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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
number of stems
development stage · conifers and broadleaves
regional demarcation: canton
unit: n/ha
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
development stage	conifers and broadleaves	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
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	.
	conifers	0	.	0	.	0	.	54	35	0	.	0	.	0	.	0	.	87	30	20	0	23	75	0	.	0	.	0	.	30	71	0	.	51	70	10	72	107	74	89	39	0	.	27	47	16	22	0	.	0	.	53	20
	broadleaves	0	.	0	.	120	0	11	94	0	.	20	0	0	.	227	38	0	.	50	0	0	.	0	.	0	.	0	.	0	.	0	.	187	62	0	.	7	82	0	.	0	.	31	57	0	.	0	.	50	0	24	39
	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	.	120	0	65	29	0	.	20	0	0	.	227	38	87	30	70	0	23	75	0	.	0	.	0	.	30	71	0	.	238	64	10	72	113	67	89	39	0	.	58	50	16	22	0	.	50	0	76	18
young growth/ thicket	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	.
	conifers	22	39	0	.	57	65	81	16	18	83	63	30	0	.	7	81	107	15	31	71	185	28	351	73	0	.	83	66	21	49	20	71	15	55	135	33	22	72	19	50	18	93	58	31	23	38	62	47	49	37	65	11
	broadleaves	50	31	0	.	126	34	47	26	64	52	32	44	43	41	57	81	18	44	34	52	59	40	35	58	0	.	39	57	40	37	90	55	23	39	63	70	58	72	47	33	27	55	25	36	22	39	22	84	50	29	38	10
	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	72	20	0	.	183	38	129	13	82	36	95	25	43	41	64	81	125	14	65	41	245	23	386	71	0	.	121	35	61	33	110	58	38	29	198	35	80	72	66	25	45	43	83	22	45	25	84	51	98	18	103	8
pole timber	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	.
	conifers	176	25	0	.	547	52	287	14	148	42	226	43	0	.	118	49	426	10	127	36	461	19	148	42	0	.	583	35	268	30	211	58	321	35	314	27	237	33	100	24	345	20	313	19	206	16	125	14	226	26	257	5
	broadleaves	254	15	0	.	189	38	249	13	408	16	227	24	0	.	166	32	185	21	273	29	151	24	323	23	515	30	42	55	233	28	455	23	388	29	237	36	226	14	460	10	223	45	361	16	274	12	260	11	248	16	288	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	.
	total	430	11	0	.	735	31	536	7	556	12	453	21	0	.	284	17	611	7	400	18	612	13	471	13	515	30	625	31	501	18	666	18	709	14	550	16	462	16	560	8	568	17	675	8	481	8	385	12	474	11	546	3
young timber	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	.
	conifers	217	23	510	0	211	28	320	15	159	50	393	24	0	.	148	44	411	12	223	27	340	20	204	55	745	49	525	36	376	19	189	43	92	36	382	4	380	26	124	26	312	25	251	19	528	14	600	0	245	26	313	6
	broadleaves	249	19	140	0	273	76	199	17	387	27	214	30	0	.	257	39	136	28	241	20	86	34	128	48	364	79	298	43	372	28	240	27	380	16	387	65	134	50	508	9	333	31	278	18	80	29	20	0	232	18	255	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	.
	total	466	11	650	0	485	31	519	9	546	10	606	15	0	.	404	24	547	8	464	13	425	16	333	19	1109	28	823	18	748	14	429	15	472	11	769	35	514	15	633	7	645	17	529	10	608	11	620	0	477	13	568	3
medium timber	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	.
	conifers	79	43	364	37	587	24	251	11	84	26	322	27	110	71	193	28	347	8	181	25	169	16	236	29	378	41	175	47	175	18	148	21	105	25	234	20	166	32	142	24	596	22	220	13	395	12	151	44	115	20	232	5
	broadleaves	195	15	19	71	194	21	199	12	337	19	214	22	375	12	127	26	37	29	222	22	171	24	109	35	287	39	288	36	222	13	199	23	297	14	179	20	231	21	268	16	158	80	198	14	82	43	150	51	160	12	177	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	.
	total	274	11	383	36	782	20	451	8	421	16	536	16	485	7	320	12	385	7	403	12	340	13	344	14	665	10	462	24	397	10	347	15	402	12	412	12	398	18	411	8	755	15	418	8	477	9	301	12	275	9	409	3
old timber	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	.
	conifers	56	21	384	13	237	15	203	6	96	34	206	13	94	75	230	39	268	6	163	15	245	13	185	14	170	32	335	21	185	12	65	40	73	25	194	13	105	21	154	16	318	25	177	17	314	9	250	36	92	19	199	3
	broadleaves	163	14	55	67	136	27	137	9	122	18	115	22	267	9	71	36	35	23	188	20	151	19	109	19	185	34	115	28	146	17	157	16	163	13	134	20	176	19	151	18	109	33	126	12	28	31	95	46	164	12	118	4
	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	220	9	439	10	373	13	340	5	218	16	320	9	361	13	301	29	302	5	351	11	396	9	294	11	355	17	450	14	332	8	222	18	236	10	327	9	281	12	304	9	427	16	304	10	342	8	345	14	255	8	318	2
mixed	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	.
	conifers	84	41	280	0	244	14	276	6	85	28	241	28	0	.	268	33	359	7	227	20	305	11	265	11	206	39	426	20	217	19	27	82	171	34	252	16	97	39	189	20	410	19	204	9	316	7	370	4	79	23	269	3
	broadleaves	230	19	100	0	20	0	127	12	257	20	276	18	0	.	172	37	52	24	213	22	154	15	102	15	189	37	86	35	267	17	560	15	235	25	147	30	238	21	253	11	117	42	148	14	80	20	40	71	200	20	142	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	.
	total	314	18	380	0	264	13	403	5	342	15	517	11	0	.	440	20	410	6	440	13	459	8	367	8	395	38	513	15	484	12	586	18	407	18	399	16	334	11	443	11	527	16	352	7	396	6	410	3	278	15	411	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	.
	conifers	104	14	357	15	281	19	235	4	106	19	249	12	57	61	189	19	324	4	168	10	294	8	232	10	315	34	397	14	197	9	123	21	129	17	229	10	167	15	134	10	332	11	211	7	302	6	213	23	141	13	231	2
	broadleaves	192	8	49	44	151	19	151	6	286	11	178	11	193	31	148	17	70	12	198	11	139	10	129	12	304	26	130	21	203	10	244	13	263	10	159	14	185	10	330	6	159	21	191	8	102	10	101	30	186	7	169	2
	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	296	7	405	14	432	13	386	3	393	8	427	7	250	31	337	11	394	3	367	7	433	6	360	6	619	16	528	10	400	6	367	10	392	8	388	7	351	8	464	4	491	9	403	5	403	4	314	14	327	6	400	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. 1325272. Birmensdorf, Swiss Federal Research Institute WSL

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