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volume of bolewood (HG 2010) #211
Wood volume of the stem without bark or stump ≥7 cm in diameter (limit for merchantable wood) of all trees and shrubs with a diameter at breast height (dbh) ≥12 cm, based on the stem-form functions according to Kaufmann (2001). The definition of the assortments is based on the 2010 edition of the Swiss trading customs for raw timber (HG 2010).

type of assortment #2221
Sorting of roundwood into five length classes (4 for conifers, 1 for broadleaves) on the basis of the 2010 and 2021 editions of the Swiss Trading Customs for Raw Timber (HG 2010, Waldwirtschaft Schweiz et al. 2010). Reference: Forest Service Survey (MID 2020: Längensortierung des Nadelrundholzes)

tree species (5 classes) #1157
Type of trees and shrubs ≥12 cm in diameter at breast height (dbh). The three most common species or species groups in Switzerland (spruce - Picea spp.; fir - Abies spp.; beech - Fagus sylvatica) are in individual classes, while the remaining species are classified as «other conifers» or «other broadleaves». Reference: Field Survey (MID 50: Baumart)

production region #49
Demarcation of Switzerland into five regions (Jura, Plateau, Pre-Alps, Alps and Southern Alps) with relatively uniform growth and timber production conditions. The production regions were established by the Federal Office of Forestry long before the first National Forest Inventory (NFI1, 1983-1985). With a small exception along Lake Geneva, the boundaries of the production regions still follow the municipal boundaries of the time. Unlike the NFI, the Forestry Statistics of the Federal Statistical Office don't use the production regions as demarcation but rather the forestry zones, whose boundaries are somewhat different.

accessible forest without shrub forest #434
Forest of which less than two-thirds is covered with shrubs that can be accessed on foot.

1.4 km grid, subgrids 1-5 #1746
Sub-grids 1, 2, 3, 4 and 5 of the field surveys on the sampling grid with a mesh size of 1.4 km (base grid).

Estimates and standard error of the estimates

The results published in the NFI tables are estimates for variables in the Swiss forest (population parameters) whose true values are not known and must therefore be extrapolated (estimated) from the NFI sample data.

The extrapolations carry uncertainties. However, the accuracy of the extrapolations can be reliably estimated from the NFI sample, which is designed to be a random sample. To this end, all NFI tables include a second figure in addition to the estimate itself, namely the standard error of the estimate.

In most tables the percent standard error is reported (±%), but the absolute standard error (±) is also reported occasionally, especially for estimated percentages. The relationship between absolute and percent standard error is as follows:

percent standard error = absolute standard error / estimate × 100

absolute standard error = percent standard error × estimate / 100

Information on the forest road network is based on a full survey of all forest roads. In this case, there is no need to specify a standard error because there is no uncertainty caused by sampling.

Confidence interval of the estimate

With the estimate and the standard error of the estimate, the limits of the so-called confidence interval of the estimate can be calculated as:

lower limit = estimate – tQ × absolute standard error

upper limit = estimate + tQ × absolute standard error

If the simple standard error is used for the calculation (tQ = 1), then the 68% confidence interval is formed. In this case, the true value of the population parameter has a 68% probability of lying within this confidence interval of the estimate. If the double standard error is used (tQ = 2), then there is a 95% probability that the true value lies within this 95% confidence interval.

Significance of the estimate

The confidence interval can be used to statistically test whether the estimated population parameter is larger or smaller than a specific reference or target value, or whether two estimated population parameters truly differ (in the real population). For practical applications, proceed as follows: if a reference value lies outside the confidence interval, the estimated population value can be interpreted as being significantly different from it; if it lies within the confidence interval, the difference between the estimated population parameter and the reference value can be interpreted as random or not significant. When two population parameters are compared, they can be interpreted as differing significantly if their confidence intervals do not overlap.

Dealing with missing values

When calculating a results table, data are not always available for all combinations of characteristics^* of the classification variables and regional demarcation applied. In most cases, this indicates that the attribute estimated with the respective target variable does not occur or occurs only very rarely. Usually, the value 0 is used (imputed). However, as this value is not based on direct measurements, the associated standard error is represented with a full stop [.]. If the imputed value of 0 is referred to in the calculation, e.g. in the case of percentages or certain change estimates, no value can be entered. In this case, the estimated value and standard error are both represented with a full stop [.].

For example, no Arolla pines have been found and measured in the Swiss Plateau to date (growing stock of Arolla pines by production region). The values can be assumed to be missing because Arolla pine does not occur in the Swiss Plateau and therefore the growing stock there must be 0.

^* e.g. ‘Arolla pine’ characteristic of the classification variable ‘tree species’ and ‘Swiss plateau’ characteristic of the ‘regional demarcation’

Changes

There are two types of changes in the NFI:

The first type involves specific target variables relating to components of change, such as increment, fellings, mortality and losses. These target variables are only available for two consecutive inventory cycles, e.g. NFI3–NFI4. In the evaluation of components of change, the classification variable characteristic of the second inventory cycle is assigned to the first inventory cycle. These evaluations therefore do not take into account potential changes in a classification variable characteristic from the earlier to the later inventory (e.g. from private to public ownership).

With the second type of change, the difference in target variables, such as number of stems, growing stock or forest area, is used to assess the change between two inventory cycles. Target variables are generally used to represent states, such as that in NFI4, but can also indicate the net change between any two inventory cycles, e.g. NFI1–NFI4. In evaluations of change using these target variables, the change in a classification variable characteristic is considered in the analysis. Consequently, one can detect, for example, that the forest area without shrub forest increased between the two inventory cycles. This only has an effect on classification variables whose characteristic (class) can actually change, e.g. the affiliation with a forest area or the tree condition.

NFI5
volume of bolewood (HG 2010)
type of assortment · tree species (5 classes)
regional demarcation: production region
unit: 1000 m³
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
type of assortment	tree species (5 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	0	0	0	0	0	0	0	0	0	0	0
	fir	0	0	0	0	0	0	0	0	0	0	0	0
	other conifers	0	0	0	0	0	0	0	0	0	0	0	0
	beech	0	0	0	0	0	0	0	0	0	0	0	0
	other broadleaves	0	0	0	0	0	0	0	0	0	0	0	0
	indeterminable	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
coniferous wood	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	49	74	842	49	946	36	28	100	1864	29
	fir	0	.	46	100	223	63	0	.	0	.	269	55
	other conifers	0	.	19	100	0	.	243	41	0	.	262	39
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	114	75	1065	43	1188	31	28	100	2395	25
coniferous shortwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5686	11	15353	7	37956	5	60293	4	10969	12	130256	3
	fir	3482	15	9726	9	15467	7	6704	12	1749	24	37128	5
	other conifers	1450	24	3311	15	694	32	19506	6	5234	10	30195	5
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	0
	total	10618	9	28390	6	54117	4	86503	3	17952	8	197580	2
coniferous mediumwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	913	29	1255	29	3355	20	3170	19	212	61	8906	11
	fir	580	28	1030	28	2503	21	1145	28	23	100	5282	13
	other conifers	266	36	201	39	77	70	1208	28	303	48	2055	19
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	1759	22	2487	21	5936	17	5523	16	538	42	16242	9
coniferous longwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	10767	9	4940	17	2347	25	1085	40	0	.	19139	7
	fir	10427	9	2708	18	1265	30	399	78	0	.	14799	8
	other conifers	696	32	839	33	215	98	28	60	0	.	1779	23
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	0	0	.	0	0	0	.	0	.	0	0
	total	21890	6	8488	13	3828	22	1511	42	0	.	35717	6
broadleaf roundwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	18861	5	19922	5	14921	7	7836	9	5388	11	66928	3
	other broadleaves	8424	7	14211	7	7158	8	8779	8	11270	7	49842	3
	indeterminable	19	61	6	87	5	47	68	42	20	78	118	29
	total	27304	4	34139	4	22085	6	16683	6	16678	5	116888	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17366	6	21597	6	44500	4	65494	3	11209	12	160166	2
	fir	14489	7	13510	8	19458	6	8248	11	1772	24	57477	4
	other conifers	2412	17	4371	13	986	32	20984	5	5537	10	34291	4
	beech	18861	5	19922	5	14921	7	7836	9	5388	11	66928	3
	other broadleaves	8424	7	14211	7	7158	8	8779	8	11270	7	49842	3
	indeterminable	19	61	6	87	5	47	68	42	20	78	118	29
	total	61572	2	73617	2	87030	3	111408	2	35195	4	368822	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. 1364297. Birmensdorf, Swiss Federal Research Institute WSL

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