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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
growing stock (stemwood)
site quality · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
site quality	main tree species	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	.
	spruce	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	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	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	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	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	.
	indeterminable	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	.
low	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1000	25	8	100	222	63	163	57	159	100	1228	41	4850	18	1752	28	2794	21	2036	27	2121	33	1705	32	5435	17	830	34	24303	8
	fir	1828	22	59	95	0	.	78	61	220	94	119	96	96	49	15	89	679	48	192	68	117	85	67	85	0	.	0	.	3469	17
	pine	365	34	89	63	0	.	108	54	74	100	0	.	221	81	0	.	48	72	74	79	36	74	28	74	404	31	22	100	1470	19
	larch	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	99	100	1715	24	2985	18	1029	21	5828	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	804	27	1103	22	0	100	1908	17
	other conifers	26	56	30	100	0	.	1	100	7	100	0	.	9	90	0	.	10	100	12	71	3	100	0	.	29	41	0	.	127	31
	beech	3552	17	503	43	233	100	901	40	246	56	319	52	1032	41	408	69	666	40	284	69	226	52	203	73	0	.	0	.	8575	12
	maple	268	30	42	44	4	100	25	74	25	71	131	40	240	49	30	53	198	31	21	102	117	42	119	47	0	.	0	.	1220	15
	ash	118	46	7	83	0	.	46	60	48	75	80	100	34	87	0	.	16	100	3	100	11	81	93	87	0	.	0	.	455	30
	oak	107	62	70	78	0	.	95	67	47	82	1	100	62	92	0	.	18	74	50	100	2	100	17	71	0	.	0	.	468	29
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	71	0	.	0	.	60	71
	other broadleaves	69	44	27	58	0	.	43	64	85	79	26	60	63	47	14	61	74	52	15	51	19	81	165	51	6	59	4	72	611	21
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	7334	14	835	35	459	65	1461	36	912	43	1904	32	6609	16	2219	27	4504	18	2687	26	2750	28	4975	17	9962	12	1886	21	48495	6
moderate	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4338	14	0	.	505	51	9	100	31	100	6312	17	3031	21	4351	20	8562	15	1434	41	1760	32	12968	10	19862	9	8775	12	71938	4
	fir	3475	19	0	.	270	60	28	100	0	.	1852	31	935	31	1099	29	1239	31	532	64	65	77	1255	30	498	40	1765	26	13013	10
	pine	21	100	0	.	0	.	0	.	0	.	0	.	0	.	8	100	32	55	116	100	22	100	1890	19	2313	19	142	54	4544	13
	larch	0	.	0	.	0	.	0	.	0	.	6	100	1	100	0	.	237	44	0	.	74	97	7755	13	4089	14	5035	13	17197	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	243	36	665	30	8	100	929	23
	other conifers	7	100	0	.	0	.	0	.	0	.	0	.	1	100	7	100	34	100	0	.	0	0	13	58	13	64	0	.	74	50
	beech	1864	18	0	.	147	69	35	100	45	100	324	36	873	33	1024	30	474	47	156	61	476	48	348	43	838	26	2304	19	8907	9
	maple	453	23	0	.	67	59	23	100	0	.	295	35	166	36	255	32	395	32	132	63	339	46	246	38	221	32	177	39	2770	11
	ash	63	51	0	.	0	.	0	.	0	.	136	45	104	51	88	54	202	51	131	88	186	55	315	37	131	46	83	59	1439	17
	oak	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	8	100	0	.	0	.	195	42	64	100	194	41	467	28
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	5	100	698	34	714	34
	other broadleaves	77	33	0	.	0	.	1	100	0	.	90	34	39	52	50	65	125	35	38	63	69	61	924	21	720	26	955	27	3088	12
	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
	total	10298	12	0	.	990	40	96	100	76	100	9015	15	5155	18	6882	18	11321	13	2538	35	2991	26	26162	8	29418	7	20138	7	125080	3
good	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7516	12	882	30	1327	38	1333	30	2519	20	3468	21	8126	12	10398	13	2057	26	2748	26	1888	27	686	49	5396	18	2991	40	51335	5
	fir	6062	15	174	44	993	34	2656	24	1811	23	1202	31	7924	12	3809	17	826	39	1375	34	626	43	320	62	899	31	303	61	28978	6
	pine	692	36	302	38	159	100	157	64	439	37	0	.	500	54	62	87	0	.	104	65	0	.	0	.	0	.	0	.	2415	19
	larch	12	100	36	59	79	78	55	92	212	44	20	100	0	.	0	.	10	100	0	.	101	71	0	.	196	71	725	39	1446	24
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	27	62	76	87	6	100	510	56	76	76	0	.	82	98	10	71	2	100	0	.	-19	104	0	.	0	.	82	98	853	37
	beech	4812	12	1965	22	982	29	2268	20	3614	17	1071	33	4110	18	3296	16	1301	31	946	28	760	34	316	56	473	58	4126	15	30039	6
	maple	1085	20	295	35	183	37	203	33	928	26	194	43	766	23	1236	20	244	32	243	34	340	36	103	57	211	52	302	33	6334	8
	ash	496	36	257	44	444	43	664	47	770	28	227	65	547	55	938	28	262	49	370	42	125	52	45	71	142	65	799	33	6087	12
	oak	490	39	324	50	282	46	324	36	441	29	49	81	56	77	25	60	23	100	55	74	0	.	20	100	85	100	781	24	2955	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	26	100	18	100	4714	14	4763	14
	other broadleaves	560	27	226	51	153	39	200	41	209	40	81	42	317	24	449	30	328	43	231	41	150	48	22	53	382	39	3999	13	7308	9
	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
	total	21751	8	4536	17	4610	21	8371	16	11019	13	6312	17	22428	9	20222	10	5058	20	6074	19	3971	22	1537	35	7802	15	18821	9	142513	3
very good	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2909	19	1607	26	1994	27	6303	14	8427	11	202	66	2123	28	1146	39	147	61	79	71	187	69	0	.	0	.	0	.	25124	7
	fir	3877	17	770	36	1024	34	5122	15	3296	18	67	66	2632	22	1635	32	11	100	28	94	217	69	0	.	0	.	0	.	18679	8
	pine	315	39	161	50	298	39	407	36	839	23	0	.	0	.	41	100	0	.	40	100	0	.	0	.	0	.	0	.	2102	15
	larch	63	74	44	72	248	45	82	58	399	33	0	.	18	100	192	48	172	63	0	.	0	.	0	.	0	.	0	.	1217	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	11	65	294	86	56	100	280	58	497	70	0	.	0	.	19	57	0	.	0	.	0	.	0	.	0	.	0	.	1157	40
	beech	4759	14	3595	16	2756	22	3934	14	6121	12	109	89	1829	27	1800	29	143	52	239	79	673	39	53	71	0	.	208	78	26220	6
	maple	1078	18	589	25	118	42	462	23	1257	17	39	100	195	43	359	40	162	47	53	85	35	59	8	100	0	.	0	.	4355	9
	ash	731	24	632	30	1098	31	858	29	2152	18	196	84	163	55	612	36	63	61	0	.	45	74	11	89	0	.	30	74	6591	10
	oak	623	31	344	48	791	34	439	31	1559	31	12	100	0	.	56	84	0	.	0	.	0	.	45	78	0	.	42	80	3909	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	100	0	.	0	0	68	100
	other broadleaves	288	40	348	32	948	33	724	24	1426	18	45	100	57	43	156	43	324	88	50	83	64	62	65	76	0	.	46	72	4542	12
	indeterminable	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	14654	10	8384	14	9332	14	18609	9	25973	7	671	46	7018	17	6015	20	1022	40	490	61	1220	35	249	60	0	.	327	62	93963	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15763	7	2497	19	4049	19	7809	12	11136	10	11210	12	18130	8	17646	10	13561	11	6297	17	5955	17	15359	9	30693	7	12596	12	172699	2
	fir	15241	8	1003	29	2287	22	7884	12	5327	14	3240	21	11587	10	6558	13	2754	21	2127	28	1025	32	1642	26	1397	24	2068	24	64140	4
	pine	1394	22	552	27	457	43	672	28	1352	19	0	.	721	45	111	61	80	48	334	45	58	60	1918	18	2717	17	165	49	10531	8
	larch	75	64	81	47	326	39	137	50	611	26	25	81	19	94	192	48	419	36	0	.	274	52	9470	12	7270	11	6789	11	25688	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	1047	22	1768	18	9	94	2836	13
	other conifers	71	34	399	66	62	91	791	41	580	61	0	.	92	88	36	41	46	77	12	71	-16	122	13	58	42	35	82	98	2211	25
	beech	14987	6	6063	12	4118	17	7138	11	10026	9	1823	23	7845	13	6528	13	2583	20	1624	24	2136	21	920	30	1310	27	6638	11	73741	3
	maple	2885	10	925	19	373	25	713	18	2211	14	659	22	1368	17	1879	15	1000	18	449	28	831	25	475	26	432	30	479	25	14678	5
	ash	1409	18	897	24	1542	25	1568	25	2970	15	640	38	847	38	1638	21	543	31	504	38	367	34	463	31	274	40	913	30	14573	7
	oak	1219	23	737	32	1073	28	857	22	2047	24	62	67	124	58	81	61	49	57	105	61	2	100	276	33	149	71	1017	20	7799	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	165	51	23	82	5412	13	5605	12
	other broadleaves	995	19	601	26	1101	29	969	20	1720	16	243	27	476	18	668	23	852	38	334	32	301	31	1175	19	1108	21	5004	11	15548	6
	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
	total	54037	4	13755	10	15390	11	28538	7	37979	5	17902	10	41210	6	35338	7	21905	9	11788	14	10932	13	32923	7	47182	5	41172	4	410050	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. 1195161. Birmensdorf, Swiss Federal Research Institute WSL

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