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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
volume of bolewood (HG 2010)
timber assortment classes (HG 2010) · tree species (5 classes)
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-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
timber assortment classes (HG 2010)	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	±%
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	.
	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	.
	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	.
smallwood	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2035	11	98	44	420	24	257	26	349	20	339	31	192	13	260	24	115	26	59	27	37	20	276	19	250	11	84	17	4772	6
	fir	1859	10	40	57	211	28	216	21	136	26	87	33	157	16	108	34	23	24	78	73	8	30	91	68	8	29	12	24	3034	7
	other conifers	201	42	22	40	122	45	41	57	28	26	6	75	15	47	6	49	12	38	5	57	4	81	130	13	106	14	62	21	760	14
	beech	53	9	21	13	12	19	24	13	29	11	7	28	25	15	22	14	9	21	5	29	8	22	4	36	7	29	34	14	260	4
	other broadleaves	24	13	11	19	12	22	12	16	23	12	4	24	13	21	14	18	9	19	5	29	7	29	12	20	7	23	80	9	233	5
	indeterminable	0	72	0	0	0	0	0	0	0	0	0	0	0	0	0	100	0	0	0	0	0	0	0	0	0	0	0	0	0	68
	total	4173	8	193	30	777	18	550	18	564	15	444	26	401	10	411	20	168	19	152	45	63	15	514	17	379	9	272	8	9059	4
classes 1 and 2	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2596	11	699	23	1038	21	2678	14	3779	11	2723	14	5512	10	4357	11	3586	11	1632	19	1498	19	5155	10	8774	7	2997	13	47026	3
	fir	2429	10	364	32	333	27	2073	14	1089	17	904	24	2893	11	1210	17	510	23	574	29	170	28	383	25	211	27	423	27	13565	5
	other conifers	537	23	358	25	180	39	300	27	613	16	12	100	243	53	76	41	200	30	151	47	69	64	3661	10	2727	11	1524	13	10649	5
	beech	4224	8	1477	14	1022	17	1509	11	2273	10	557	25	2184	12	1622	15	677	21	501	28	555	26	512	28	359	28	2614	14	20085	4
	other broadleaves	2879	10	1074	14	1259	18	1237	14	2633	10	765	19	1250	19	1608	16	1108	18	439	26	612	21	1213	14	961	21	6746	7	23784	4
	indeterminable	8	50	0	100	0	0	0	0	6	87	1	100	2	71	2	77	9	70	0	0	0	0	0	0	13	100	4	58	46	36
	total	12672	5	3973	10	3833	12	7796	8	10394	6	4962	12	12084	6	8874	8	6090	9	3296	15	2904	14	10924	7	13045	6	14309	5	115155	2
classes 3 and 4	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7644	7	1071	20	1906	23	3170	15	4663	10	4507	13	7410	9	7328	10	5469	12	2532	19	2333	17	6100	10	10994	7	4804	15	69930	3
	fir	6975	9	530	28	1012	25	3293	14	2269	15	1470	19	5139	10	2936	14	1076	22	835	30	445	32	756	30	583	25	835	26	28155	4
	other conifers	566	19	521	32	388	33	694	23	1133	18	3	100	409	56	107	45	162	41	91	55	49	62	4050	11	3829	10	1965	12	13968	5
	beech	7162	7	3143	13	2042	19	3504	11	3885	10	762	29	3372	14	3606	13	1029	23	615	31	913	22	285	37	662	31	1999	12	32980	3
	other broadleaves	2465	13	1105	19	1475	18	1496	20	3155	10	342	28	887	20	1412	15	711	24	360	38	642	27	357	30	287	31	2535	10	17229	4
	indeterminable	0	0	11	100	0	0	0	0	0	0	0	0	0	0	0	0	13	100	0	0	0	0	0	0	0	0	0	0	23	71
	total	24810	4	6380	11	6824	12	12157	8	15106	6	7084	11	17218	6	15390	8	8459	10	4433	15	4381	14	11548	8	16356	6	12138	7	162285	1
classes 5 and 6	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2217	16	466	36	465	38	1036	19	1393	19	1545	17	2660	13	2997	15	2001	15	1072	23	1329	25	1977	15	3741	11	2306	19	25205	4
	fir	1856	17	125	39	452	38	1190	18	990	21	607	29	2397	14	1181	19	763	27	427	47	234	45	208	42	414	30	461	31	11305	6
	other conifers	84	53	123	76	35	76	410	39	428	51	0	0	53	64	55	53	41	55	9	100	21	72	1262	15	1211	14	723	23	4453	9
	beech	1413	16	938	21	703	23	1656	17	2602	14	214	48	1228	23	782	24	300	34	214	42	328	36	89	57	209	45	463	20	11138	6
	other broadleaves	231	28	320	29	402	28	459	30	1360	25	45	62	58	61	315	35	246	72	64	72	76	46	52	43	58	47	948	26	4636	11
	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	5802	9	1972	15	2057	17	4750	11	6773	10	2410	15	6397	9	5330	11	3350	13	1787	21	1988	19	3588	11	5633	9	4900	11	56736	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14491	7	2335	19	3829	19	7141	12	10185	9	9114	12	15774	8	14942	10	11171	11	5295	17	5197	17	13509	9	23759	7	10190	13	146933	2
	fir	13119	8	1058	27	2008	23	6772	12	4484	13	3068	20	10587	9	5435	13	2371	22	1915	28	857	30	1437	26	1216	24	1732	24	56058	4
	other conifers	1388	19	1024	31	725	30	1444	23	2202	20	21	83	720	42	244	35	415	28	256	50	142	50	9102	9	7873	9	4273	12	29830	5
	beech	12852	6	5579	12	3781	17	6692	10	8789	9	1540	24	6809	12	6032	13	2015	20	1335	27	1803	20	890	29	1237	28	5110	11	64463	3
	other broadleaves	5599	10	2510	14	3149	17	3204	15	7171	10	1156	19	2208	17	3349	13	2074	21	867	29	1337	22	1635	15	1313	20	10310	7	45881	3
	indeterminable	8	50	11	100	0	0	0	0	6	87	1	100	2	71	2	77	22	66	0	0	0	0	0	0	13	100	4	58	70	34
	total	47457	4	12517	10	13491	11	25252	7	32836	5	14900	10	36100	6	30005	7	18067	9	9668	15	9336	13	26574	7	35412	6	31619	5	343236	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. 1400837. Birmensdorf, Swiss Federal Research Institute WSL

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