895	17	22763	8	19029	8	18408	8	10431	10	1481	25	1938	23	3512	18	87	.	2129	23	3909	17	5386	15	5184	16	565	39	6284	16	7927	11	6496	12	7686	11	16699	7	12873	8	13783	8	4997	17	5898	15	7803	13	1298	23	2800	18	1636	25	1301	26	4067	17	1964	21	2310	21	4129	17	4460	17	2905	19	5206	14	3782	18	7906	15	4516	14	3666	18	2651	19	1936	21	3643	16	3847	17	3570	18	3167	20	3411	19	2403	23	3196	18	1481	20	3710	14	2534	17	2215	23	2487	17	3010	13	4326	12	2490	16	3267	15	1517	24	1438	28	2019	23	3020	19	2479	20	4171	17	2508	25	346	38	2979	17	2001	25	1498	26	2702	15	2184	20	3114	17	2405	18	1280	27	1979	29	777	39	2025	28	11517	8	8126	10	10550	9	2452	22	2375	22	2806	20	3563	17	3749	17	2182	22	1837	26	2241	24	0	.	407258	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. 2298139. Birmensdorf, Swiss Federal Research Institute WSL

back