Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
growing stock (stemwood)
higher/lower altitude zone · diameter at breast height (10 classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
higher/lower altitude zone	diameter at breast height (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	305	23	276	24	242	32	274	27	22	73	65	37	367	21	502	15	1054	12	385	19	61	38	122	31	173	21	0	.	89	42	136	31	155	33	337	29	45	49	109	42	62	39	109	29	21	.	320	25	8	85	871	14	227	27	363	23	193	22	93	34	22	75	49	34	46	42	29	39	70	43	20	66	127	37	225	24	131	41	133	33	152	29	75	34	294	21	166	25	82	42	176	36	150	21	157	34	7	.	35	50	229	21	247	28	317	34	8	81	221	27	34	46	439	21	341	18	607	17	531	16	439	20	418	19	41	61	4	77	33	61	116	33	75	46	60	52	123	35	66	42	102	34	106	32	80	37	19	78	102	41	75	32	131	25	82	48	64	45	4	100	63	39	111	38	77	33	310	20	66	35	120	29	110	40	230	29	194	28	118	41	94	34	143	32	0	.	15882	3
	21-30 cm	519	18	514	25	430	21	559	26	50	48	251	33	654	20	1065	16	1998	11	942	18	115	33	263	26	446	22	1	.	141	28	406	27	416	32	530	28	93	42	265	32	110	43	147	39	21	.	517	21	12	71	1380	11	422	23	594	21	408	26	88	40	11	78	139	36	62	41	42	40	150	46	145	62	259	35	521	22	209	39	197	31	385	35	284	34	810	19	222	31	261	28	191	30	475	24	252	33	0	.	76	64	524	24	344	31	570	25	10	100	248	28	59	53	476	21	634	21	876	16	711	18	749	21	695	18	128	66	52	65	56	58	211	36	84	44	79	64	341	31	95	49	206	33	142	34	195	38	56	85	240	37	176	33	272	30	167	40	165	42	33	71	160	39	115	37	167	34	569	21	151	32	243	43	235	26	386	25	443	22	321	30	222	28	338	27	0	.	29988	2
	31-40 cm	861	18	444	23	701	24	814	22	162	53	415	30	1063	20	1626	13	2486	11	1294	17	189	42	395	26	685	24	13	.	267	34	535	27	463	26	793	22	141	45	428	34	102	49	221	32	0	.	516	25	13	74	2597	11	643	21	936	23	671	27	93	39	6	100	259	36	122	44	157	37	282	40	44	67	321	34	798	23	388	33	355	27	443	29	543	30	1063	16	431	27	448	26	402	25	826	20	294	34	14	.	151	63	807	23	404	25	569	24	6	100	216	32	78	60	436	26	630	20	922	16	508	19	369	21	668	22	102	59	29	73	68	61	219	31	69	60	63	53	293	36	112	49	307	34	242	36	251	33	51	73	198	29	412	29	500	28	175	32	169	37	112	59	261	33	103	39	112	38	409	26	127	39	446	26	364	29	609	22	832	21	452	26	393	33	293	31	0	.	40303	2
	41-50 cm	1155	16	446	25	594	22	1137	17	187	51	605	31	1195	22	2024	14	3390	10	1365	17	304	32	488	27	746	21	22	.	297	42	572	28	477	26	865	25	156	46	513	30	140	43	203	33	0	.	340	34	33	86	3006	11	906	21	1019	19	956	22	219	37	7	100	351	37	146	40	222	36	149	42	100	73	233	39	817	22	352	38	434	27	499	30	804	26	1252	19	399	26	744	26	465	25	862	18	279	34	38	.	290	41	710	25	507	28	610	24	9	100	189	28	50	59	224	26	396	20	498	22	396	22	373	22	438	23	75	59	103	70	52	55	371	37	95	51	108	58	280	32	188	41	248	33	391	32	201	34	23	77	167	36	432	30	409	28	262	36	368	33	85	68	211	35	64	40	43	49	332	26	333	34	480	29	525	24	660	21	960	20	382	27	422	31	423	26	0	.	44893	2
	51-60 cm	748	21	573	25	540	24	1118	21	84	68	663	36	1438	21	2873	14	3395	12	1138	20	343	35	388	36	707	24	40	.	276	39	461	27	553	28	764	24	65	71	417	34	60	47	221	34	0	.	300	33	19	100	1906	12	899	26	1047	21	702	27	168	39	0	.	331	42	107	46	218	40	120	46	20	100	183	47	878	24	122	42	532	33	467	29	679	27	753	22	706	26	338	35	492	35	717	23	326	41	27	.	194	46	675	29	398	37	525	24	0	.	278	40	6	100	143	32	273	26	196	34	368	23	260	34	373	24	70	72	42	76	16	100	185	41	135	82	137	63	298	37	61	54	142	44	352	38	161	45	14	100	232	45	418	32	457	32	143	43	246	43	126	51	345	31	118	53	41	100	159	38	344	40	658	29	454	24	677	26	740	21	472	36	410	33	539	27	0	.	40802	3
	61-70 cm	573	24	376	36	238	36	802	21	34	100	505	39	563	32	1562	18	2165	14	280	35	281	38	217	42	328	36	0	.	142	46	225	39	314	46	367	33	20	100	115	48	47	71	168	36	0	.	136	47	9	100	683	20	646	29	773	25	382	32	54	100	0	.	36	100	50	58	119	47	36	71	8	100	64	74	365	28	65	78	267	49	162	46	371	37	524	31	492	29	74	51	224	37	228	42	180	59	0	.	245	40	213	43	151	43	272	40	16	100	91	43	7	100	97	51	183	44	182	40	176	33	67	52	135	36	32	73	0	.	0	.	118	56	16	100	0	.	114	45	58	100	62	59	205	42	61	100	18	100	77	75	103	58	119	50	49	58	293	33	0	.	381	45	11	100	0	.	111	36	160	53	477	33	390	33	410	30	308	33	146	47	103	54	459	35	0	.	21383	4
	71-80 cm	245	38	215	41	142	54	469	35	35	100	77	58	283	52	716	23	1115	20	230	53	62	71	96	50	177	49	25	.	80	74	172	45	21	100	275	41	19	100	0	.	35	71	142	44	12	.	116	58	0	.	439	28	169	41	141	45	231	43	42	71	0	.	50	71	92	62	75	58	0	.	0	.	0	.	258	43	76	60	74	58	179	49	175	54	119	45	299	35	0	.	55	73	74	78	46	72	0	.	245	59	100	51	159	53	106	61	0	.	108	54	5	100	41	100	86	42	44	51	162	36	131	56	119	37	16	100	0	.	20	100	78	58	21	100	52	73	184	60	0	.	23	100	189	51	30	100	0	.	0	.	49	72	60	72	0	.	117	56	0	.	117	69	0	.	0	.	61	52	110	52	86	58	215	36	129	46	99	60	84	58	76	58	137	45	0	.	10611	6
	81-90 cm	111	63	32	100	113	51	165	52	0	.	51	100	49	100	308	40	469	30	67	71	32	100	33	100	41	100	0	.	32	100	43	100	50	71	73	71	0	.	35	100	24	100	34	100	0	.	52	63	0	.	88	51	0	.	0	.	34	100	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	71	73	27	100	0	.	39	100	72	100	61	71	170	45	29	100	27	100	0	.	0	.	0	.	82	71	34	100	70	100	154	59	0	.	104	53	9	100	14	100	65	62	24	72	82	70	31	100	39	73	0	.	0	.	0	.	0	.	0	.	0	.	68	71	0	.	0	.	68	62	0	.	0	.	0	.	0	.	39	100	0	.	33	100	0	.	26	100	0	.	0	.	52	58	0	.	159	51	116	78	99	58	82	72	58	71	145	54	0	.	0	.	4119	10
	91-100 cm	26	100	0	.	0	.	33	100	0	.	0	.	0	.	46	100	165	61	0	0	0	.	45	100	0	.	0	.	44	100	0	.	27	100	0	.	0	.	0	.	0	.	0	.	0	.	64	72	0	.	72	71	0	.	0	.	0	.	0	.	0	.	39	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	100	0	.	0	.	0	.	0	.	37	100	0	.	0	.	24	100	0	.	95	100	0	.	0	.	56	71	0	.	0	.	0	0	27	72	0	.	0	0	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	68	71	0	.	0	.	0	.	0	.	46	100	0	.	0	.	78	73	0	.	0	.	43	100	0	.	0	.	0	.	1140	20
	>100 cm	0	.	75	71	0	.	47	100	0	.	0	.	0	.	42	100	40	100	0	.	0	.	60	100	0	.	0	.	0	.	0	.	0	.	38	100	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	126	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	242	100	0	.	174	49	0	.	214	100	192	100	61	75	296	53	0	.	132	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	1896	24
	total	4543	14	2952	17	3002	17	5418	14	575	48	2631	28	5612	17	10765	11	16277	8	5702	14	1387	26	2107	22	3303	18	101	.	1366	30	2549	23	2476	23	4041	19	539	39	1882	28	581	34	1245	25	54	.	2379	20	93	73	11041	9	3912	18	4873	15	3577	21	756	29	46	71	1253	31	752	34	897	33	806	34	337	56	1187	31	3933	18	1369	29	1991	24	2326	23	3091	24	4876	14	2885	19	1976	21	2031	20	3369	17	1534	28	86	.	1342	34	3292	20	2375	23	3364	18	50	96	1685	23	248	46	2085	19	2799	17	3437	12	3229	14	2419	17	3028	15	463	53	229	61	246	53	1298	30	494	44	499	47	1701	27	580	38	1091	28	1738	27	978	28	181	71	1055	29	1666	23	1987	23	878	29	1520	28	360	55	1563	27	523	30	439	30	2083	17	1291	29	2668	22	2487	20	3200	18	3657	17	2077	23	1866	24	2363	21	0	.	211017	1
higher altitude zone	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	102	42	121	35	1072	10	342	16	5	71	335	19	0	.	0	.	12	100	0	.	18	67	101	30	209	25	59	43	0	.	212	26	402	16	268	18	841	16	980	13	1109	11	116	27	108	34	27	51	238	20	10	133	167	26	9	60	37	37	217	39	97	36	188	25	242	28	1	.	91	45	209	23	87	38	120	26	0	.	23	65	73	40	0	.	8	100	258	27	203	26	132	33	0	.	0	.	0	.	198	29	186	24	185	23	31	58	0	.	0	.	141	22	34	47	59	51	113	32	109	42	164	25	82	33	124	31	264	24	26	53	0	.	139	34	0	.	11	69	221	23	58	40	83	44	29	40	24	58	0	.	6	71	0	.	872	11	718	14	715	14	57	46	31	.	0	.	19	66	0	.	0	.	0	.	0	.	0	.	13549	3
	21-30 cm	0	.	0	.	0	.	0	.	281	30	329	28	2024	10	764	16	19	72	800	19	0	.	0	.	4	100	0	.	107	60	292	38	553	21	185	45	0	.	589	26	935	17	750	17	1231	13	1719	11	2028	10	237	34	140	36	42	67	476	20	76	87	333	22	29	73	74	40	377	25	308	30	408	28	569	25	0	.	243	34	443	25	92	39	403	24	0	.	70	55	120	41	0	.	35	91	222	27	518	21	278	31	0	.	0	.	0	.	173	31	366	24	302	21	51	47	0	.	0	.	288	24	84	49	75	56	352	31	145	39	251	28	193	30	203	35	542	28	16	60	0	.	409	39	0	.	25	70	421	24	148	38	176	42	57	47	66	74	0	.	26	60	0	.	1695	11	1478	13	1576	13	167	36	31	.	0	.	95	82	0	.	0	.	0	.	0	.	0	.	27514	3
	31-40 cm	0	.	0	.	0	.	0	.	529	26	596	27	3273	9	1290	15	4	100	1135	18	0	.	0	.	29	100	0	.	159	51	439	30	876	26	240	35	0	.	552	26	1558	15	1064	17	1298	14	2746	10	2953	10	656	26	263	39	142	44	624	20	121	45	554	27	119	62	138	40	461	25	370	30	461	23	540	22	0	.	274	33	764	22	220	35	587	23	0	.	106	58	232	38	0	.	51	74	510	26	585	23	368	31	0	.	0	.	0	.	305	28	414	27	473	25	50	50	0	.	0	.	292	25	55	49	103	54	286	30	273	36	398	31	232	35	329	34	651	23	68	54	0	.	459	28	0	.	32	85	669	20	160	49	227	33	137	45	75	82	0	.	39	76	0	.	2160	11	1693	13	1911	12	209	37	0	.	0	.	18	76	0	.	0	.	0	.	0	.	0	.	38608	2
	41-50 cm	0	.	0	.	0	.	0	.	572	25	895	28	4033	10	1616	15	8	72	1327	17	0	.	0	.	38	86	0	.	198	52	433	31	865	24	263	35	0	.	1040	18	1891	15	1173	16	1616	15	3339	10	3348	10	690	25	152	40	195	64	862	22	161	43	611	22	130	54	169	43	831	23	225	34	337	27	643	22	0	.	394	33	736	24	217	34	918	22	0	.	138	41	172	38	0	.	29	77	533	24	801	23	439	26	0	.	0	.	0	.	130	34	422	23	714	24	44	48	0	.	0	.	325	27	78	42	39	63	268	32	232	45	441	32	340	42	371	28	851	22	52	74	0	.	480	27	0	.	65	85	816	20	287	32	341	31	240	29	67	100	0	.	35	63	0	.	2141	11	1602	14	1817	13	203	41	0	.	0	.	58	88	0	.	0	.	0	.	0	.	0	.	44495	2
	51-60 cm	0	.	0	.	0	.	0	.	481	31	863	27	3689	11	1651	16	27	100	775	20	0	.	0	.	36	72	0	.	67	62	302	41	723	24	255	39	0	.	672	20	1821	17	1122	18	1845	14	2629	12	2796	15	383	25	142	52	115	58	789	26	144	43	646	27	69	82	150	40	546	23	250	42	352	33	890	27	0	.	175	37	603	26	485	35	952	22	0	.	106	67	50	63	0	.	36	100	514	24	778	22	410	31	0	.	0	.	0	.	258	29	510	23	413	27	38	60	0	.	0	.	308	26	57	58	56	62	240	35	106	44	244	34	335	36	378	32	981	24	100	58	0	.	481	30	0	.	81	48	412	22	314	36	338	38	293	45	49	100	0	.	164	66	0	.	2241	12	1429	15	1621	14	249	35	0	.	0	.	84	95	0	.	0	.	0	.	0	.	0	.	40117	3
	61-70 cm	0	.	0	.	0	.	0	.	205	42	422	37	1986	14	1111	20	0	.	448	38	0	.	0	.	18	100	0	.	36	71	118	57	344	34	176	50	0	.	436	27	888	20	500	26	954	18	1634	13	1278	20	395	32	305	49	105	61	423	32	110	47	229	30	39	100	105	72	765	24	118	43	251	36	390	31	0	.	166	36	346	28	224	39	882	27	0	.	42	100	17	100	0	.	0	.	526	28	239	34	154	44	0	.	0	.	0	.	174	46	237	31	337	30	11	100	0	.	0	.	114	38	28	71	116	77	94	52	130	41	149	45	217	36	292	33	460	34	40	61	0	.	388	41	0	.	61	58	230	30	305	37	121	42	69	48	0	.	0	.	35	72	0	.	1300	17	688	18	863	21	98	56	0	.	0	.	111	61	0	.	0	.	0	.	0	.	0	.	23052	4
	71-80 cm	0	.	0	.	0	.	0	.	87	75	183	48	1014	19	650	23	0	.	251	40	0	.	0	.	0	.	0	.	0	.	31	79	158	48	105	58	0	.	489	29	628	28	347	32	656	23	1011	20	465	22	128	47	166	58	69	100	166	50	147	62	126	53	0	.	106	51	128	63	15	100	156	43	219	39	0	.	98	54	208	51	338	42	552	32	0	.	0	.	0	.	0	.	0	.	321	40	144	45	130	85	0	.	0	.	0	.	227	33	144	49	163	38	15	100	0	.	0	.	73	51	43	58	30	100	100	46	175	68	236	54	75	55	272	35	246	41	0	0	0	.	78	58	0	.	0	.	51	72	153	53	46	71	41	72	0	.	0	.	86	76	0	.	1270	20	268	34	563	33	44	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13691	5
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	453	30	321	32	0	.	98	72	0	.	0	.	0	.	0	.	0	.	0	.	53	71	0	.	0	.	126	43	169	54	258	35	282	32	482	25	267	32	118	50	0	.	0	.	24	100	31	100	35	100	0	.	0	.	76	100	34	100	42	100	49	100	0	.	54	100	69	58	67	71	279	64	0	.	0	.	0	.	0	.	0	.	83	71	31	100	85	59	0	.	0	.	0	.	37	100	135	48	144	47	34	100	0	.	0	.	18	100	0	.	23	100	0	.	49	100	25	100	113	54	175	47	105	51	22	100	0	.	44	100	0	.	0	.	38	100	0	.	0	.	0	.	0	.	0	.	44	100	0	.	417	28	195	36	155	44	0	.	0	.	0	.	32	100	0	.	0	.	0	.	0	.	0	.	5390	8
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	218	47	64	100	0	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	133	45	50	100	56	73	76	71	83	64	199	44	0	.	31	100	0	.	0	.	0	.	46	100	0	.	0	.	88	100	0	.	0	.	119	79	0	.	33	100	24	100	0	0	47	100	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	0	.	20	100	24	100	38	100	0	.	0	.	0	.	87	71	0	.	0	.	0	.	25	100	37	100	0	.	69	100	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	166	43	108	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1937	15
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	137	62	73	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	158	54	40	100	290	78	115	58	255	44	69	58	39	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	100	0	.	0	.	22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	71	0	.	0	.	0	.	116	79	58	100	0	.	0	.	0	.	0	.	40	100	0	.	32	100	0	.	0	.	43	100	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	71	0	.	29	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1683	20
	total	0	.	0	.	0	.	0	.	2257	24	3409	22	17898	8	7882	12	63	76	5204	14	0	.	0	.	138	78	0	.	585	45	1716	27	3808	18	1283	31	0	.	4406	16	8383	12	5826	14	8915	10	14879	8	14512	8	2760	20	1305	31	695	53	3602	18	801	36	2747	18	395	51	779	36	3490	20	1417	27	2193	21	3704	18	1	.	1528	27	3424	19	1731	27	4740	19	0	.	486	38	664	33	0	.	159	72	3002	20	3299	18	2053	23	0	.	0	.	0	.	1638	20	2496	18	2768	18	274	34	0	.	0	.	1687	18	378	37	532	44	1453	26	1244	30	1987	24	1587	27	2234	22	4100	19	323	52	0	.	2478	24	0	.	276	55	2857	16	1425	28	1334	27	867	30	281	82	0	.	436	61	0	.	12309	8	8179	10	9250	10	1026	30	62	.	0	.	417	52	0	.	0	.	0	.	0	.	0	.	210037	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	305	23	276	24	242	32	274	27	124	37	185	26	1439	9	845	11	1059	11	720	13	61	38	122	31	186	20	0	.	106	37	237	22	365	20	395	26	45	49	320	22	465	15	377	15	862	16	1300	12	1117	11	987	13	335	21	390	22	431	15	103	33	188	24	58	30	83	28	246	35	167	28	208	23	370	22	226	23	222	31	342	19	240	23	195	21	294	21	190	24	155	29	176	36	158	21	415	21	210	26	167	28	229	21	247	28	317	34	206	28	407	18	219	21	470	20	341	18	607	17	673	14	473	18	478	18	154	28	113	41	197	23	199	24	199	26	324	22	149	30	66	42	242	24	106	32	91	34	240	22	160	30	159	28	160	22	105	39	64	45	10	58	63	39	983	10	795	13	1025	12	123	28	151	31	110	40	248	28	194	28	118	41	94	34	143	32	0	.	29431	2
	21-30 cm	519	18	514	25	430	21	559	26	331	27	580	21	2678	9	1830	11	2017	11	1742	13	115	33	263	26	450	21	1	.	248	30	699	23	969	18	715	24	93	42	854	20	1046	16	897	16	1252	13	2236	10	2039	10	1616	11	562	19	636	20	884	16	164	46	344	21	167	32	136	29	420	23	458	25	553	26	828	20	521	22	452	26	640	20	476	30	687	20	810	19	292	27	381	23	191	30	510	23	474	22	518	21	354	28	524	24	344	31	570	25	183	30	615	18	361	20	526	20	634	21	876	16	999	15	833	19	770	17	479	29	197	33	307	25	404	24	287	28	621	26	357	30	95	49	616	28	142	34	220	35	477	23	387	27	352	27	329	26	233	35	165	42	59	48	160	39	1810	11	1644	12	2146	11	318	24	274	40	235	26	481	26	443	22	321	30	222	28	338	27	0	.	57502	2
	31-40 cm	861	18	444	23	701	24	814	22	692	24	1011	20	4336	8	2916	10	2490	11	2428	12	189	42	395	26	715	23	13	.	426	29	974	20	1338	19	1033	19	141	45	980	21	1660	14	1285	15	1298	14	3263	9	2966	10	3253	10	906	19	1078	21	1295	17	214	31	560	26	378	32	261	30	618	21	652	24	505	22	861	19	798	23	661	23	1120	17	663	22	1130	18	1063	16	537	24	680	22	402	25	878	19	804	21	599	22	519	28	807	23	404	25	569	24	311	27	630	21	551	23	487	24	630	20	922	16	800	15	424	20	771	20	388	27	302	33	466	28	450	24	398	30	714	22	360	31	112	49	765	21	242	36	283	31	720	19	358	27	639	22	637	24	250	33	169	37	151	48	261	33	2264	11	1805	12	2321	11	336	27	446	26	364	29	627	21	832	21	452	26	393	33	293	31	0	.	78911	2
	41-50 cm	1155	16	446	25	594	22	1137	17	758	23	1500	20	5228	9	3640	10	3397	10	2692	11	304	32	488	27	784	21	22	.	494	33	1005	21	1342	18	1129	21	156	46	1552	16	2031	14	1376	14	1616	15	3679	10	3381	10	3695	10	1058	19	1214	19	1817	16	380	28	618	22	482	31	315	30	1053	19	374	26	437	27	876	19	817	22	746	25	1170	18	716	23	1721	17	1252	19	537	22	916	22	465	25	891	18	812	20	838	22	729	23	710	25	507	28	610	24	139	32	611	18	764	23	268	23	396	20	498	22	721	17	451	20	477	22	344	28	335	38	492	29	711	28	466	24	959	21	332	30	188	41	728	21	391	32	267	33	840	19	455	24	773	22	649	20	329	35	368	33	120	52	211	35	2204	11	1645	13	2149	12	535	26	480	29	525	24	718	21	960	20	382	27	422	31	423	26	0	.	89388	1
	51-60 cm	748	21	573	25	540	24	1118	21	565	28	1526	22	5126	10	4524	11	3422	11	1914	14	343	35	388	36	743	23	40	.	343	34	763	23	1276	18	1019	20	65	71	1088	18	1881	16	1343	16	1845	14	2929	11	2815	15	2288	11	1041	24	1163	20	1491	19	312	29	646	27	400	37	257	30	765	20	370	32	371	32	1073	23	878	24	297	28	1135	21	952	23	1631	17	753	22	812	24	388	31	492	35	753	22	841	22	805	21	604	26	675	29	398	37	525	24	258	29	787	20	419	26	180	28	273	26	196	34	676	17	316	29	429	22	311	32	148	38	260	33	520	28	513	32	1118	22	398	31	61	54	623	25	352	38	241	34	426	22	546	28	756	24	750	26	192	41	246	43	290	43	345	31	2359	12	1470	15	1780	13	594	27	658	29	454	24	761	26	740	21	472	36	410	33	539	27	0	.	80919	2
	61-70 cm	573	24	376	36	238	36	802	21	239	39	927	27	2549	13	2673	13	2165	14	728	27	281	38	217	42	346	34	0	.	178	39	343	32	658	28	542	28	20	100	550	23	935	19	667	21	954	18	1770	12	1287	20	1077	17	951	25	878	23	805	23	165	46	229	30	75	71	155	52	885	21	154	37	260	35	454	29	365	28	231	34	613	26	386	30	1253	22	524	31	534	28	92	45	224	37	228	42	706	25	239	34	399	30	213	43	151	43	272	40	190	43	328	25	344	30	108	47	183	44	182	40	290	25	96	42	251	40	126	43	130	41	149	45	334	31	309	32	460	34	154	37	58	100	450	36	205	42	122	58	247	29	382	33	225	35	188	36	49	58	293	33	35	72	381	45	1312	17	688	18	974	19	258	39	477	33	390	33	522	27	308	33	146	47	103	54	459	35	0	.	44435	3
	71-80 cm	245	38	215	41	142	54	469	35	122	60	260	38	1297	19	1366	16	1115	20	481	33	62	71	96	50	177	49	25	.	80	74	202	40	180	44	379	34	19	100	489	29	664	27	489	26	668	22	1127	19	465	22	567	24	334	35	210	45	397	32	189	51	126	53	50	71	198	40	203	45	15	100	156	43	219	39	258	43	174	40	282	41	517	32	727	28	119	45	299	35	0	.	55	73	74	78	366	36	144	45	375	48	100	51	159	53	106	61	227	33	252	36	168	37	57	78	86	42	44	51	235	29	173	44	148	35	115	42	175	68	256	51	154	40	293	33	297	36	184	60	0	.	101	50	189	51	30	100	51	72	153	53	96	50	101	52	0	.	117	56	86	76	117	69	1270	20	268	34	624	30	154	43	86	58	215	36	129	46	99	60	84	58	76	58	137	45	0	.	24303	4
	81-90 cm	111	63	32	100	113	51	165	52	0	.	51	100	502	28	628	25	469	30	165	51	32	100	33	100	41	100	0	.	32	100	43	100	104	50	73	71	0	.	162	40	193	49	292	33	282	32	533	24	267	32	206	36	0	.	0	.	58	72	31	100	35	100	0	.	0	.	110	76	34	100	42	100	49	100	71	73	81	75	69	58	106	58	351	55	61	71	170	45	29	100	27	100	0	.	83	71	31	100	167	46	34	100	70	100	154	59	37	100	239	36	152	44	48	77	65	62	24	72	99	61	31	100	61	59	0	.	49	100	25	100	113	54	175	47	105	51	90	59	0	.	44	100	68	62	0	.	38	100	0	.	0	.	39	100	0	.	33	100	44	100	26	100	417	28	195	36	207	36	0	.	159	51	116	78	131	50	82	72	58	71	145	54	0	.	0	.	9509	6
	91-100 cm	26	100	0	.	0	.	33	100	0	.	0	.	218	47	110	72	165	61	36	100	0	.	45	100	0	.	0	.	44	100	0	.	54	71	0	.	0	.	133	45	50	100	56	73	76	71	148	48	199	44	72	71	31	100	0	.	0	.	0	.	46	100	39	100	0	.	88	100	0	.	0	.	119	79	0	.	33	100	24	100	0	0	101	71	0	.	0	.	0	.	0	.	37	100	35	100	0	.	24	100	0	.	95	100	0	.	20	100	80	58	38	100	0	.	0	0	27	72	87	71	0	0	11	100	0	.	25	100	37	100	0	.	69	100	0	.	0	0	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	68	71	0	.	0	.	166	43	108	51	46	100	0	.	0	.	78	73	0	.	0	.	43	100	0	.	0	.	0	.	3078	12
	>100 cm	0	.	75	71	0	.	47	100	0	.	0	.	137	62	115	73	40	100	0	.	0	.	60	100	0	.	0	.	0	.	0	.	0	.	38	100	0	.	158	54	40	100	290	78	115	58	273	42	69	58	39	100	0	.	0	.	0	.	0	.	0	.	0	.	126	100	0	.	0	.	0	.	43	100	0	.	0	.	22	100	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	71	0	.	0	.	242	100	116	79	231	44	0	.	214	100	192	100	61	75	336	48	0	.	164	45	0	.	0	.	43	100	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	0	.	0	.	0	.	0	.	0	.	0	.	46	71	0	.	62	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	3579	16
	total	4543	14	2952	17	3002	17	5418	14	2832	22	6041	17	23510	7	18647	8	16340	8	10906	10	1387	26	2107	22	3441	18	101	.	1951	25	4265	17	6285	14	5324	16	539	39	6288	14	8964	11	7070	12	8969	10	17257	7	14606	8	13802	8	5216	16	5567	15	7180	14	1558	23	2793	18	1648	26	1531	25	4387	17	2223	21	2530	20	4890	16	3934	18	2897	19	5416	15	4057	18	7831	14	4876	14	3370	17	2641	18	2031	20	3528	16	4536	16	3385	18	3395	19	3292	20	2375	23	3364	18	1687	20	4181	14	3016	17	2359	17	2799	17	3437	12	4916	11	2797	16	3560	15	1916	23	1474	27	2233	22	2885	20	2729	19	4599	17	2024	24	580	38	3569	19	1738	27	1254	25	3039	16	2480	20	3000	17	2855	18	1159	29	1520	28	796	42	1563	27	12832	8	8618	10	11333	9	2317	21	2730	21	2487	20	3617	17	3657	17	2077	23	1866	24	2363	21	0	.	421054	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 2297315. Birmensdorf, Swiss Federal Research Institute WSL

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