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IZVORNI I ZNANSTVENI ČLANCI – ORIGINAL SCIENTIFIC PAPERS Šumarski list br. 1–2, CXXXV (2011), 19-27 UDK 630* 114.2 (001) PEDOTRANSFER FUNCTIONS FOR BULK DENSITY ESTIMATION OF FOREST SOILS PEDOTRANSFER FUNKCIJE ZAPROCJENU GUSTOĆE ŠUMSKIH TALA 1 12 Milan KOBAL, Mihej URBANČIČ, Nenad POTOČIĆ , 31 Bruno DE VOS , Primož SIMONČIČ ABSTRACT: The data of 45 soil profiles from a 16 × 16 km grid across Slovenia was analysed to develop a local pedotransfer function (PTF) for bulk density (.b) estimation. In total, 106 soil horizons were considered. Concentration of organic carbon (OC) was found to be well correlated (r = -0.861, p < 0.001) with .b. Two separate line segments were fitted to the data, which was partitioned into two intervals, based on OC content (below 36.0 g/kg and above 36.0 g/kg). Nearly 80 % of the variability in .b is explained with segmented regression. The local PTF was compared with published PTFs and four validations indices (MPE, SDPE, RMSPE and R2) confirmed the highest prediction quality of the local PTF. The differences of carbon stock (Cpool) estimation, based on usage of different PTFs could be higher than 160 t OC per hectare. Prediction of carbon stocks could be substantially improved by calibration of the models coefficients with data stratified according to each unique soil type. Key words:pedotransfer function PTF, organic carbon OC, segmented regression, forest soil, carbon stock Cpool INTRODUCTION – Uvod Since forest soil sampling and analyses of chemical Nimmo,2003), while PTFs for estimation of soil bulk and physical properties of forest soils are time consu-density (.b)were introduced in the 1970s’ (e.g. Jeffrey, mingand labor intensive, the development of alternative 1970).At first, bulk density was correlated only with methods is indispensable. By using pedotransfer func-soil organic matter (SOM) (Adams,1973;Federer, tions (PTFs), soil scientists are able to get information 1993;RawlsandBrakensiek,1985,Honeysett on crucial soil properties, which are otherwise difficult andRatkowski,1989), but later the information on (expensive or time consuming) toobtain. PTFs can be soil texture was added to some PTFs (Leonavičiute, defined as statistical models for predicting soil physical 2000; Kauret al., 2002). Simple univariate models (bulk density, soil hydraulic properties, etc.) and chemi-were supplemented with multiple regressions and diffecal (e.g. cation exchange capacity) properties from other rent equations were developed separately for the organic more available and routinely measured properties. and the mineral soil layers(e.g. Harrisonin Bocock, 1981), or even for different genetic soil horizons The first PTF (for wilting coefficient) was develo (e.g.Leonavičiute,2000). Recently, various techni ped by Briggs and McLane 1907 (Landa and ques of tree regressions were incorporated in PTFs de 1 Mr. sc. Milan Kobal, Slovenian Forestry Institute, Večna pot 2, velopment (e.g.Martinetal., 2009). SI-1000 Ljubljana, milan.kobal@gozdis.si Mihej Urbančič, dipl. inž., Slovenian Forestry Institute, Soil bulk density (.b)is defined as the mass of a unit Večna pot 2, SI-1000 Ljubljana, mihej.urbancic@gozdis.si volume of dry soil (105 °C), which includes both solids Dr. sc. Primož Simončič, Slovenian Forestry Institute, and pores and, thus, bulk density reflects the total soil Večna pot 2, SI-1000 Ljubljana, primoz.simoncic@gozdis.si 3 2 porosity (FAO, 2006). Usually, it is expressed in g/cm Cvjetno naselje 41, HR-10450 Jastrebarsko, nenadp@sumins.hr Dr. sc. Nenad Potočić, Croatian Forest Research Institute, 3 or kg/dm. Soil bulk density is necessary for the asses 3 Mr. Bruno De Vos, Research Institute for Nature and Forest, sment of soil carbon and nutrient pools (Tamminen Gaverstraat 4, B-9500 Geraardsbergen, Belgium, bruno.devos@inbo.be andStarr,1994) and for other mass-to-volume conver |
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M. Kobal, M. Urbančič, N. Potočić, B. De Vos, P. Simončič: PEDOTRANSFER FUNCTIONS FOR BULK DENSITY ... Šumarski list br. 1–2, CXXXV (2011), 19-27 sions. It is also needed when estimating soil water reten-cially recent studies evaluating existing PTFs (e.g. De tion characteristics and is a required input parameter in Voset al., 2005; Martinet al., 2009) warn against models of water, sediment and nutrient transport (Bouc-usage of PTFs without first testing their accuracy, and neau et al., 1998).Additionally, soil bulk density is an in-stress the importance of local calibrations of coefficients dicator of soil compaction, porosity and site productivity in the models. (Tamminen andStarr,1994;Salifuetal., 1999). The aim of our study was to develop a local PTF for Several studies have investigated variation in forest the estimation of soil bulk density of(forest) mineral soils properties at very detailed spatial scales (Phillips soils in Slovenia. Based on literature, we hypothesized and Marion,2005; Scharenbrochand Bock -that (1) the bulk density.b correlated strongly with soil heim 2007) and revealed that soil variability can be organic carbon concentration (OC) and (2) that our high even on short distances and in small areas. Espe-local PTF perform better than published PTFs. 2 METHODS – Materijali i metode 2.1 Data sources and laboratory work – Izvori podataka i laboratorijski rad The information on soil bulk densities as well as physical and chemical properties of soil horizons wastaken from the soil database of the Slovenian Forestry Institute (SFI). Only the data on soil profiles opened in year 2006 on the 16 × 16 km network across Slovenia were finally selected; in total, 45 soil profiles with 109 soil horizons (Figure 1). Summary information about soil profiles is presented inTable 1. Todescribe locations of the soil profiles and evaluate morphological and physical properties of the soil horizons, FAO methodology was followed (FAO, 2006).In each soil horizon, separate soil samples Figure 1 Locations of soil profiles (n = 45) across Slovenia from which the data for were taken for bulk density estima development of a local PTF for bulk density (.) estimation was derived. tion and for chemical and physical b Slika 1. Položaj profila tla (n=45) u Sloveniji na osnovi kojih su dobivene lokalne soil analysis. Samples for bulk den pedotransfer funkcije (PTF) za procjenu gustoće tla. sity estimation (ISO 11272) of a fine earth fraction (< 2mm) were obtained in five replicates by using metal O-rings with volume of 5 cm.In the laboratory, soil samp les were air dried (105 °C) and weig hed. Variability of bulk density estimation using metal O-rings based on 5 replicates is presented in Figure 2., where almost 80% of values have a CVless than 10%. Soil samples for chemical and physical soil analysis were also air driedand passed through a 2 mm sieve.The fine earth fraction (< 2mm) wasre- tained(UN/ECE ICP-Forests 2006, http://www.icp-forests. Figure 2 Frequency distribution for coefficient of variation (CV) for bulk density measure org/pdf/FINAL_soil.pdf) for furt 3 ments, obtained using 5 cm metal O-rings. her chemical and physical analyses. Slika 2. Distribucija frekvencija za koeficijent varijacije (CV) gustoća tala, izmjerenih ko- The following methods were used: rištenjem metalnih O-prstenova zapremine 5 cm3 |
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M. Kobal, M. Urbančič, N. Potočić, B. De Vos, P. Simončič: PEDOTRANSFER FUNCTIONS FOR BULK DENSITY... Šumarski list br. 1–2, CXXXV (2011), 19-27 Table 1 Summary information of soil profiles included in the study, grouped chloride following ISO 10390 on pH was determined in calcium according toWorld Reference Base soil reference groups (SRG) Tablica 1.Zbirni podaci o profilima tla uključenim u studiju, grupiranima prema automatic pH-meter Metrohm Ti- WRB referentnim grupama tala (SRG) trino, C and N content using dry combustion using ISO 10694 and/or 13878 on Leco CNS-2000, carbonates following ISO 10693 with Scheibler calcium-meter (Eijkelkamp) and soil texture following ISO 11277 with sedimentary method and pipette according to Köhn. SRG Referentna grupa N Soil depth, cm Dubina tla, cm Elevation, m Nadmorska visina, m tala prema WRB mean SD min max Acrisol Cambisol Fluvisol Histosol Leptosol Luvisol Phaeozem Planosol 2 23 1 2 1 9 6 1 135 77 120 73 33 83 57 100 21.2 24.0 23.3 30.6 20.6 110 262 188 1227 720 316 532 383 557 1318 188 1497 720 910 1208 383 2.2 Statistical analyses and model comparison Statističke analize i usporedba modela In total, 109 soil samples were included in the statistical analyses.Three influential points (soil samples) according to Cook’s distance were excluded from further analysis.The simple and multiple regression models were used to predict.b from different explanatory variables. According to PTFs, developed by Hoekstra and Poelman (1982), van Wallenburg (1988) and Reinds et al. (2001), regression models with segmented relationships were also tested. Only variables that show statistical significance at the 0.05 level were included in the models. Models were compared using partial F-test. From the literature, four different published PTFs were selected (Jeffrey, 1970; Harrison et al., 1981; Tamminen, 1994; Kaur et al., 2002) using following equations: Jeffrey: Harrison: Tamminen: Kaur: and Loss-On-Ignition method, the equation according toCraftet al.(1991)was used: The local PTF was compared with published PTFs using four validation indices: mean predicted error (MPE), standard deviation of the prediction error (SDPE), root mean square prediction error (RMSPE) and coefficient of determination(R2 ).These indices are defined as: 3 where.bis soil bulk density (g/cm),OCis OC concentrationby dry combustion method,LOIis organic ith matter content (g/kg) by Loss-On-Ignition method, Where.b,i is measured bulk density of soil sam- Clayis percentage of a clay fraction (0-2 µ) andSiltis ple,.bp,i is predicted bulk density ofith soil sample,nis percentage of silt fraction (2-63 µ). For conversion of the number of soil samples,covis the covariance and the data on OC obtained by dry combustion method varis the variance. 2.3 Carbon stock calculation (C ) Izračun zalihe ugljika (Cpool) pool Carbon stock per given area, hectare in our case, WhereOCiis organic carbon concentration of ith soil ith was calculated using following equation: horizon, is thickness of soil horizon (in m), is di .b,i ith 3 bulk densityof soil horizon (in g/cm),stoni is a corith rection factor for stoniness in horizon and n is the number of soil horizons for a given soil profile. |
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M. Kobal, M. Urbančič, N. Potočić, B. De Vos, P. Simončič: PEDOTRANSFER FUNCTIONS FOR BULK DENSITY ... Šumarski list br. 1–2, CXXXV (2011), 19-27 22 Statistical analyses were carried out using the R 2.9.3 software environment (R Development Core Team, 2009). Package ‘’segmented’’was used to fitre- gression models with segmented relationships(Mug- geo,2008). 3 RESULTS AND DISCUSSION – Rezultati i rasprava 3.1 Development of local PTF for predicting soil bulk density of mineral part of soil Razvoj lokalnih pedotransfer funkcija za predviđanje gustoće mineralnog sloja tla Soil bulk densityand concentration of organic car- bon were strongly correlated (r = -0.86, p < 0.001). Other chemical soil properties, except the concentra- tion of total nitrogen (N), were less correlated with bulk density (Figure 3).The correlation between bulk density and base saturation (BS) and the correlation between bulk density and clay content were not statisti- cally significant (p > 0.05). Figure 3 Relationship between bulk density (.b) and concentration of organic carbon (OC), concentration of total nitrogen (N), cation exchange capacity (CEC), base saturation (BS) and clay content (Clay) for 106 soil samples. Slika 3. Odnos gustoće tla i koncentracije organskog ugljika (OC), ukupnog dušika (N), pH, kapaciteta za izmjenu kationa (KIK), sume baza (BS) i sadržaja gline (glina) za 106 uzoraka. More than 73 % of the total variability of bulk den- sity was explained byOC (model SFI 1,Table 2).Ad- ding other chemical properties as explanatory variables in the multiple regression models(modelsSFI 2, SFI 3, Table 2 Regression relationship between soil properties as predictors and bulk density as response for 106 soil horizons (OC – organic carbon, BS – base saturation, CEC – cation exchange capacity, CLAY- clay content. Tablica 2.Regresijski odnos karakteristika tla kao prediktora i gustoće tla kao odziva za 106 horizonata tla (OC – organski ugljik, BS – suma baza, KIK - kapacitet za izmjenu kationa, glina – sadržaj gline). Model Response variable Intercept OC pH* BS* CEC* CLAY* SE Adj. R 2 Varijabla odziva KIK glina SFI 1 .b 1.3983 -0.0734 0.1403 0.7384 SFI 2 .b 1.4509 -0.0720 -0.0115 0.1404 0.7379 SFI 3 .b 1.3752 -0.0749 0.0004 0.1399 0.7398 SFI 4 .b 1.3902 -0.0788 -0.0011 0.1402 0.7385 SFI 5 .b 1.3438 -0.0734 0.0019 0.1390 0.7431 SFI 6 .b for OC < 3.6 % 1.4842 -0.1424 0.1257 0.7958.b for OC . 3.6 % 1.1253 -0.0452 * denotes not statistically significant variable in the model * označava nesignifikantnost varijable u modelu Bulk density/gustoća tla [g/m3 ] Bulk density/gustoća tla [g/m3 ] Bulk density/gustoća tla [g/m3 ] Bulk density/gustoća tla [g/m3 ] Bulk density/gustoća tla [g/m3 ] Bulk density/gustoća tla [g/m3 ] |
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M. Kobal, M. Urbančič, N. Potočić, B. De Vos, P. Simončič: PEDOTRANSFER FUNCTIONS FOR BULK DENSITY... Šumarski list br. 1–2, CXXXV (2011), 19-27 23 Figure4 Segmented regression relations- hip between soil bulk density (. b ) and organic carbon content (OC) in the mineral soil [g/kg] Slika4. Segmentirana regresija odnosa gustoće tla (. b ) i sadržaj organ- skog ugljika (OC) u mineralnom sloju tla [g/kg] SFI 4 and SFI 5 inTable 2) did not significantly im- prove the prediction of SFI 1 (partial F-test, p > 0.05). Unexpectedly, soil texture, especially clay content, was not statistically significant variable in the models; contrary tomany studies revealing that clay content is related with soil bulk density (Kaur, 2002; Leo na- vičiute,2000). The segmented regression method (SFI 6) improved prediction of .b (partial F-test, p < 0.001). The inde - pendent variable OC was partitioned into two intervals and a separate line segment was fitted to each interval. The boundary between two segments (breakpoint) was 36.0 g/kg OC (Figure 4).Nearly 80 % of the total va riabi- lity in.b was explained by using segmented regression. Figure 5 Evaluation indices for published PTFs and local PTF (SFI 6) Slika 5. Indeksi evaluacije za objavljene i lokalni PTF (SFI 6) 3.2 Validation of local and published PTFs for mineral part of soil Validacija lokalnih i objavljenih pedotransfer funkcija za mineralni sloj tla For the validation of local and published PTFs,bulk density was calculated using four published PTFs and predicted values were compared with local PTF (SFI 6).The prediction quality of all five PTFs is presented in Figure 5.All four validation indices confirmed the highest prediction powerof our local PTF (Figure 5)by |
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M. Kobal, M. Urbančič, N. Potočić, B. De Vos, P. Simončič: PEDOTRANSFER FUNCTIONS FOR BULK DENSITY ... Šumarski list br. 1–2, CXXXV (2011), 19-27 24 Figure 6 Performance of two local PTFs and published PTFs for the total dataset: estimated versus observed bulk densities with references to the 1:1 line. Slika 6. Kvaliteta predviđanja dvije lokalne i objavljenih pedotransfer funkcija za ukupni zbir podataka: procijenjene u odnosu na izmjerene gustoće s linijama izjednačenja. 3.3 Carbon stock calculation using different PTFs Izračun zalihe ugljika korištenjem različitih pedotransfer funkcija Carbon stock (C pool ) per hectare was calculated for different soil profiles, based on the usage of different PTFs (Table 4). Four different soil profiles were ran- domly selected from our soil databaseof the 16 × 16 km grid: Zajama, Lubnik, Besnica and Merljaki (Table 3). In the calculation of C pool , the stone content in soil hori- zons was considered, while the root portion was not.We assumednosurface rock outcrops. Soil profile “Zajama” was excavated in the Pokljuka plateau and is classified as Leptosol, soil profile “Lub- nik” was dug near Škofja Loka and is classified as Cam- bisol, profile “Besnica” was excavated near Ljubljana and is classified also as Cambisol, whereas soil profile “Merljaki” is classified asAcrisol and was excavated near Nova Gorica. Morphological, physical and chemi- cal properties are presented in detail inTable 3. The calculation of the C pool , based on the PTF of Kaur etal. (2002)gives highly underestimated values for all four soil profiles.The differences between cal- culated C pool using PTF SFI 6and measured C pool are not unambiguous, i.e. for soil profile “Lubnik” and “Be- snica” the carbon stock is underestimated, while for soil profile ‘’Zajama’’carbon stock is overestimated. The calculations of C pool revealed that differences of calculated carbon stock per hectare could be quite large and arestrongly dependent upon the PTFs algorithm. However, the lowest difference of the C pool based on measured and calculated bulk density was found for profile ‘’Merljaki’’. Both chemical and physical pro- perties of this profile are close to average soil proper- ties, included in this study, i.e. lower OC concentration and high bulk density (Figure 3). Consequently, the having the lowest value ofbias of the regression model (MPE), the lowest random variation of the predictions after correction for the global bias (SDPE),the lowest overall error of the predictions (RMSPE) and the hig- hest coefficient of determination (R2 ). In the case of high bulk density, local SFI 6 PTF seems slightly less accurate (Figure 6). Probably, that could be explained because of not including informa- tion onclay content, which is normally the highest just forthe soil horizons with high bulk densities (Urban- čič et al., 2005).For other PTFs the systematic error in predictions is evident from the scatterplots of Figure 6. |
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M. Kobal, M. Urbančič, N. Potočić, B. De Vos, P. Simončič: PEDOTRANSFER FUNCTIONS FOR BULK DENSITY... Šumarski list br. 1–2, CXXXV (2011), 19-27 Green World Research, Wageningen, The Netherlands, 55 pp. Salifu, K. F., W. L. Meyer,and H. G. Murchison, 1999. Estimating soil bulk density from organic matter content, pH, silt and clay. J. Tropic. For. 15:112–120. Scharenbroch, B.C., J. G.Bockheim,2007. Pedodiversity in an old-growth northern hardwood forest in the Huron Mountains, Upper Peninsula, Michigan. Canadian Journal of Forest Research 37, 1106–1117. Tamminen,P.and M.Starr,1994. Bulk density of forested mineral soils. Silva Fennica 28:53–60. Urbančič, M,P.Simončič,T.Prus and L.Kutnar, 2005.Atlas gozdnih tal Slovenije. Zveza gozdarskih društev Slovenije: Gozdarski vestnik: Silva Slovenica: Gozdarski inštitut Slovenije, Ljubljana. Van Wallenburg,C.,(1988)The density of peaty soils (in Dutch). Internal Report, Soil Survey Institute, Wageningen, The Netherlands, 5 pp. SAŽETAK: S obzirom na vremensku zahtjevnost i veliku količinu rada potrebnog za uzorkovanja i analize kemijskih i fizikalnih svojstava šumskih tala, razvoj alternativnih metoda je vrlo važan. Korištenjem pedotransfer funkcija (PTF), znanstvenici koji se bave proučavanjem tala mogu dobiti informaciju o najvažnijim svojstvima tala koja je inače teško (skupo ili vremenski zahtjevno) dobiti. PTF se mogu definirati kao statistički modeli za predviđanje fizikalnih (gustoća, hidraulička svojstva, itd.) i kemijskih (npr. kapacitet za izmjenu kationa) svojstava tla iz drugih, dostupnijih ili rutinski analiziranih svojstava. Cilj ovog rada je bio razviti lokalnu PTF za procjenu gustoće mineralnog dijela šumskih tala Slovenije. Na osnovi literature, hipoteza je bila da (1) gustoća snažno korelira s konce4ntracijom organskog ugljika (OC) i (2) lokalna PTF daje bolčje vrijednosti od objavljenih pedotransfer funkcija. Podaci 45 profila tla s bioindikacijske 16 x 16 km mreže u Sloveniji su analizirani s ciljem razvijanja lokalne pedotransfer funkcije za procjenu gustoće tla. Ukupno je obrađeno 106 profila tla. Uzorci za procjenu gustoće tla uzeti su u pet ponavljanja korištenjem metalnih O-prstenova zapremine 5 cm3. U laboratoriju su uzorci tla osušeni na 105 °C i izvagani za daljnje kemijske i fizikalne analize. Korištene su sljedeće analitičke metode: pH je određen u KCl prema ISO 10390 na automatskom ph-metru Metrohm Titrino, sadržaj C i N je određen prema ISO 10694 i/ili 13878 na elementarnom analizatoru Leco CNS-2000, karbonati prema ISO 10693 Scheiblerovim kalcimetrom a mehanički nsastav tla prema ISO 11277 sedimentnom metodom i pipetom prema Köhnu. Jednostavna i multipla regresija korištene su za predviđanje .b korištenjem različitih zavisnih varijabla, a testirani su također i regresijski modeli sa segmentnim odnosima. Koncentracija organskog ugljika (OC) dobro korelira (r = -0.861, p < 0.001) s gustoćom tla. Dva odvojena segmenta linije izjednačenja uklopljeni su u podatke koji su razdijeljeni u dva intervala prema sadržaju OC (ispod i iznad 36,0 g/kg). Gotovo 80 % varijabiliteta gustoće tla objašnjeno je segmentnom regresijom (Slika 4.). Lokalna pedotransfer funkcija uspoređena je s objavljenim funkcijama a četiri indeksa validacije (MPE, SDPE, RMSPE and R2) potvrdila su najveću kvalitetu predviđanja lokalne pedotransfer funkcije (Slika 5.). Razlike u procjeni zalihe ugljika u tlu (Cpool) različitih pedotransfer funkcija bile su veće od 160 t/ha (Tablica 4.). Predviđanje zaliha ugljika moglo bi biti značajno unaprijeđeno kalibracijom koeficijenata u modelima pomoću podataka razvrstanih prema vrsti tla. Ključne riječi:pedotransfer funkcija PTF, organski ugljik OC, segment na regresija, šumsko tlo, zaliha ugljika Cpool |
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M. Kobal, M. Urbančič, N. Potočić, B. De Vos, P. Simončič: PEDOTRANSFER FUNCTIONS FOR BULK DENSITY... Šumarski list br. 1–2, CXXXV (2011), 19-27 Table 3 Morphological, physical and chemical properties of four soil profiles for evaluating C estimations. pool Tablica 3.Morfološka, fizikalna i kemijska svojstva četiri profila tla za ocjenu kvalitete predviđanja zalihe ugljika Profile Profil Horizon Horizont Horizon boundary Granica horizonta Upper Lower Gornja Donja Physical soil properties Fizikalna svojstva tla Clay Silt Sand Stoniness Glina Prah Pijesak Kamenitost .b Chemical soil properties Kemijska svojstva tla OC N pH CEC BSKIK -Cm Cm % % % % g/cm 3 % % -cmol/kg % Zajama AC CA 0 13 13 33 9.9 48.2 42.0 15 20.2 57.0 22.8 40 0.520 0.737 9.47 0.78 6.99 83.89 100 6.52 0.56 7.24 68.28 100 Lubnik AC BC 0 15 15 45 32.5 51.9 15.6 15 40.7 43.1 16.1 40 0.719 0.801 10.70 0.73 7.13 66.72 100 8.47 0.68 7.18 58.05 100 Besnica A Bv BC CB 0 3 3 29 29 49 49 82 20.4 27.7 51.9 5 12.2 40.3 47.5 8 18.1 36.1 45.8 30 18.9 34.0 47.2 65 0.842 1.468 1.529 1.474 8.38 0.5 3.47 12.62 45.7 1.06 0.06 3.89 5.39 15.7 0.45 0.03 4.01 4.16 10.8 0.49 0.03 4.00 4.43 12.5 Merljaki A E BE BC 0 10 10 47 47 85 85 122 25.4 40.8 33.8 5 21.5 50.6 27.9 10 26.5 43.2 30.3 13 32.9 46.5 20.6 13 0.928 1.239 1.339 1.206 6.86 0.46 3.69 10.95 40.3 0.67 0.06 3.81 7.18 6.6 0.63 0.05 3.84 5.95 5.6 0.69 0.05 3.95 5.65 12.3 Table 4 Estimated carbon stock (C ) tilldepth of parent material for four different soil profiles based on measuredand pool calculated bulk densities. Tablica 4.Zaliha ugljika do dubine matičnog supstrata za četiri profila tla, procijenjena na osnovi izmjerenih i izračunatih gustoća tla. Profile Profil Measured bulk density Izmjerena gustoća tla Carbon stock C pool in t/ha Zaliha ugljika Cpool u t/ha Bulk density calculated using PTF Gustoća tla izračunata pomoću PTF SFI 6 Jeffrey Harrison Tamminen Kaur Zajama Lubnik Besnica Merljaki 112.1 220.2 75.3 135.9 138.0 200.7 68.6 137.7 115.0 169.3 62.4 126.0 139.3 206.2 73.7 148.9 166.3 247.6 72.0 145.6 52.7 59.8 52.1 101.4 model is nicely predicting the.bof the profile ‘’Merljaki’’, whereas differences for other soil types are larger; i.e. even higher than 25t of OC per hectare (profile ‘’Zajama’’).Using non local PTFs drawn from litera ture may resulted in high differences between measured and calculated C up to 160 t of OC per hectare pool (profile ‘’Lubnik’’, PTF Kaur). 4. CONCLUSIONS – Zaključci Using national data from a 16 × 16 km plot network, we developed a pedotransfer functionfor bulk density of mineral forest soils of Slovenia. Most of the variability in soil bulk density can be explained by concentration of organic carbon. Adding other chemical (pH, N, CEC, BS) and physical soil properties (soil texture) in the regression equation did not significantly improve the prediction quality.The prediction quality of all five PTFs (Jeffrey, Harrison, Tamminen, Kaur and local SFI 6) were tested using four validation indices (MPE, SDPE, RMSPE,R2 ), the result being that local PTF SFI 6 gives the most accurate prediction of soil bulk density. The PTFs were also used for prediction of carbon stocks in forest soils. Unexpectedly, using the local PTF SFI 6 could still lead to possible inaccuracies of the C calculation higher than 25t of OCper hectare. pool Weassume that the main reason for that is a high pedodiversity of Slovenian forest soils, requiring additional soil .b sampling, especially for the main forest soil types. |
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