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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.

NFI4
growing stock (stemwood)
diameter at breast height (main diameter classes) · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		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
diameter at breast height (main diameter classes)	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	.
12-15 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	333	16	74	31	118	32	208	15	369	18	137	19	321	12	171	16	225	14	117	31	84	19	348	12	699	10	250	27	3452	5
	fir	299	12	19	31	48	23	145	25	95	27	64	27	187	17	80	30	33	46	28	32	43	62	23	27	8	42	40	37	1112	7
	pine	9	61	1	292	0	0	6	100	5	64	0	.	7	61	1	100	16	51	8	53	1	100	92	31	104	18	3	78	252	15
	larch	1	100	2	100	14	93	3	78	2	72	5	90	2	100	4	84	7	51	0	.	5	52	111	19	100	30	85	21	339	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	1	100	0	.	23	44	61	44	2	65	87	33
	other conifers	16	43	4	61	2	71	9	39	10	48	0	.	2	60	7	45	3	64	10	58	1	100	5	64	22	28	2	73	92	15
	beech	323	12	121	22	116	36	122	21	261	19	38	48	225	23	108	20	83	33	14	24	46	28	58	59	43	38	364	12	1923	6
	maple	103	21	53	25	41	24	58	31	96	43	27	25	47	25	66	32	58	22	23	49	25	32	47	26	27	31	49	22	720	9
	ash	61	23	24	47	73	49	46	61	112	36	-4	352	47	26	32	28	30	37	35	41	13	61	22	28	22	43	109	18	623	12
	oak	36	39	1	100	12	53	5	69	10	36	1	100	0	100	1	100	5	71	2	71	0	100	35	27	0	100	35	25	143	15
	sweet chestnut	0	.	0	.	2	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	87	0	.	180	23	188	22
	other broadleaves	128	13	45	26	75	27	42	26	109	18	44	27	77	20	34	56	111	19	64	31	25	60	226	13	206	17	513	13	1700	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
	total	1308	6	344	13	501	15	645	11	1068	10	311	14	915	9	504	11	571	10	301	17	244	17	993	8	1293	8	1632	7	10631	3
16-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1150	13	220	26	393	21	600	17	1126	14	575	16	1091	11	783	13	707	14	306	18	321	23	1240	10	2364	8	662	12	11537	3
	fir	938	10	79	35	121	24	528	19	325	25	231	23	688	12	251	19	70	28	135	29	38	45	175	28	51	32	116	31	3747	5
	pine	22	72	21	54	0	.	34	78	21	45	0	0	39	64	2	100	24	74	14	55	17	63	352	20	340	17	9	71	896	11
	larch	9	100	3	100	14	71	7	61	18	51	9	71	6	100	14	100	10	46	0	.	19	49	526	13	274	20	313	17	1222	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	2	100	0	.	109	30	123	18	2	100	240	17
	other conifers	20	107	6	72	11	59	24	47	44	46	0	.	8	77	9	116	6	100	2	100	4	58	7	67	32	34	0	.	174	22
	beech	1223	9	319	16	330	24	412	14	701	14	150	32	591	13	451	16	226	22	126	27	161	24	163	32	111	29	1106	12	6070	4
	maple	356	14	132	25	119	30	128	25	418	18	134	23	174	18	217	25	183	23	65	31	90	24	122	20	30	61	140	21	2308	6
	ash	184	20	134	22	168	29	67	24	276	18	66	33	81	28	191	25	88	36	65	36	52	35	146	35	81	35	236	21	1837	7
	oak	99	31	42	50	98	34	25	51	56	34	3	100	9	77	3	100	30	47	0	.	0	.	73	27	10	55	142	23	590	12
	sweet chestnut	0	.	0	.	4	100	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	71	0	.	939	13	956	13
	other broadleaves	228	15	107	37	136	26	156	26	316	15	79	39	119	16	158	30	127	25	132	30	84	28	436	13	388	20	1247	9	3712	5
	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
	total	4230	5	1063	11	1395	12	1983	9	3300	7	1247	11	2810	6	2079	8	1472	10	847	13	787	14	3361	7	3803	6	4912	5	33289	2
24-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3064	9	455	26	1006	21	1420	15	2885	11	2163	13	3182	9	2515	11	2223	12	1119	16	1088	21	3253	9	5736	7	1826	12	31936	3
	fir	2131	10	235	27	284	26	957	16	676	18	587	23	1602	12	870	15	202	27	244	32	133	35	291	26	137	32	222	32	8571	5
	pine	335	29	102	43	68	55	48	60	205	30	0	.	120	46	5	100	14	78	123	43	70	63	683	17	619	18	133	58	2526	10
	larch	25	77	19	59	73	49	54	64	143	42	0	.	0	.	3	100	54	45	0	.	61	46	1349	12	883	13	1145	12	3809	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	24	100	7	100	0	.	13	100	0	.	357	20	423	20	5	100	829	14
	other conifers	39	64	53	39	30	100	28	52	43	68	0	.	4	100	14	72	24	61	0	.	-12	100	0	0	9	58	0	.	230	26
	beech	4218	9	1242	14	888	14	1056	15	1733	12	428	26	1769	12	1315	13	530	21	300	28	396	22	348	25	240	26	1731	11	16195	4
	maple	844	13	237	25	221	40	250	22	662	17	284	24	511	18	471	20	351	22	198	42	215	30	102	30	109	44	171	26	4624	6
	ash	485	19	277	26	490	23	315	22	632	18	218	34	289	35	508	19	127	44	95	44	60	43	143	26	71	31	462	22	4171	7
	oak	431	27	223	30	159	33	137	33	239	23	20	77	22	59	12	100	32	78	5	100	0	.	56	46	12	76	352	21	1701	11
	sweet chestnut	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	71	3	100	1308	13	1353	13
	other broadleaves	368	16	219	31	169	37	180	30	528	17	28	39	109	26	184	26	257	29	49	35	66	44	411	20	194	28	1693	10	4455	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
	total	11941	5	3061	9	3400	10	4444	8	7745	6	3728	11	7634	6	5903	7	3814	9	2146	13	2078	14	7023	6	8435	5	9048	4	80400	2
36-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6660	7	938	20	1528	17	2463	13	4527	9	4352	10	7045	7	5603	9	4561	10	1819	16	2139	15	4983	8	10661	6	3553	11	60831	2
	fir	5214	8	491	28	648	21	1972	13	1507	13	1101	17	3265	9	2312	12	562	24	462	26	361	25	513	24	425	22	476	22	19310	4
	pine	900	17	370	23	256	41	216	28	850	16	0	.	278	47	79	43	53	64	151	36	50	59	634	18	1030	16	186	45	5054	7
	larch	24	57	97	50	88	40	120	40	139	37	8	100	27	58	60	56	55	54	0	.	240	47	2894	10	2123	11	1890	11	7764	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	20	100	3	100	9	148	4	100	0	.	305	20	536	16	0	.	876	12
	other conifers	57	100	141	59	105	55	66	72	76	41	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	11	100	459	28
	beech	7535	6	2881	10	1896	14	2628	10	3216	9	960	19	2486	12	2730	11	765	23	448	24	774	19	220	27	411	29	1642	11	28592	3
	maple	975	11	270	23	106	31	172	32	485	16	153	32	336	20	440	16	286	25	110	31	254	23	73	49	95	53	49	68	3805	6
	ash	697	18	413	19	612	19	578	35	1502	12	114	43	285	22	699	17	159	42	89	57	175	32	88	42	36	44	264	23	5712	6
	oak	476	20	257	29	577	22	302	30	520	18	8	72	39	59	-3	766	57	61	32	62	14	100	45	38	90	62	318	24	2733	9
	sweet chestnut	0	.	0	.	32	100	0	.	3	100	0	.	0	.	0	.	4	100	0	.	0	.	13	59	3	100	1102	13	1159	13
	other broadleaves	376	23	187	44	241	32	176	37	483	20	3	100	127	30	92	36	117	38	31	49	65	48	135	34	147	31	1016	13	3198	8
	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	22915	3	6045	8	6089	9	8694	7	13309	5	6699	9	13909	5	12016	6	6632	8	3146	12	4073	11	9902	6	15557	5	10507	4	139493	1
=52 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7444	7	1067	21	1840	18	3396	14	4665	11	4646	12	9062	9	8256	10	5524	11	2748	18	2996	16	5043	11	12050	8	4794	10	73531	3
	fir	6453	9	564	27	1141	23	3417	13	3136	13	1937	21	5917	10	3419	13	1185	20	623	32	562	32	521	30	1080	21	974	24	30929	4
	pine	456	26	98	66	105	48	212	35	789	20	0	.	114	55	79	66	16	100	32	58	45	83	323	34	724	21	70	54	3063	10
	larch	39	73	46	76	198	51	106	44	307	26	38	100	129	93	151	45	102	43	0	.	111	50	4929	10	3357	12	2645	11	12158	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	27	73	0	0	0	.	309	23	476	20	7	100	829	15
	other conifers	0	.	73	71	158	70	226	55	235	72	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	16	100	751	33
	beech	3465	10	2642	14	1593	17	2924	13	4372	11	664	24	2065	17	2156	14	849	26	324	30	578	28	137	46	309	35	1472	19	23549	4
	maple	164	27	91	50	49	52	84	62	294	29	50	43	57	46	141	39	144	46	8	100	113	41	0	.	36	65	33	58	1263	13
	ash	404	24	256	33	569	30	499	30	993	18	86	52	125	55	332	34	82	47	36	100	69	48	7	123	25	77	102	46	3584	10
	oak	264	32	564	31	547	31	416	23	1130	22	36	73	35	71	119	48	13	100	25	72	0	.	34	62	64	48	110	37	3356	11
	sweet chestnut	0	.	0	.	22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	64	5	100	1823	19	1904	18
	other broadleaves	201	66	374	43	397	42	133	65	358	69	0	.	22	71	77	76	289	55	18	75	49	53	148	52	32	53	228	35	2324	18
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0
	total	18890	5	5774	10	6619	11	11413	8	16279	6	7457	10	17565	6	14740	7	8232	9	3815	15	4522	13	11503	7	18158	6	12274	6	157240	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18651	5	2752	15	4885	12	8088	10	13572	7	11873	9	20701	6	17328	7	13239	9	6109	13	6628	12	14867	7	31510	5	11084	8	181288	2
	fir	15035	6	1388	21	2241	17	7020	10	5739	10	3921	15	11659	8	6931	10	2052	18	1492	22	1138	24	1523	20	1701	18	1829	18	63668	3
	pine	1722	15	593	23	429	33	515	23	1870	13	0	0	558	39	166	47	124	38	329	34	183	54	2082	15	2817	13	401	42	11791	6
	larch	98	42	167	50	386	36	290	30	608	21	60	79	164	79	231	39	228	30	0	.	434	38	9809	8	6738	9	6078	8	25291	4
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	48	100	22	100	35	87	20	100	0	.	1102	15	1619	14	15	60	2861	10
	other conifers	132	65	277	47	306	47	352	41	407	47	0	.	55	76	30	53	37	65	12	53	-7	141	12	50	63	31	29	93	1705	19
	beech	16764	5	7205	9	4823	12	7142	9	10283	7	2240	17	7137	10	6759	9	2453	19	1213	21	1956	16	926	21	1114	22	6315	9	76329	2
	maple	2442	8	782	16	536	23	693	18	1956	11	648	18	1124	13	1334	13	1022	15	404	25	698	19	344	19	297	35	442	18	12721	4
	ash	1831	13	1103	16	1914	16	1506	22	3516	10	480	27	827	22	1763	14	486	26	320	33	370	25	405	23	236	25	1173	16	15928	5
	oak	1306	17	1087	21	1393	20	885	18	1954	16	68	46	106	39	134	37	138	54	64	48	15	97	242	24	175	50	957	16	8524	7
	sweet chestnut	0	.	0	.	71	99	2	100	3	100	0	.	0	.	0	.	4	100	0	.	0	.	116	54	11	100	5352	10	5559	9
	other broadleaves	1303	15	932	29	1019	24	687	21	1793	18	154	25	454	15	544	20	901	26	294	24	288	25	1355	13	966	16	4698	7	15389	5
	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	59284	3	16287	7	18004	7	27179	6	41701	4	19443	8	42834	5	35243	5	20720	7	10256	11	11704	10	32783	5	47246	4	38372	3	421054	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. 1280587. Birmensdorf, Swiss Federal Research Institute WSL

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