1

Fig. 1. Statistical regions of Latvia.

Table 1. Characterization of population (age 15–74) in different statistical regions of Latvia in 2017 and 2018 (Central Statistical Bureau of Latvia 2019).
Parameter, unit Year Statistical regions of Latvia
Rīga Pierīga Kurzeme Zemgale Latgale Vidzeme
Population, number (thousand) 2017 468.2 267.2 178.2 171.4 199.3 139.0
2018 465.7 268.5 175.8 169.2 195.3 136.3
Economically active population*, number (thousand) total 2017 336.9 188.4 118.4 114.9 127.2 94.5
2018 341.6 191.3 117.5 112.6 124.8 94.4
employed 2017 310.8 177.3 107.9 104.2 109.3 85.3
2018 319.3 182.4 108.3 103.5 110.0 85.9
unemployed 2017 26.1 11.1 10.4 10.7 17.9 9.2
2018 22.3 8.9 9.1 9.1 14.7 8.5
Economically inactive population**, number (thousand) 2017 131.3 78.8 59.8 56.5 72.1 44.5
2018 124.1 77.2 58.3 56.6 70.5 41.9
Unemployment rate, % 2017 7.8 5.9 8.8 9.3 14.0 9.7
2018 6.5 4.7 7.8 8.1 11.8 9.0
Number of people with higher education, number (thousand) 2017 184.7 83.5 35.2 38.1 44.1 26.3
2018 179.8 83.0 37.5 37.9 43.8 29.7
Average monthly salaries (Gross/Net), euro 2017 1044/758 871/640 775/568 786/579 640/471 739/544
2018 1129/829 949/705 858/641 848/634 701/529 803/604
* Economically active population – active population consists of employed persons and unemployed persons actively seeking a job.
** Economically inactive population – persons who can neither be classified as employed nor as unemployed persons (pupils, students, non–working pensioners, etc.).
Table 2. Characterization of forest in different statistical regions of Latvia (Latvian NFI data, 2015).
Parameter, unit Statistical regions of Latvia
Rīga Pierīga Kurzeme Zemgale Latgale Vidzeme
Forest area, kha 5.27 550.30 770.91 428.80 595.99 889.82
Forest cover, % of total land area 17.3 54.3 56.7 39.9 41.0 58.3
Estimated potential mean bilberry yield*, kg ha–1 27.6 13.1 19.3 10.0 6.8 8.6
Estimated total bilberry yield in the statistical region**, kt 0.15 7.22 14.90 4.29 4.05 7.64
Estimated potential mean lingonberry yield*, kg ha–1 12.3 4.2 5.5 3.3 2.4 2.7
Estimated total lingonberry yield in the statistical region**, kt 0.06 2.29 4.23 1.40 1.45 2.42
* Potential mean berry yields are calculated according to equations that take into consideration site type, stand age and stand density. Information on projective cover of specific berry species is obtained from the NFI data.
** Information on the cover of different forest types and their properties in the statistical region is obtained from State Forest Register.
Table 3. Non-wood forest product sales according to the place of origin in 2017 and 2018 in Latvia.
Statistical regions of Latvia Birch and maple sap Mushrooms Berries
count share of total count, % count share of total count, % count share of total count, %
2017 2018 2017 2018 2017 2018 2017 2018 2017 2018 2017 2018
Kurzeme 14 5 14 5 47 20 24 13 23 47 9 11
Latgale 3 2 3 2 9 10 4 6 21 53 8 13
Vidzeme 8 11 8 12 19 16 10 10 58 45 22 11
Zemgale 26 17 26 18 25 24 13 16 47 72 18 17
Pierīga 47 54 47 57 87 76 45 49 103 173 38 41
Rīga 0 0 0 0 3 1 2 1 9 1 3 <1
Unknown 2 5 2 5 3 7 2 5 6 33 2 8
Total 100 94 100 100 193 154 100 100 267 424 100 100
2

Fig. 2. Mean retail price of birch and maple sap in the 2017 and 2018 season by statistical region. White figures in the bars indicate number of cases included in the calculation of mean price.

3

Fig. 3. Mean retail price of forest mushrooms in the 2017 and 2018 season by statistical region. White figures in the bars indicate number of cases included in the calculation of mean price.

4

Fig. 4. Mean retail price of wild forest berries in the 2017 and 2018 season by statistical region. White figures in the bars indicate number of cases included in the calculation of mean price.

Table 4. Changes in average price of non-wood forest products and offered amount (expressed as number of sales) in 2018 if compared to 2017 in Latvia.
Non-wood forest
products 		Change in average price, % Change in offered amount, %
Birch sap +22.2 –21.5
Maple sap +2.6 +52.4
Chanterelles –19.1 –76.6
Boletes species +10.6 –71.4
Stinkhorn +108.3 –85.0
Bilberry –1.6 +144.3
Lingonberry +26.4 +8.2
Cranberry +11.5 +79.2
Cloudberry –3.9 –25.0
Wild strawberry +12.0 +33.3
Wild raspberry –15.9 +2100*
* The extremely large change is explained by very low number of cases in 2017 (n = 1)
Table 5. Correlation coefficients and p-values of linear regression model describing relations between average price of non-wood forest products and forest characteristics or socio-economic parameters in different statistical regions of Latvia in 2017 and 2018 (correlation coefficient r/p-value is shown in table; bold figures indicate r > 0.50 and p < 0.05, grey cells indicate cases when both strong correlation and statistically significant regression was detected).
Parameter Year Birch sap* Maple sap* Chanter-elles** Boletes species** Stink-horn** Bil-berry* Lingon-berry* Cran-berry* Cloud-berry* Wild straw-berry* Wild rasp-berry*
Population 2017 0.63/ 0.192 0.13/ 0.806 0.63/ 0.183 0.52/ 0.651 –0.66/ 0.222 0.53/ 0.277 0.89/ 0.018 0.92/ 0.029 0.21/ 0.731 0.79/ 0.213 -
2018 0.31/ 0.531 0.47/ 0.428 0.67/ 0.215 - –0.70/ 0.510 0.75/ 0.011 0.81/ 0.028 0.50/ 0.315 –0.12/ 0.852 –0.35/ 0.730 0.65/ 0.177
Economically active population (total) 2017 0.59/ 0.225 0.13/ 0.805 0.64/ 0.175 0.53/ 0.645 –0.65/ 0.234 0.57/ 0.242 0.90/ 0.014 0.93/ 0.021 0.22/ 0.725 0.78/ 0.222 -
2018 0.27/ 0.580 0.51/ 0.377 0.69/ 0.197 - –0.70/ 0.510 0.75/ 0.010 0.80/ 0.031 0.53/ 0.281 –0.10/ 0.871 –0.37/ 0.737 0.66/ 0.164
Economically active population (employed) 2017 0.57/ 0.247 0.14/ 0.794 0.66/ 0.156 0.55/ 0.629 –0.64/ 0.242 0.60/ 0.210 0.90/ 0.013 0.94/ 0.017 0.21/ 0.731 0.78/ 0.217 -
2018 0.24/ 0.623 0.53/ 0.355 0.70/ 0.184 - –0.70/ 0.510 0.76/ 0.008 0.81/ 0.031 0.53/ 0.280 –0.10/ 0.876 –0.36/ 0.764 0.68/ 0.149
Economically active population (unemployed) 2017 0.75/ 0.076 <0.01/ 0.993 0.24/ 0.642 0.25/ 0.841 –0.63/ 0.252 0.06/ 0.912 0.72/ 0.109 0.66/ 0.226 0.27/ 0.666 0.56/ 0.445 -
2018 0.73/ 0.101 0.11/ 0.864 0.35/ 0.561 - –0.68/ 0.522 0.47/ 0.182 0.66/ 0.120 0.43/ 0.395 –0.16/ 0.796 –0.41/ 0.383 0.30/ 0.594
Economically inactive population 2017 0.72/ 0.113 0.13/ 0.810 0.59/ 0.215 0.50/ 0.668 –0.70/ 0.192 0.43/ 0.399 0.84/ 0.036 0.85/ 0.066 0.20/ 0.748 0.80/ 0.197 -
2018 0.43/ 0.385 0.31/ 0.615 0.60/ 0.290 - –0.70/ 0.508 0.73/ 0.021 0.81/ 0.027 0.39/ 0.449 –0.16/ 0.792 –0.30/ 0.711 0.58/ 0.237
Unemployment rate 2017 0.17/ 0.683 –0.31/ 0.555 –0.78/ 0.065 –0.96/ 0.188 0.13/ 0.835 –0.98/ <0.001 –0.49/ 0.323 –0.64/ 0.244 0.09/ 0.891 –0.49/ 0.506 -
2018 0.65/ 0.188 –0.74/ 0.157 –0.71/ 0.183 - 0.80/ 0.409 –0.70/ 0.102 –0.49/ 0.306 –0.31/ 0.556 –0.03/ 0.958 –0.04/ 0.407 –0.77/ 0.069
Number of people with higher education 2017 0.57/ 0.248 0.11/ 0.841 0.61/ 0.198 0.51/ 0.660 –0.63/ 0.254 0.56/ 0.248 0.91/ 0.013 0.94/ 0.018 0.22/ 0.728 0.76/ 0.242 -
2018 0.27/ 0.580 0.53/ 0.357 0.68/ 0.206 - –0.68/ 0.524 0.74/ 0.012 0.79/ 0.037 0.55/ 0.260 –0.11/ 0.864 –0.38/ 0.722 0.66/ 0.164
Average monthly salaries (Bruto) 2017 0.26/ 0.642 0.29/ 0.571 0.73/ 0.103 0.53/ 0.646 –0.46/ 0.431 0.84/ 0.035 0.93/ 0.007 0.98/ 0.003 0.15/ 0.810 0.67/ 0.330 -
2018 –0.10/ 0.866 0.73/ 0.164 0.86/ 0.059 - –0.70/ 0.502 0.86/ 0.003 0.79/ 0.040 0.63/ 0.183 –0.13/ 0.838 –0.20/ 0.860 0.81/ 0.056
Forest area,
kha 		2017 –0.52/ 0.289 –0.11/ 0.838 –0.46/ 0.353 –0.01/ 0.992 0.27/ 0.661 –0.36/ 0.487 –0.80/ 0.060 –0.79/ 0.103 0.20/ 0.748 –0.80/ 0.205 -
2018 –0.31/ 0.556 –0.33/ 0.593 –0.41/ 0.492 - 0.28/ 0.816 –0.86/ 0.029 –0.22/ 0.088 –0.75/ 0.673 0.56/ 0.327 –0.14/ 0.796 –0.75/ 0.088
Forest cover,
% of total
land area 		2017 –0.56/ 0.250 –0.04/ 0.935 –0.18/ 0.728 0.17/ 0.892 0.29/ 0.636 –0.09/ 0.861 –0.68/ 0.092 –0.74/ 0.203 0.08/ 0.897 –0.56/ 0.438 -
2018 –0.55/ 0.261 –0.16/ 0.798 –0.24/ 0.701 - 0.31/ 0.802 –0.68/ 0.139 –0.26/ 0.165 –0.65/ 0.624 0.54/ 0.343 0.06/ 0.905 –0.52/ 0.294
Potential mean berry yield,
kg ha–1 		2017 - - - - - 0.69 /0.131 0.97 /0.001 *** *** *** ***
2018 - - - - - 0.86 /0.027 0.93 /0.008 *** *** *** ***
Estimated total berry yield, kt 2017 - - - - - 0.06 /0.906 –0.53 /0.277 *** *** *** ***
2018 - - - - - –0.40 /0.427 –0.27 /0.604 *** *** *** ***
* Price per 1 L.
** Price per 1 kg.
*** No data on potential berry yields available.
5

Fig. 5. Main non-wood forest product flows from ecosystem service providing units (statistical regions and counties) to the marketplaces in Rīga in 2017 and 2018. n – number of cases used for flows analysis. Different division in percentages correspond to data structure of every product and year. View larger in new window/tab.

6

Fig. 6. Main non-wood forest product flows from ecosystem service providing units (SPU) to the service benefitting area (SBA) excluding sales at marketplaces in Rīga at county level in 2017 and 2018 in Latvia. n – number of cases used for flows analysis. Different division in percentages correspond to data structure of every product and year. View larger in new window/tab.

Table 6. Characteristics of distances to transport non-wood forest product from service providing units (SBU) to service benefitting area (SBA) in 2017 and 2018 in Latvia.
Flow of NWFPs Cases when NWFPs were transported outside SPU county borders (number/ share)* Characteristics of distance, km
Average value (± S.E.) Minimum value Maximum value Median Mode
From SPU to the marketplaces in Rīga 440/ 100% 63.1 (1.8) 14 261 52.5 37
From SPU to SBA in cases when NWFPs were transported outside SPU county borders* 221/ 37% 59.7 (3.4) 6 250 41 30
* SPU and SBA does not match.
NWFPs – non-wood forest products. SPU – service providing units. SBA – service benefitting area.