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	23-35calculated with twice the standard error, i.e. 29 ± 2 × 3

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

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

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

Data: see example 2

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

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
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	230	35	168	43	287	45	153	26	0	.	73	51	343	31	440	20	1119	16	387	23	87	44	91	49	164	29	0	.	89	66	158	34	221	34	205	53	22	64	58	43	51	53	102	39	0	.	287	43	15	86	657	17	248	35	430	26	235	26	123	41	40	75	63	37	67	49	37	48	104	49	5	.	111	56	173	36	185	49	127	44	161	41	73	48	326	26	170	34	83	58	134	44	127	31	127	49	0	.	5	100	301	27	301	35	262	34	12	.	179	44	50	55	405	28	294	28	698	23	437	27	388	31	405	28	69	62	5	.	47	72	126	41	92	51	2	.	116	58	65	61	136	42	49	51	83	50	35	79	129	50	98	35	75	39	139	52	89	54	8	100	45	68	138	49	60	48	243	30	44	49	91	41	83	76	168	30	162	48	103	40	80	54	164	43	0	.	15036	4
	21-30 cm	392	26	444	40	269	32	511	40	21	.	234	46	572	26	1130	22	2094	14	1207	23	105	45	306	34	401	29	2	.	75	52	496	33	464	45	369	43	121	52	287	36	83	53	90	44	0	.	531	31	20	71	1055	16	481	25	649	23	496	31	70	54	21	78	125	46	71	49	46	49	250	47	0	.	270	44	390	33	335	42	162	37	432	42	248	36	1022	25	162	41	239	39	212	37	524	34	221	53	0	.	38	81	572	28	338	44	623	31	19	.	219	40	79	68	454	32	748	30	966	20	540	30	754	31	583	25	226	68	9	.	71	72	202	52	119	52	8	.	272	53	143	57	295	38	87	48	85	48	96	85	347	42	268	37	157	53	282	42	215	45	37	98	101	63	137	52	86	49	383	31	120	40	201	42	174	37	505	30	447	30	349	41	203	40	330	36	0	.	29589	3
	31-40 cm	577	24	383	32	489	32	640	29	96	.	472	38	1036	24	1467	17	2430	14	1504	22	111	56	381	34	659	31	23	.	213	55	606	33	338	38	644	31	206	53	453	45	60	47	169	50	0	.	414	32	23	75	2083	16	691	27	1210	29	724	34	100	53	12	100	142	57	180	52	155	39	389	48	0	.	385	42	548	31	524	39	331	38	399	38	524	35	1124	22	403	40	424	36	442	34	614	29	161	44	0	.	190	70	841	31	489	30	589	30	11	.	158	42	130	65	223	33	548	28	789	21	319	31	322	33	602	34	177	61	0	.	79	80	222	36	77	77	47	.	283	51	152	60	412	38	135	76	194	44	91	73	185	35	446	34	300	44	210	35	171	50	181	65	108	55	148	48	88	55	229	40	108	52	429	37	420	36	681	28	1024	25	414	33	550	39	250	35	0	.	37985	3
	41-50 cm	930	24	310	31	386	39	1260	23	101	.	413	39	1386	27	1669	17	3822	12	1584	21	172	59	383	44	537	35	38	.	255	63	612	34	336	41	691	32	178	65	696	34	138	61	159	46	0	.	256	34	55	85	2675	15	1058	28	1329	22	1298	27	184	51	13	100	248	43	241	44	229	47	173	55	10	.	281	47	758	31	493	44	203	35	374	50	721	35	1339	24	295	36	628	39	482	32	723	27	203	56	0	.	323	49	586	33	519	35	604	28	18	.	240	37	59	73	113	50	352	26	491	32	330	33	279	32	429	30	126	59	36	.	51	84	384	45	78	53	37	.	281	45	276	46	344	37	185	70	155	49	42	78	176	50	505	38	348	43	300	43	375	43	139	75	147	60	64	54	49	65	247	40	286	48	371	40	582	30	749	25	1093	24	378	33	510	37	378	32	0	.	43362	3
	51-60 cm	664	31	446	31	283	42	1363	26	27	.	381	60	1271	28	2731	17	3966	14	1130	27	269	46	131	61	611	33	70	.	154	62	541	34	400	44	783	29	52	100	569	39	59	58	193	36	0	.	249	49	31	100	2024	16	961	36	1295	24	939	35	112	52	0	.	216	75	165	51	225	56	111	69	0	.	113	59	809	35	134	50	423	47	473	38	705	35	656	29	724	37	244	45	582	47	371	33	159	58	0	.	175	46	516	38	650	37	608	27	0	.	294	53	0	.	179	39	192	43	226	49	166	42	197	45	313	35	116	71	55	.	0	.	152	55	227	80	38	.	194	56	42	71	214	49	121	73	158	60	26	100	302	53	285	47	363	50	184	51	159	46	175	60	290	46	134	77	0	.	174	50	168	58	516	39	375	32	697	34	823	28	658	41	336	41	523	36	0	.	38836	4
	61-70 cm	425	35	232	44	124	51	956	27	58	.	292	66	619	43	1409	22	2284	17	191	53	242	53	176	68	221	54	0	.	68	100	294	45	283	77	251	46	35	100	57	71	41	100	250	41	0	.	86	69	16	100	621	25	444	38	965	29	349	40	101	100	0	.	66	100	38	100	72	76	33	100	0	.	37	100	296	38	85	100	288	62	121	72	209	52	568	37	437	38	78	71	250	45	108	59	39	100	0	.	199	54	118	50	219	47	329	50	30	.	0	0	0	.	104	64	156	43	215	48	204	44	21	100	135	48	52	73	0	.	0	.	110	60	27	100	0	.	115	58	0	.	114	59	50	72	108	100	32	100	44	100	30	100	100	71	61	71	251	47	0	.	203	73	0	.	0	.	54	59	91	100	617	42	136	79	487	35	232	47	186	53	104	75	272	52	0	.	19020	5
	71-80 cm	202	51	138	58	209	60	453	39	0	.	0	.	128	58	897	28	1272	25	41	100	60	100	90	70	81	71	44	.	0	.	186	58	0	.	170	61	0	.	0	.	61	71	148	55	0	.	106	62	0	.	430	35	35	100	240	45	187	75	0	.	0	.	90	71	85	100	42	100	0	.	0	.	0	.	331	51	134	60	36	100	201	63	183	62	132	58	265	45	0	.	63	100	0	.	0	.	0	.	173	100	67	71	218	60	175	61	0	.	77	79	9	100	0	.	83	59	15	100	104	60	24	100	109	45	26	100	0	.	37	100	97	71	34	100	32	.	85	71	0	.	42	100	45	100	53	100	0	.	0	.	54	100	65	100	0	.	20	100	0	.	163	79	0	.	0	.	47	71	28	100	93	71	51	100	82	71	40	100	95	71	90	71	43	100	0	.	9116	8
	81-90 cm	41	100	53	100	87	71	224	60	0	.	0	.	81	100	264	61	495	36	0	.	0	0	58	100	0	.	0	.	0	.	71	100	0	.	0	.	0	.	58	100	44	100	63	100	0	.	79	73	0	.	46	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	79	100	0	.	0	.	67	100	0	.	0	.	66	100	52	100	0	.	0	.	0	.	0	.	0	.	56	100	116	100	259	59	0	.	112	74	16	100	26	100	29	100	27	100	26	100	0	.	27	100	0	.	0	.	0	.	0	.	0	.	0	.	65	100	0	.	0	.	51	100	0	.	0	.	0	.	0	.	72	100	0	.	0	.	0	.	42	100	0	.	0	.	94	58	0	.	113	72	50	100	47	100	80	100	100	71	85	71	0	.	0	.	3417	14
	91-100 cm	0	.	0	.	0	.	54	100	0	.	0	.	0	.	79	100	292	61	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	100	0	.	69	100	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	100	0	.	168	100	0	.	0	.	53	100	0	.	0	.	0	0	29	100	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	116	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	100	0	.	0	.	0	.	0	.	0	.	0	.	1109	28
	>100 cm	0	.	67	100	0	.	86	100	0	.	0	.	0	.	74	100	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	117	100	0	.	0	.	0	.	36	100	166	100	0	.	183	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	.	865	33
	total	3462	19	2241	23	2133	26	5700	19	304	.	1865	40	5437	22	10160	14	17842	10	6045	18	1046	35	1615	31	2674	26	177	.	854	48	2964	27	2042	32	3181	25	614	53	2178	34	537	40	1174	33	0	.	2079	26	160	72	9658	13	3918	24	6119	18	4227	26	689	40	86	71	1020	39	847	43	807	42	1060	42	15	.	1198	39	3385	25	1890	33	1570	34	2229	33	2664	29	5169	18	2521	28	1747	29	2166	28	2467	24	911	41	0	.	1144	47	3056	25	3018	28	3448	21	90	.	1448	34	344	57	1503	25	2401	23	3493	17	2291	22	1985	25	2806	22	792	53	105	.	286	71	1292	36	654	52	165	.	1412	42	679	42	1556	32	722	51	837	39	321	72	1184	35	1686	31	1480	36	1176	33	1397	37	539	64	1099	45	620	40	283	51	1470	26	846	43	2431	30	1915	26	3415	23	3900	21	2284	29	1958	30	1960	27	0	.	198337	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	.	40	71	134	48	823	15	268	22	4	.	335	28	0	.	0	.	23	.	0	.	30	67	111	40	173	35	91	46	0	.	146	42	315	27	240	25	475	25	807	16	907	16	95	39	102	51	17	65	273	27	19	93	198	34	12	74	34	50	137	42	108	58	199	34	148	43	0	.	38	73	215	34	98	53	85	36	0	.	39	67	80	58	0	.	0	.	151	41	209	35	90	56	0	.	0	.	0	.	187	41	131	41	93	31	7	80	0	.	0	.	60	39	26	53	41	86	146	39	104	58	187	35	93	49	83	41	167	38	37	60	0	.	106	41	0	.	3	100	201	29	26	55	110	57	38	49	16	.	0	.	0	.	0	.	752	17	856	19	562	22	32	58	51	.	0	.	16	93	0	.	0	.	0	.	0	.	0	.	11399	4
	21-30 cm	0	.	0	.	0	.	0	.	292	43	370	34	1665	14	676	21	13	.	815	25	0	.	0	.	7	.	0	.	176	62	396	46	373	32	290	49	0	.	571	39	757	25	547	28	663	21	1428	18	1789	13	156	40	78	55	26	59	532	26	157	76	325	29	34	100	93	53	342	38	314	42	454	35	442	35	0	.	97	56	401	39	104	51	365	32	0	.	79	64	79	68	0	.	0	.	128	39	377	34	225	37	0	.	0	.	0	.	114	58	348	33	256	30	42	73	0	.	0	.	203	42	85	51	64	74	395	40	164	54	334	34	140	43	102	47	466	53	21	71	0	.	260	40	0	.	0	.	412	29	115	56	199	60	84	54	74	.	0	.	0	.	0	.	1364	17	1413	20	1246	20	142	59	51	.	0	.	142	91	0	.	0	.	0	.	0	.	0	.	23873	4
	31-40 cm	0	.	0	.	0	.	0	.	631	33	728	34	2888	13	1289	21	7	.	1124	22	0	.	0	.	0	.	0	.	248	55	408	42	582	33	348	38	0	.	394	42	1507	18	796	23	1061	22	2517	15	2992	14	638	39	147	49	150	56	662	27	156	59	630	35	99	68	145	57	396	30	460	37	393	34	540	31	0	.	267	49	667	32	299	41	524	34	0	.	160	69	269	51	0	.	0	.	323	33	663	29	522	36	0	.	0	.	0	.	192	47	364	35	460	38	58	66	0	.	0	.	243	40	82	58	126	75	326	38	327	48	467	39	245	48	374	44	419	37	78	65	0	.	359	35	0	.	0	.	525	27	144	46	142	47	204	51	106	.	0	.	53	.	0	.	1834	17	1463	18	1664	18	126	54	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	36019	3
	41-50 cm	0	.	0	.	0	.	0	.	747	30	1052	33	3733	14	1416	19	6	.	1232	22	0	.	0	.	11	.	0	.	217	58	474	40	753	37	350	37	0	.	826	26	2115	18	856	22	1701	21	2596	15	3274	13	732	36	169	48	128	73	704	30	164	54	667	31	150	63	108	79	741	30	294	42	290	37	727	30	0	.	292	47	513	35	195	50	628	31	0	.	145	53	163	49	0	.	0	.	603	32	696	28	412	36	0	.	0	.	0	.	112	49	309	31	816	33	22	100	0	.	0	.	135	41	94	51	63	64	247	47	311	58	601	38	272	72	427	38	468	36	30	100	0	.	364	35	0	.	26	71	625	27	116	49	288	44	326	36	118	.	0	.	15	.	0	.	1814	19	1584	19	1770	18	35	100	0	.	0	.	83	100	0	.	0	.	0	.	0	.	0	.	40947	3
	51-60 cm	0	.	0	.	0	.	0	.	679	35	927	33	3415	15	1114	21	47	.	574	26	0	.	0	.	27	.	0	.	74	78	305	41	611	33	249	41	0	.	680	27	1558	21	933	25	1763	21	2510	17	2492	16	338	37	149	71	97	87	766	31	108	66	576	41	123	82	98	67	563	31	343	50	236	52	773	33	0	.	120	54	404	34	629	43	726	30	0	.	169	73	0	.	0	.	0	.	445	32	809	31	522	38	0	.	0	.	0	.	260	42	456	34	454	40	49	75	0	.	0	.	173	53	82	71	85	73	246	46	143	51	342	39	328	50	423	44	587	47	105	72	0	.	346	35	0	.	40	71	340	29	255	44	227	61	420	53	87	.	0	.	85	.	0	.	1566	18	1321	21	1965	19	73	78	0	.	0	.	119	109	0	.	0	.	0	.	0	.	0	.	36527	4
	61-70 cm	0	.	0	.	0	.	0	.	317	46	436	43	1773	20	886	30	0	.	356	39	0	.	0	.	0	.	0	.	62	71	142	69	469	41	142	47	0	.	480	36	780	26	437	34	955	24	1254	19	1008	22	410	43	476	53	103	76	510	40	179	51	199	46	68	100	75	100	612	31	176	47	101	65	458	41	0	.	84	71	287	41	268	51	770	35	0	.	0	.	30	100	0	.	0	.	652	35	229	48	145	62	0	.	0	.	0	.	195	69	284	40	324	42	20	100	0	.	0	.	73	78	53	71	195	85	158	52	180	48	164	52	199	48	273	45	369	55	55	71	0	.	434	55	0	.	37	100	168	42	216	61	0	.	109	54	0	.	0	.	0	.	0	.	968	31	626	25	793	28	43	100	0	.	0	.	33	100	0	.	0	.	0	.	0	.	0	.	21301	5
	71-80 cm	0	.	0	.	0	.	0	.	149	75	130	74	745	25	642	30	0	.	111	60	0	.	0	.	0	.	0	.	0	.	13	100	269	53	20	100	0	.	545	39	787	31	109	58	379	35	661	32	466	30	91	77	200	77	118	100	218	62	129	74	84	71	0	.	113	71	55	100	0	.	147	66	333	42	0	.	59	71	356	50	503	47	563	41	0	.	0	.	0	.	0	.	0	.	465	43	51	100	38	100	0	.	0	.	0	.	207	47	158	65	163	55	29	100	0	.	0	.	100	60	77	58	56	100	169	46	92	100	134	77	54	71	88	59	30	100	0	.	0	.	46	100	0	.	0	.	90	72	83	100	44	100	72	72	0	.	0	.	111	.	0	.	999	26	154	45	454	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11957	7
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	495	41	355	38	0	.	175	72	0	.	0	.	0	.	0	.	0	.	0	.	91	72	0	.	0	.	144	53	111	72	164	51	295	39	532	33	304	42	99	72	0	.	0	.	0	.	56	100	62	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	100	118	71	282	100	0	.	0	.	0	.	0	.	0	.	66	100	53	100	92	74	0	.	0	.	0	.	0	.	53	100	51	100	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	100	100	75	52	100	49	100	37	100	0	.	0	.	0	.	0	.	67	100	0	.	0	.	0	.	0	.	0	.	81	.	0	.	405	42	170	53	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4686	12
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	65	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	144	59	92	100	0	.	61	100	0	.	61	100	0	.	53	100	0	.	0	.	0	.	84	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	81	100	0	.	0	.	0	.	0	.	0	.	65	100	0	.	0	.	0	.	0	.	0	.	37	100	0	.	0	.	0	.	0	.	0	.	75	100	0	.	0	.	0	.	47	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	60	94	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1135	23
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	126	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	0	.	0	.	127	71	191	61	52	100	67	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	163	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	985	29
	total	0	.	0	.	0	.	0	.	2854	30	3777	28	15571	11	6771	16	78	.	4788	18	0	.	0	.	69	.	0	.	808	50	1848	35	3321	24	1490	34	0	.	3981	23	8021	15	4081	20	7480	16	12497	12	13343	11	2626	30	1372	43	640	72	3665	24	967	46	2825	25	485	59	665	50	2846	25	1696	35	1820	31	3422	26	0	.	956	42	2922	26	2213	34	4024	26	0	.	592	48	621	45	0	.	0	.	2898	26	3086	24	2090	30	0	.	0	.	0	.	1467	31	2103	27	2616	27	228	61	0	.	0	.	1063	32	499	46	629	63	1686	32	1368	41	2273	31	1432	39	1858	31	2556	32	364	62	0	.	1915	29	0	.	107	76	2428	22	954	43	1010	39	1253	35	402	.	0	.	344	.	0	.	9883	13	7681	14	8542	15	451	51	102	.	0	.	404	72	0	.	0	.	0	.	0	.	0	.	188828	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	230	35	168	43	287	45	153	26	40	71	207	36	1166	14	708	15	1123	16	722	18	87	44	91	49	187	28	0	.	120	52	270	26	394	24	296	40	22	64	204	32	365	25	342	21	475	25	1094	16	922	16	752	15	350	29	448	25	508	19	143	37	238	31	75	33	101	36	174	35	211	38	204	33	259	34	173	36	223	42	342	27	259	32	158	29	326	26	209	30	162	41	134	44	127	31	277	31	209	35	95	54	301	27	301	35	262	34	199	39	310	31	142	28	412	27	294	28	698	23	497	24	415	30	446	27	215	33	108	55	234	32	219	31	175	33	169	38	153	47	65	61	242	29	49	51	86	48	236	27	155	43	207	35	113	31	155	48	89	54	8	100	45	68	890	17	916	18	804	18	76	37	142	44	83	76	184	28	162	48	103	40	80	54	164	43	0	.	26435	3
	21-30 cm	392	26	444	40	269	32	511	40	313	41	604	27	2237	12	1806	16	2107	14	2022	17	105	45	306	34	408	29	2	.	251	46	891	27	837	29	659	33	121	52	858	28	840	23	637	25	663	21	1959	15	1809	13	1210	15	559	23	675	23	1028	20	227	55	346	27	159	42	164	37	387	34	564	31	454	35	712	27	390	33	432	35	563	29	536	36	613	24	1022	25	240	34	318	34	212	37	524	34	349	36	377	34	263	34	572	28	338	44	623	31	133	52	567	25	334	28	496	30	748	30	966	20	743	25	840	28	648	24	621	35	174	52	405	31	342	35	221	35	475	52	293	49	143	57	555	28	87	48	85	48	508	28	462	35	467	33	241	39	356	39	215	45	37	98	101	63	1501	16	1499	19	1629	17	262	37	252	39	174	37	647	31	447	30	349	41	203	40	330	36	0	.	53463	2
	31-40 cm	577	24	383	32	489	32	640	29	727	31	1199	25	3923	11	2756	13	2437	14	2628	15	111	56	381	34	659	31	23	.	461	39	1014	26	920	25	993	24	206	53	846	31	1567	17	965	20	1061	22	2932	14	3015	14	2721	15	838	24	1361	26	1386	22	255	42	641	34	240	44	325	38	552	24	849	30	393	34	926	25	548	31	791	30	999	25	698	28	1048	24	1124	22	563	35	693	29	442	34	614	29	484	26	663	29	712	32	841	31	489	30	589	30	203	45	522	27	590	33	281	30	548	28	789	21	561	24	404	28	728	31	504	33	327	48	546	36	467	30	450	39	467	35	361	42	152	60	771	26	135	76	194	44	616	25	329	28	588	28	504	33	316	41	171	50	234	55	108	55	1983	16	1552	17	1893	16	233	38	429	37	420	36	690	27	1024	25	414	33	550	39	250	35	0	.	74003	2
	41-50 cm	930	24	310	31	386	39	1260	23	848	29	1464	26	5119	12	3084	13	3828	12	2817	15	172	59	383	44	548	34	38	.	472	43	1086	26	1089	28	1040	25	178	65	1522	21	2253	17	1016	20	1701	21	2853	13	3329	13	3407	14	1227	25	1457	21	2002	20	347	37	680	31	397	36	348	39	970	25	467	33	300	35	1008	25	758	31	785	33	716	27	569	37	1349	24	1339	24	440	30	791	32	482	32	723	27	806	28	696	28	735	29	586	33	519	35	604	28	129	44	548	23	876	32	135	45	352	26	491	32	465	26	373	27	491	27	373	37	347	53	652	36	656	40	505	33	505	34	311	42	276	46	709	25	185	70	181	43	667	26	292	36	794	29	674	28	418	42	375	43	153	69	147	60	1878	18	1633	18	2017	17	321	44	371	40	582	30	832	25	1093	24	378	33	510	37	378	32	0	.	84310	2
	51-60 cm	664	31	446	31	283	42	1363	26	706	34	1309	29	4686	13	3845	13	4013	14	1704	19	269	46	131	61	638	32	70	.	228	49	846	26	1011	26	1032	24	52	100	1249	23	1617	20	1125	22	1763	21	2759	16	2523	16	2363	15	1110	33	1392	23	1705	24	220	42	576	41	339	56	263	41	789	27	454	41	236	52	886	30	809	35	253	36	827	29	1102	30	1431	23	656	29	893	33	244	45	582	47	371	33	604	28	809	31	697	30	516	38	650	37	608	27	260	42	750	29	454	40	228	34	192	43	226	49	339	33	278	38	399	31	361	38	198	46	342	39	480	38	650	40	625	45	299	44	42	71	560	28	121	73	199	50	365	28	557	35	512	38	782	37	271	47	159	46	260	52	290	46	1699	18	1321	21	2139	17	242	47	516	39	375	32	816	33	823	28	658	41	336	41	523	36	0	.	75363	2
	61-70 cm	425	35	232	44	124	51	956	27	376	41	728	37	2393	18	2295	18	2284	17	547	31	242	53	176	68	221	54	0	.	130	62	435	38	751	39	394	34	35	100	537	33	821	25	687	26	955	24	1340	18	1024	22	1030	23	920	33	1069	27	859	29	280	49	199	46	134	71	113	74	684	29	209	42	101	65	495	39	296	38	169	62	575	37	389	41	979	30	568	37	437	38	108	58	250	45	108	59	691	33	229	48	344	40	118	50	219	47	329	50	225	61	284	40	324	42	124	56	156	43	215	48	278	38	74	58	330	54	210	43	180	48	164	52	309	38	299	42	369	55	171	45	0	.	547	46	50	72	146	78	200	39	260	54	30	100	209	44	61	71	251	47	0	.	203	73	968	31	626	25	846	26	134	75	617	42	136	79	521	34	232	47	186	53	104	75	272	52	0	.	40321	4
	71-80 cm	202	51	138	58	209	60	453	39	149	75	130	74	873	23	1540	20	1272	25	152	51	60	100	90	70	81	71	44	.	0	.	199	55	269	53	190	56	0	.	545	39	848	30	257	40	379	35	767	28	466	30	520	32	234	67	358	45	405	48	129	74	84	71	90	71	197	59	97	71	0	.	147	66	333	42	331	51	193	47	391	46	703	38	747	34	132	58	265	45	0	.	63	100	0	.	465	43	51	100	211	84	67	71	218	60	175	61	207	47	235	51	173	52	29	100	83	59	15	100	204	42	101	50	165	45	195	42	92	100	171	64	151	52	122	51	63	71	85	71	0	.	88	71	45	100	53	100	90	72	83	100	98	71	137	61	0	.	20	100	111	100	163	79	999	26	154	45	501	34	28	100	93	71	51	100	82	71	40	100	95	71	90	71	43	100	0	.	21073	5
	81-90 cm	41	100	53	100	87	71	224	60	0	.	0	.	575	38	619	34	495	36	175	72	0	0	58	100	0	.	0	.	0	.	71	100	91	72	0	.	0	.	202	48	154	59	227	46	295	39	611	30	304	42	145	58	0	.	0	.	0	.	56	100	62	100	0	.	0	.	0	.	0	.	0	.	0	.	79	100	0	.	44	100	185	58	282	100	0	.	66	100	52	100	0	.	0	.	66	100	53	100	92	74	56	100	116	100	259	59	0	.	165	60	67	80	26	100	29	100	27	100	26	100	0	.	27	100	0	.	0	.	46	100	100	75	52	100	49	100	102	73	0	.	0	.	51	100	0	.	67	100	0	.	0	.	72	100	0	.	0	.	81	100	42	100	405	42	170	53	131	50	0	.	113	72	50	100	47	100	80	100	100	71	85	71	0	.	0	.	8103	9
	91-100 cm	0	.	0	.	0	.	54	100	0	.	0	.	34	100	79	100	292	61	65	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	144	59	92	100	0	.	61	100	69	100	61	100	69	100	53	100	0	.	0	.	0	.	84	100	72	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	81	100	0	.	0	.	0	.	0	.	0	.	65	100	0	.	41	100	0	.	168	100	0	.	37	100	53	100	0	.	0	.	0	0	29	100	75	100	0	.	20	100	0	.	47	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	116	71	0	.	0	.	142	60	94	71	0	.	0	.	0	.	46	100	0	.	0	.	0	.	0	.	0	.	0	.	2244	18
	>100 cm	0	.	67	100	0	.	86	100	0	.	0	.	0	.	200	73	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	100	0	.	51	100	0	.	0	.	127	71	191	61	52	100	67	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	163	100	117	100	0	.	0	.	0	.	36	100	166	100	0	.	183	59	0	.	0	.	0	.	0	.	38	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1849	22
	total	3462	19	2241	23	2133	26	5700	19	3158	29	5641	22	21007	10	16932	10	17920	10	10833	12	1046	35	1615	31	2743	25	177	.	1662	34	4812	21	5363	19	4671	20	614	53	6159	19	8558	14	5255	17	7480	16	14576	11	13504	10	12284	12	5290	21	6759	18	7893	18	1656	32	2911	24	1505	33	1512	32	3653	22	2756	27	1835	31	4620	21	3385	25	2846	26	4492	21	4442	23	6688	19	5169	18	3113	24	2368	25	2166	28	2467	24	3809	22	3086	24	3234	25	3056	25	3018	28	3448	21	1557	30	3551	21	2960	25	1731	23	2401	23	3493	17	3354	18	2485	22	3435	21	2479	28	1472	39	2559	28	2723	27	2512	27	2721	30	1776	36	679	42	3471	22	722	51	944	36	2750	21	2138	27	2696	24	2732	25	1578	36	1397	37	883	55	1099	45	10503	12	7963	14	10012	13	1297	33	2534	29	1915	26	3819	22	3900	21	2284	29	1958	30	1960	27	0	.	387165	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. 2297036. Birmensdorf, Swiss Federal Research Institute WSL

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