Selection from the list of topics

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: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
type of assortment	tree species (5 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	±%
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	.
	spruce	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	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	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	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	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	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	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	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	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	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	.	0	0	0	0	0	.	0	0	0	.	0	.	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
coniferous wood	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	.
	spruce	0	.	0	.	10	100	290	90	0	.	427	69	0	.	0	.	448	56	0	.	0	.	0	.	10	100	0	.	375	56	0	.	0	.	0	.	17	100	28	100	0	.	0	.	259	59	0	.	0	.	1864	29
	fir	0	.	0	.	0	.	79	72	0	.	130	100	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	42	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	269	55
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	92	70	0	.	0	.	262	39
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	10	100	369	82	0	.	557	58	0	.	0	.	598	44	0	.	0	.	0	.	28	100	0	.	417	52	0	.	0	.	0	.	36	100	28	100	0	.	0	.	352	57	0	.	0	.	2395	25
coniferous shortwood	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	.
	spruce	2054	21	2104	32	1590	31	23044	7	390	42	5287	16	1	100	3550	22	30166	6	68	85	6304	14	23	100	1174	39	2887	22	7616	14	988	30	1271	23	5205	16	1759	23	6517	19	4106	20	7037	13	11607	10	984	36	4525	15	130256	3
	fir	1004	30	376	41	1097	39	10322	10	204	39	1438	29	0	.	476	44	1707	23	166	67	4518	15	226	100	265	58	676	31	2690	19	128	69	1077	23	1921	24	960	34	1144	29	953	31	3114	19	743	34	341	41	1582	21	37128	5
	other conifers	541	28	0	.	10	74	1318	22	517	55	30	72	72	100	25	92	10462	8	26	100	288	45	0	.	4	100	224	75	572	61	344	42	355	28	135	56	379	38	3686	13	249	50	204	36	9735	9	28	100	991	20	30195	5
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	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	0	0	.	0	0	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	3599	17	2480	31	2697	28	34684	6	1111	32	6755	15	73	100	4051	21	42335	5	260	65	11110	12	249	100	1443	34	3786	19	10879	12	1461	26	2703	16	7261	15	3098	19	11346	12	5308	18	10355	12	22085	7	1354	34	7098	13	197580	2
coniferous mediumwood	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	.
	spruce	11	100	0	.	0	.	1271	26	181	68	468	56	0	.	0	.	438	44	259	55	653	38	61	76	362	71	737	47	1319	33	60	100	68	59	0	.	198	74	212	61	0	.	477	51	1860	25	0	.	271	55	8906	11
	fir	124	100	0	.	0	.	1721	27	174	52	0	.	0	.	0	.	123	65	66	73	1134	29	65	100	27	100	233	59	396	40	0	.	116	46	0	.	34	100	23	100	0	.	346	41	616	43	0	.	86	67	5282	13
	other conifers	0	.	0	.	0	.	173	43	11	100	41	82	0	.	0	.	297	55	58	100	34	100	83	71	0	.	1	100	193	52	38	77	53	60	0	.	0	.	303	48	0	.	100	53	631	43	0	.	40	82	2055	19
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	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	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	135	92	0	.	0	.	3165	22	366	53	509	52	0	.	0	.	857	39	382	47	1822	29	208	54	389	71	971	45	1908	29	97	77	238	43	0	.	232	71	538	42	0	.	922	34	3107	22	0	.	397	42	16242	9
coniferous longwood	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	.
	spruce	392	63	349	72	342	68	2788	18	133	87	1695	30	2	100	0	.	99	100	1880	25	52	100	4052	15	0	.	185	100	630	54	312	70	287	94	0	.	506	47	0	.	0	.	4193	15	711	52	0	.	531	47	19139	7
	fir	299	60	113	100	237	89	2552	18	145	99	715	39	3	100	0	.	0	.	3329	20	51	100	3400	17	0	.	295	100	435	51	28	100	102	72	0	.	167	61	0	.	0	.	2718	17	104	91	0	.	107	52	14799	8
	other conifers	0	.	0	.	0	.	534	53	27	71	295	58	12	100	0	.	12	100	235	41	187	100	70	72	0	.	0	.	0	.	7	100	0	.	0	.	14	100	0	.	0	.	356	37	16	75	0	.	13	100	1779	23
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	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	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0
	total	691	50	462	71	579	61	5874	15	305	89	2706	25	17	77	0	.	110	100	5444	17	290	100	7522	12	0	.	480	100	1065	42	346	65	389	73	0	.	688	43	0	.	0	.	7266	12	831	47	0	.	651	45	35717	6
broadleaf roundwood	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	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	4334	15	274	47	424	34	12476	9	2411	19	2874	19	0	.	869	29	1786	22	2900	17	3479	16	1216	18	441	42	1281	30	5485	14	1878	21	4386	15	1754	23	1538	20	4862	12	964	34	6218	12	920	28	299	44	3860	13	66928	3
	other broadleaves	2393	16	16	72	527	34	5119	10	1046	22	1995	19	338	55	835	31	2884	15	2001	22	1402	25	728	22	677	47	310	41	3369	14	441	28	1551	18	1067	26	2402	22	10047	8	614	29	4507	14	2113	14	197	45	3264	13	49842	3
	indeterminable	0	.	0	.	1	100	16	91	12	93	0	.	0	.	0	.	46	54	1	100	1	100	0	.	0	.	1	100	2	100	0	.	4	100	0	.	1	100	4	58	0	.	24	61	1	100	0	.	5	100	118	29
	total	6726	13	289	47	952	30	17611	7	3469	17	4868	16	338	55	1704	23	4716	13	4902	14	4882	15	1944	15	1118	35	1591	26	8856	12	2319	20	5940	13	2821	20	3942	17	14914	6	1578	27	10749	10	3035	14	496	41	7129	11	116888	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	.
	spruce	2457	20	2453	29	1941	28	27393	7	703	33	7878	13	3	75	3550	22	31150	6	2206	22	7010	13	4136	15	1546	34	3809	19	9941	12	1360	27	1627	25	5205	16	2480	20	6757	18	4106	20	11706	10	14437	9	984	36	5327	14	160166	2
	fir	1427	26	489	39	1334	36	14674	8	523	36	2283	22	3	100	476	44	1830	22	3561	19	5703	13	3691	17	310	51	1203	32	3562	16	156	59	1295	21	1921	24	1161	30	1167	28	953	31	6178	12	1462	26	341	41	1775	19	57477	4
	other conifers	541	28	0	.	10	74	2025	20	555	52	367	48	84	87	25	92	10921	8	320	36	509	45	153	50	4	100	225	74	766	47	389	38	407	26	135	56	413	35	3989	12	249	50	659	24	10475	9	28	100	1044	19	34291	4
	beech	4334	15	274	47	424	34	12476	9	2411	19	2874	19	0	.	869	29	1786	22	2900	17	3479	16	1216	18	441	42	1281	30	5485	14	1878	21	4386	15	1754	23	1538	20	4862	12	964	34	6218	12	920	28	299	44	3860	13	66928	3
	other broadleaves	2393	16	16	72	527	34	5119	10	1046	22	1995	19	338	55	835	31	2884	15	2001	22	1402	25	728	22	677	47	310	41	3369	14	441	28	1551	18	1067	26	2402	22	10047	8	614	29	4507	14	2113	14	197	45	3264	13	49842	3
	indeterminable	0	0	0	.	1	100	16	91	12	93	0	0	0	.	0	0	46	54	1	100	1	100	0	0	0	0	1	100	2	100	0	.	4	100	0	0	1	100	4	58	0	0	24	61	1	100	0	.	5	100	118	29
	total	11151	11	3232	28	4237	23	61703	5	5249	16	15396	10	428	52	5755	18	48616	5	10989	12	18104	10	9923	11	2978	25	6829	17	23124	9	4223	18	9269	12	10082	13	7995	13	26826	6	6886	16	29293	7	29409	6	1849	30	15275	9	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. 1364293. Birmensdorf, Swiss Federal Research Institute WSL

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