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net increment #80
Increase in the stemwood volume between two inventories of all surviving trees and shrubs with a diameter at breast height (dbh) ≥12 cm with bark, the stemwood volume of all ingrowing trees and shrubs with bark, and the modelled increase in the stemwood volume of the losses with bark during half the inventory period minus the volume of mortality. The net increment thus corresponds to the increment (=gross increment) minus the volume of mortality.

site quality #894
Production capacity of the site in kilograms per hectare and year based on the total mean increment (TMI) according to Keller (1978) - in four classes. Low: TMI ≤1500 kg/(ha/year), moderate: TMI 1501-3000 kg/(ha/year), good: TMI 3001-4500 kg/(ha/year), very good: TMI >4500 kg/(ha/year).

main tree species #90
Type of trees and shrubs ≥12 cm in diameter at breast height (dbh) with the ten most common species or species groups in Switzerland ('main tree species') categorised, and the classes 'other conifers' and 'other broadleaves' for the remaining species. The main tree species are: spruce (Picea spp.), fir (Abies spp.), pine (Pinus sylvestris, P. nigra, P. strobus, P. mugo subsp. uncinata), larch (Larix spp.), Arolla pine (Pinus cembra), beech (Fagus sylvatica), maple (Acer spp.), ash (Fraxinus spp.), oak (Quercus spp.) and chestnut (Castanea sativa). 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 NFI4/NFI5 #2282
Forest that was less than two-thirds covered with shrubs in both NFI4 (2009-2017) and NFI5 (2018-2026) and is accessible 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.

NFI4–NFI5
net increment
site quality · main tree species
regional demarcation: production region
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
change 2009/17–2018/26
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
site quality	main tree species	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
low	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	11	110	1	683	71	29	171	23	23	35	277	17
	fir	43	31	6	58	-4	129	23	46	0	.	69	27
	pine	4	126	3	53	4	82	7	80	-1	100	17	50
	larch	0	100	0	.	0	.	68	26	36	33	103	20
	Arolla pine	0	.	0	.	0	.	24	35	0	100	24	35
	other conifers	1	70	1	62	0	96	0	543	0	.	2	57
	beech	28	54	47	34	5	228	13	80	0	.	92	29
	maple	6	68	-2	86	5	71	9	37	0	.	17	37
	ash	0	548	-1	176	8	90	-1	138	0	.	6	141
	oak	2	77	-1	176	3	101	2	56	0	.	7	61
	sweet chestnut	0	.	0	.	0	.	1	76	0	.	1	76
	other broadleaves	-3	123	0	399	4	56	3	164	0	74	4	163
	indeterminable	0	0	0	0	0	.	0	0	0	0	0	0
	total	93	29	53	36	96	28	319	15	58	29	619	11
moderate	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14	150	1	408	175	19	769	10	65	55	1022	9
	fir	78	24	5	50	91	21	63	24	31	40	269	13
	pine	0	100	0	.	0	100	31	51	-3	142	28	59
	larch	0	.	0	.	1	84	161	16	65	34	226	15
	Arolla pine	0	.	0	.	0	.	24	39	0	100	25	38
	other conifers	0	100	0	.	2	96	-1	120	0	.	2	145
	beech	30	32	4	68	55	25	31	44	47	35	169	16
	maple	7	63	1	56	10	40	40	33	7	55	64	23
	ash	1	189	0	.	5	58	7	153	3	56	17	71
	oak	0	.	0	.	0	100	3	75	0	1234	3	104
	sweet chestnut	0	.	0	.	0	.	1	82	6	138	6	126
	other broadleaves	2	47	0	100	2	339	21	80	5	333	30	84
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0
	total	130	25	12	64	342	13	1150	8	227	24	1861	7
good	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	113	26	159	18	370	14	251	16	44	32	937	8
	fir	169	17	131	18	325	15	52	36	-1	552	676	10
	pine	1	520	7	62	7	105	1	104	0	.	16	70
	larch	2	59	0	11382	0	72	0	2339	11	72	13	75
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	6	62	23	38	2	65	0	189	2	92	33	30
	beech	83	26	153	19	155	19	67	26	74	25	532	10
	maple	32	32	56	20	43	31	22	40	12	49	164	14
	ash	-8	143	40	49	3	495	5	193	15	77	53	57
	oak	3	93	29	27	3	52	3	92	1	921	39	30
	sweet chestnut	0	.	0	.	0	.	0	381	3	712	3	722
	other broadleaves	7	81	19	32	23	29	5	279	50	44	104	27
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	409	14	618	11	932	10	405	14	211	20	2570	6
very good	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	65	42	589	9	73	30	8	67	0	.	733	9
	fir	115	25	319	12	114	23	2	189	0	.	549	10
	pine	9	113	9	73	1	100	0	100	0	.	18	63
	larch	2	114	28	35	4	47	3	80	0	.	37	28
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	12	66	29	34	0	83	0	.	0	.	41	30
	beech	143	16	359	10	36	49	9	95	4	86	552	8
	maple	50	27	69	21	18	93	9	52	0	.	146	18
	ash	14	56	85	22	4	388	7	58	1	75	111	23
	oak	13	78	48	28	3	82	0	1409	1	89	64	26
	sweet chestnut	0	.	0	100	0	.	-3	100	-1	100	-4	81
	other broadleaves	10	54	82	31	9	53	-2	462	3	92	102	27
	indeterminable	0	0	0	0	0	0	0	.	0	.	0	0
	total	434	13	1616	5	261	18	33	48	8	65	2349	5
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	203	23	751	8	688	10	1199	8	132	30	2968	5
	fir	404	11	461	10	525	11	140	19	31	43	1564	6
	pine	14	94	19	41	12	66	38	44	-4	103	80	31
	larch	4	69	28	39	5	39	230	13	112	24	379	11
	Arolla pine	0	.	0	.	0	.	49	26	0	87	49	26
	other conifers	19	44	52	25	5	52	-1	107	2	92	77	21
	beech	285	12	563	8	251	15	122	21	126	20	1345	6
	maple	95	19	123	15	75	29	79	21	18	37	390	10
	ash	6	233	124	22	20	102	18	86	19	61	187	22
	oak	19	58	76	20	9	47	8	49	2	451	113	19
	sweet chestnut	0	.	0	100	0	.	-2	190	8	300	7	371
	other broadleaves	17	53	101	26	38	28	27	85	58	49	239	20
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	1067	8	2299	4	1629	6	1907	6	504	14	7398	3

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. 1299493. Birmensdorf, Swiss Federal Research Institute WSL

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