Nghiên cứu khả năng tích lũy sinh khối ở hệ sinh thái rừng thứ sinh lá rộng thường xanh tại kon hà nừng, gia lai TT

1 FOREST SCIENCE INSTITUTE OF VIET NAM TRAN HOANG QUY RESEARCH ON BIOMASS ACCUMULATION CAPACITY IN SECONDARY EVERGREEN BROADLEAF FOREST ECOSYSTEMS IN KON HA NUNG, GIA LAI PROVINCE SPECIALITY: FOREST MEASUREMENT AND PLANNING CODE: 9.62.02.08 SUMMARY OF FORESTRY DISSERTATION HA NOI, 2020 The work had been completed at Forest Science Institute of Vienam Scientific Suppervisor: GS.TSKH NGUYEN NGOC LUNG Reviewer 1: Reviewer 2: Reviewer 3: The dissertation had been defended at thesis assessment Council of Forest Science Institute of Vietnm, 04 Duc Thang Street, North Tu Liem district, Hanoi city Time …., date… month… year 2020 For more information of the thesis can be searched at: Library: Forest Science Institute of Vietnam National library of Vietnam INTRODUCTION The urgency of the research The accurate estimation of annual biomass increase of tropical forests were urgently to minimizing the inadequate of net carbon stock It was to be realized an inconsistence of former and current research relevant to the estimation of biomass and absorbability and emission of carbon of forest ecosystems The difference may be because inadequate data base and various methods used to estimate biomass and carbon of forests Former research often based on direct measurement in small sample plots leading to higher biomass estimate Recent research, however, was based on forest inventory data and supply biomass data at national level or regional level An adequate method to estimate forest biomass was urgently needed to minimize the inadequate in carbon monitoring There were many systematical and wholly researches on biomass and carbon accumulation for planted and natural forests that had been done in Vietnam which could be oriented for further research However, there were some limits: (i) Only determined biomass at the time of samples collection; (ii Used methods might destroy the research objects (cutting trees, digging soil to collect samples); (iii) Dead biomass accumulated annually had been not considered; (iv) Most research overlooked fine roots production dynamics (ø2mm and fine roots Ø300 m 3/ha) that were sample plots: 2, 6, and 9; (ii) Restoration forests were forests which had been harvested with higher intensity and had good restoration and become to the status medium or rich forest status according circulation 34 of MARD (forest volume around 200 m3/ha) that were sample plots: 1, 3, 4, 5, and 10 * The thesis additionally established experimental plots of 900 m each in two forest status The experimental layout method was according the research project: “Applying advanced methods in evaluating biomass accumulation potential of some main forest ecosystem in Vietnam” (done 3/2014-3/2016 by Tran Van Do et al., 2016 Data analysis + Species composition is percentage of species participating the forest association determining in1/10 of importance value of the species IV + The diversity Shannon-Wiener (H’) index calculated by formula: H’=∑(Ni)(lnNi) with i=1,2,…,s + Species mixed ratio calculated by S/N (in which S is the number of species and N is the number of individuals in the sample plot) 2.2.2 Research methods for content Experimental layout In each sample plot for two forest status, experimental layout was the same to ensure the objective accuracy of gained results At each site selected to collect data, establish a plot with area of 900 m (30 m x 30 m) These plots were divided into 25 subplots with an area 36 m (6 m x m) like figure 2.2 6m 6m 7 10 11 12 13 14 15 16 17 18 19 20 21 22 23 30m 24 25 30m 6m - - - - Figure 2.2 Experimental layout and collecting data in sample plots (30m x 30m) Note: In subplots (8, 12, 14, 18), fine roots were collected by soil, in one subplot there were 18 soil cores, a total 72 soil cores were collected cores in each plot were collected periodically month/time x plots = 12 core/4 months/time In subplots (7, 9, 17, 19), fine root development was determined by burying plate box (1 box/plot) with the size (long 29,7 cm, wide 21 cm) according to scan machine A4 Data collected by link with a computer to scan images, periodically one month/time from 1/2016-1/2017 Determine respiration of soil microorganism were conducted in subplots (3, 11, 15, 23) Besides, decomposition bag was used to determine respiration of soil microorganism had been also arranged in the same four subplots Data collection For trees with diameter at breast height ≥ 6cm: using dendrometer to measure diameter increase of trees (5 trees per species) For the litter fall: establish 12 subplots sized 1mx1m to collect litter falls of the forests to determine raw biomass and then bring to labor to dry by temperature 105oC to determine dry biomass Method to determine root biomass: • For coarse roots: Three 1-m x 1-m x 1-m soil blocks (1m soil volume) were used to measure the coarse root diameter in July 2015 and July 2016 In July 2015, the soil in each block was carefully removed using shovels and trowels The diameter of each root segment was measured at three - • points: two ends on the wall of soil block and at the middle The points for measuring coarse root diameter were marked with red ink and numbered After measurements were completed, the soil was filled in to cover all the roots In July 2016, the soil was removed again, and coarse root diameters were remeasured at the marked points and recorded The procedure was conducted carefully to minimally affect the roots and to ensure that the coarse roots continued to grow naturally To establish allometric relationship between biomass and diameter and length of root, coarse roots were excavated and measured in four 1-m × 1m × 1-m soil blocks (1-m3 soil volume) Two blocks were sampled in June (rainy season), and two others were sampled in December (dry season) in 2016 All coarse roots were collected, cut into segments, and measured for fresh mass (M in grams), length (L in centimeters), and diameters (Ø in centimeters) Coarse roots with a diameter of cm were cut with scissors and the larger roots were cut with a handsaw Fresh root segments were sampled and transferred to the laboratory to determine dry mass by drying to constant mass in an 80°C oven (Tran Van Do et al., 2018) For fine roots: using CIE (Continuous Inflow Estimate) of Osawa and Aizawa (2012) and modified by Tran Van Do et al (2015) to estimated fine root production Method to estimate decomposition death fine root: For evaluating the decomposition ratio of dead fine roots, RIWP sheets (root-impermeable, water-permeable) were used… The RIWP sheet has a pore size of approximately μm and blocks the ingrowth of almost all fine roots; however, fine soil particles, rainwater, mycorrhizal hyphae, and other microorganisms can penetrate through the sheet Put 1,2 - 1,7 g dry roots in the bag, each bag has own sign Before burying to the field all sheets were soaked in ordinary water in room temperature for 24 hours to ensure that water content in fine roots inside bags was the same that the field The bags were buried at a depth of 20cm which is the zone most fine roots distribute in the soil 40 litter bags were buried at points of the sample plot Litter bags were collected periodic with the same time to collect soil cores, each time collected bags to determine decomposition ratio in each period correlatively After collection, the remaining fine roots inside the litter bags were separated from soil particles by washing and sieving and then oven-dried to constant mass for calculating the decomposition ratio Selection allometric equation between biomass and diameter D1,3 - Applied the method proposed by Kettering et al., (2001), in which allometric equation between individual biomass and breast height diameter had been used Equation B = aD1,3b, in which B is biomass, D1,3 is diameter at breast height and a and b are parameters - Using biomass data of 36 sample trees to determine biometric relationship equation to estimate AGB of individual trees of evergreen broadleaf forest Methods to determine respiration of soil micro-organism Two different methods were used to compare and determine suitable levels of each method for research respiration of soil micro-organism in Vietnam forests (i) Determine respiration of soil micro-organism by using tight bunch used gas exchange machine (ii) Determine respiration of micro-organism by using litter decomposition bags Data were collected periodic months/time for living, death roots, death biomass aboveground (Litter falls) Data analysis • Net primary production (NPP) NPP had been calculated by formula 2.2: (2.2) In which: ΔM is AGB increase; ΔCr: Increase of coarse roots; Lf: Biomass of litter falls; Fp: Biomass of fine roots (i) AGB increase (ΔM) was determined by the formula 2.3: (2.3) In which: Mi= biomass at first measurement; Mj = biomass at the second measurement (ii) Biomass increase of coarse roots (ΔCr): Diameter and length of coarse roots collected at time ti and tj (Δt; tj ≥ ti) were used to estimate biomass of coarse roots per unit of surface area during ∆t, assuming that soil block with surface area A (m 2) were sampled Allometric relationship of coarse root biomass to root diameter and length were determined from a separate sample of the coarse roots that were excavated, collected, cut into segments measured for length L (cm) and diameter Ø (cm) and then oven-dried to constant mass Cr (g) The 10 allometric relationship between root segment dry mass (Cr) and Ø2·L was established using the following equation: Cr =a (φ2.L)b (2.4) In which a and b are parameters to be estimated Equation 2.4 can be expressed as a lineare relationship on double logarithmic coordinated as shown in equation 2.5: logCr = loga + blog(φ2.L) (2.5) The parameters a and b were estimated based on observed data for L, φ and Cr from coarse roots in sample soil blocks The mass of coarse roots per unit surface area (A) can be calculated from equation 2.5 if the coarse root diameter and length data from the soil block of A surface area are known Next, if the diameter and length of coarse roots in a soil block of a known surface area (e.g., m2) are measured at times ti and tj (Δt; t j ≥ t i), coarse root production (ΔCr) during Δt can be estimated using equation 2.6: ΔCr = Crj - Cri (2.6) where Cri and Crj are estimated from Equation 2.4 with Li (length of coarse root at time ti), Øi (diameter of coarse root at time ti), Lj (length of coarse root at time tj), and Øj (diameter of coarse root at time tj) Through this allometric conversion, the mass of coarse roots (e.g., g m−2) can be estimated at times ti and tj, respectively Biomass of litter falls (Lf) was determined by formula 2.7 (2.7) In which: Vi is litter mass of each subplot to collect litter falls; Si is plot area Fine root biomass (Fp) was detemined by formula 2.8: (2.8) In which: Bi and Bj are live fine root biomass in the same unit area at the measured time (ti) and at the second measurement (tj); Ni and Nj are fine root death biomass in the same unit area at the measured time ti and tj; γij is decomposition ratio of ine roots in the time ∆t (tj-ti) Bi, Bj, Ni, Nj were determined by soil cores as described above (iv) Allometric relationship establishment for coarse roots: The root segments were classified into five groups based on diameter: Ø = 0,2-1,0; >1,0-2,0; >2,0-3,0; >3,0-5,0 cm and >5,0 cm Five corresponding relationships were fit using regression to estimate parameters a and b shown in Equation 2.5, the coefficient of determination (R2 ), and the bias The deviation of the predicted vs measured masses of 13 undestroyed sample trees Most used allometric equation was power equation in form B = aD1,3b which B is biomass, D1,3 is diameter at breast height and a and b were estimated parameters varying dependent on sites This variation was the source of main estimated error by using relationship equations undistinguished particular sites However Gathering B and D 1,3 for each particular site is impossible due to destroying research objects Method to select parameter a and b undestroying research objects had been proposed by Kettering et al., (2001) Parameter a and b can be estimated by relationship between H and D1,3 specifical for each site: H = kD 1,3c and so b = c + Parameter a can be estimated from average wood density (WD) of the site with a = WD*r in which r is a relatively stable coefficient From dataset of 36 sample trees in Kon Ha Nung The thesis had been determined the relationship between height and diameter, that was: H = 2,732*D0,57 (R=0,992) So that b = 2+c = + 0,57 = 2,57 Parameter a=WD*r The relationship equation will became B = r*WD*D1,3c+2 (Quirine M Ketterings et al., 2001) [67] Using data collected in Kon Ha Nung there are r = 0,151 and average wood density is 0,5, so that, parameter a of the equation is a = 0,151*0,5 = 0,0755 The allometric equation to be finded is: B = 0,0755*D1,32,57 (R = 0,995) The thesis had used data collected from 36 sample trees to validate relationship equations often used in order to find one which was most suitable (which had minimal error) The absolute value of minimal error is 8,4% and the maximal is 79,8% and the average error is -36,8% Equation had positive error and 29 negative error, average error is -8,9%, minimal absolute error is 0,1% and maximal absolute error is 43,8% Equation was similar equation 1, there is no positive error had systematical errors and estimated higher biomass Equation had positive error and 33 negative error estimated higher biomass with average was 53,5%, absolute value of minimal error is 17,5% and maximal is 111,7% Equation had average error of -5,8%, minimal absolute error is 0,5% and maximal is 16,3% Equation had maximal average error (-105%), minimal absolute value was 31% and maximal was 163,6% Equation had average error of -23,3%, the value of absolute minimal error was 1% and maximal was 52,4% Equation had positive and negative error equal (17 and 19) average error is -0,8%, minimal absolute value of error is 0,1 and maximal is 31,2 14 So that, equation 8: B=0,0755*D2,57 had been chosen to estimate AGB 3.2.1.2 AGB estimated from 10 permanent sample plots a) Living biomass Calculation results from permanent sample plots of minorinfluenced forests were accumulated in table 3.7a Table 3.7 AGB and AGB increase in 10 permanent sample plots Table 3.7a AGB and AGB increment of permanent samle plots in minorinfluenced forests N (trees/ha) Dtb (cm) G (m /ha) M (tons/ha) Plot 2012 2017 2012 558 550 432 2012 2017 25.6 26.7 40.86 465 26.1 26.1 529 541 24.8 557 563 23.3 TB 519 STD 2012 2017 43.4 354.73 374.7 3.99 33.6 36.87 301.76 334.7 6.59 25.5 39.18 42.87 363.71 385.22 4.30 24.4 34.7 38.55 301.34 322.27 4.19 530 24.95 25.66 38.25 40.42 330.39 366.72 4.77 59.54 44.10 1.22 2017 ΔM (t/ha/n ) 0.98 3.18 3.21 33.50 33.81 1.22 Table 3.7a showed that AGB of permanent sample plots of minorinfluenced forests varied from 301.34 to 363.71 tons/ha averaging 330.39 ± 33.50 tons/ha in 2012 and increased to 322.27 - 385.22 tons/ha averaging 366.72 ± 33.81 tons/ha in 2017; thus AGB increment of minorinfluenced forests varied from 3.99 to 6.59 tons/ha/year averaging 4.77 ± 1.22 tons/ha/year Table 3.7b Dry BGB and BGB increment of permanent sample plots in restoration forests 15 N (trees/ha) Dtb (cm) G (m /ha) M (tons/ha) Plot 2012 2017 2012 2017 2012 2017 397 410 24.5 24.7 24.7 25.5 196.54 197.79 0.25 434 453 24.2 25.0 26.28 29.4 202.46 238.85 7.28 483 510 22.5 23.1 25.89 28.68 198.3 241.32 8.60 648 640 21.6 22.6 241.66 274.68 6.60 626 643 23.1 23.9 34.41 38.3 259.87 296.68 7.36 10 616 614 22.5 23.2 32.83 35.7 254.55 288.94 6.88 TB 534 545 23.07 23.75 29.37 32 225.56 256.38 6.16 STD 109 101.3 1.11 0.95 32.11 34.42 4.20 4.88 2012 ΔM (t/ha/n ) 29.65 2017 37.38 2.98 Calculation results from permanent sample plots of restoration forests were systhezed in table 3.7b shown that AGB varied from 196.54 to 259.97 tons/ha in year 2012 meet an average of 225.56±29.65 tons/ha increasing from 197.79 to 296.68 tons/ha with average of 256.38±37.38 tons/ha in year 2017 so that AGB-icrease of restoration forests varied from 0.25 to 8.60 tons/ha/year with an average of 6.16±2.98 tons/ha/year Biomass increase in restoration forests is higher than in little influenced forests but varied in a wider range These were agreed with biological rules in forests b) Biomass of litter falls Table 3.8 were litter falls (VRR) had been synthezed from permanent sample plots of status little influenced forest and permanent sample plots of status of restoration forest From this table was shown that litter fall bimass in little influenced forest varied from 6.40 to 7.74 tons/ha/year meet an average of 7.28±0.40 tons/ha/year; For restoration forest Litter fall biomass varied from 6.39 to 12.19 tons/ha/year averaged of 9.19±2.34 tons/ha/year higher than that in little influenced forest but had wide variation saying that the restoration had been not yet stable as the little influenced forests 16 Table 3.8 Litter fall bioomass in evegreen broadleaf forests Little influenced forests PLOT Mean Standar d error VRR in Subplot (g/m2/day) 1.89 2.05 1.92 2.12 2.00 0.11 Restoration forests Convert to tons/ha/year ) 6.90 7.48 7.01 7.74 7.28 0.400 PLOT 10 Mean Standar d error VRR in subplot (g/m2/day) Convert to tons/ha/year) 1.75 2.34 3.34 3.25 2.08 6.39 8.54 12.19 11.86 7.59 2.34 2.52 8.54 9.19 0.64 2.34 3.2.1.3 AGB calculated from Experiments a) Litter fall biomass (death biomass) Litter fall biomass of experimental plots from two forest status was synthesized in table 3.9 shown that litter fall biomass in restoration forest was higher than that in little influence forest Litter fall mass had an average of 1.64 g/m2/day (equivalent with 6.02 tons/ha/year) for the little influenced forest and 2.26 g/m2/day (equivalent with 8.25 tons/ha/year) for the restoration forest Litter fall mass here were lower than in permanent sample plots but these differences were statistical not significant Table 3.9 Litter fall biomass in two forest status Little influenced forest Restoration forest Plot (g/m2/day) (tons/ha/year) (g/m2/day) (tons/ha/year) 1.75 638.75 2.01 733.65 1.42 518.3 2.42 883.3 1.74 635.1 2.36 861.4 Average 1.64 598.6 2.26 824.9 Standard error 0.19 69.35 0.22 80.3 b) AGB-increase Based on data of diameter increase measured by dendrometer AGBincrease were estimated by equation 2.4 as the form: ΔM = ΔM= a*Dj^b - a*Di^b 17 In which parameter a = 0.0755 and b = 2.57 In sample plots of little influenced forest there were 90 trees of 18 species measured and estimated an average biomass-increase of 6.35±6.08 kg/tree the tree had maximal biomas-increase of 22.44 kg and the minimu of 1.08 kg For the restoration forest there were 120 trees of 24 species (5 trees/species) and estimated an average biomass-increase of 7.87±7.75 kg/tree with tree had minimal biomass-increase was 0.99 and maximal of 29.24 kg (see table 3.10b) AGB-increase in little influenced forest was 4.31±0.17 tons/ha/year and in restoration forest was 6.91±0.32 tons/ha/year higher than that in little influenced forest Biomass increase in restoration forests varied wide than that in little influenced forests Larger diameter classes played higher role for biomass increase for both forest status In the little influenced forests all trees with diameter D1.3 ≤ 30 cm contributed of 14 % of total AGB and 30.8 % of biomassincrease (figure 3.3) While in restoration forest all trees with D 1.3 ≤ 30 cm contributed of 13.3 % of total AGB and 10.7 % AGB-increase (figure 3.4) Figure 3.3 Biomass and biomass-increase in little influenced forests in Kon Ha Nung Figure 3.4 Biomass and biomass-increase in restoration forests in Kon Ha Nung c) Total AGB The total AGB of evergreen broadleaf forests in Kon Ha Nung had been synthesized in table 3.11 shown that AGB-increase varied from 10.3 to 15.2 tons/ha/year maximal in restoration forest and minimal in little influenced forest In Which the percentage of live biomass account from 40.6 to 52.3% and litter fall biomass (death biomass) account from 47.7 to 59.4% Table 3.11 AGB-increase of forests in Kon Ha Nung Litter fall biomass Little influenced forests AGB (tons/ha/year) 6.02 ± 0.66 Carbon 3.01 (tons/ha/year) Percentage (%) 47.7 Restoration forests AGB (tons/ha/year) 8.25 ± 0.88 Live biomass Total AGB 4.31 ± 0.17 10.33 ± 0.83 2.20 516 52.30 6.91 ± 0.32 15.16 ± 1.20 18 Carbon (tons/ha/year) Percentage (%) 4.12 3.30 55.70 43.30 7.60 These results shown that AGB-increase in little influenced forests was (10.33 tons/ha/year) and less than that in restoration forests (15.16 tons/ha/year) 3.2.2 BGB accumulation capacity 3.2.2.1 Coarse root increase Measured data were used to estimate coarse root masses in experimental time from June 2015 to June 2016 based on relationship equation between root production and diameter and length after the formula (2.4): Cr=a*(φ2*L) The difference in the root masses between June 2015 and 10 June 2016 was coarse root production for 371 days on a soil surface area of m2 Differences were calculated separately for the experimental soil blocks The mean and standard error were calculated for each soil plot with surface area m These values were then converted to production units g/m2/day and tons/ha/year (see table 3.14) Table 3.14 Coarse root production increase between two measurements Soil block Biomass (g/m2) Biomass (1m2 production in sureface 4.6.2015 10.6.2016 371 days g/m2 area 268.1 631.4 363.3 0.98 542.3 880.4 338.1 0.91 339.9 701.4 361.5 0.97 Mean Standard error 383.43 737.73 354.3 0.95 116.1 104.85 11.48 0.03 Biomassincrease (g/m2/day) Allometric relationship between coarse root production and diameter and lenght Figure 3.7 Regression between the logarithm of Ø2L and that of root mass (g) each dot corresponds to a root segment Root mass is a function of root diameter and length Regression analyze results had determinated relationship equation of: y=0.913x+0.064 (figure 3.7) in the form of equation (2.6) in the methodology The equation had high correlation coefficient (R2 = 0.975) Replace the parameters of equation 2.6 in equation 2.5 we have: 19 Cr = 1.1588x(Ø2×L)0.913 Estimated results of coarse root production had been synthezed in table 3.14 Coarse root production divided into diameter classes had been susthezed in figure 3.8a shown that higher biomass production was observed in smaller coarse roots coarse root in the 0.2-1.0 cm diameter class produced 0.42 g/m2/day contributing to 42.9% of the total coarse root production (figure 3.8b) Production was 0.27; 0.17; 0.06 and 0.03 g/m2/day for the 1.0-2.0; 2.0-3.0; 3.0-5.0 and >5.0 diameter classes respectively The contribution to total coarse root production was 29; 19; and 3% for the 1.0–2.0; 2.0–3.0; 3.0–5.0 and >5 cm diameter classes respectively The total coarse root production measured in research region in Kon Ha Nung was 0.95 ±0.19 g/m2/day equivalent of 3.5±0.68 tons/ha/year (table 3.15) Table 3.15 Coarse root production estimating for forests in Kon Ha Nung Diameter class (cm) Coarse root production (g/m2/day) Mean Standard error Coarse root production (tons/ha/year) Mean Standard error 0.2-1.0 0.42 0.05 1.6 0.18 1.0-2.0 0.27 0.01 0.04 2.0-3.0 0.17 0.0006 0.6 0.02 3.0-5.0 0.06 0.003 0.2 0.01 >5.0 0.03 0.0002 0.1 0.01 All roots 0.95 0.19 3.5 0.68 a b Figure 3.8a Coarse root production increase (g/m /day); b Contribution of diameter classes (%) 3.3.2 BGB-increase for fine roots a Distribution of fine roots in soil depth Fine roots distributed mainly in the soil depth 0-20 cm for both forest status little influenced forest (figure 3.9a) and restoration forest (figure 3.9b) The more deep in the soil the little fine roots contributed In the little influced forest there were 50.9 % of fine roots distributed in depth 0-20 cm while in restoration forest were 53.8 % Fine roots live die and decompose in little influenced and restoration forests had no differences 20 Fine roots had decomposed of 0.006 g/m2/day in little influenced and 0.011 g/m2/day in restoration forest Fine roots dead in the both forest status were the same While fine roots produced in little influenced forest was 0.20 g/m2/day and in restoration forest was 0.18 g/m2/day a b Figure 3.9a Fine root distribution in soil depth of little influenced forest; b Fine root distribution in soil depth of restoration forest Figure 3.9 presented live fine root distribution thể (figure 3.9a) and death fine root (figure 3.9b) in the depth of soil From figure 3.9a shown that on research forests live fine roots can contribute in depth of m However more than 50% of live fine roots distributed in 0-20 cm here is the soil layer with high humus matters nutrients and water Therefore live fine roots were concentrated to take up them to feeed trees These leads to the distribution of death roots in this layer b Decomposition rate of fine roots Decomposition rates of fine roots in litter bags buried in different locations were different variation range of decompositon rates was from 0.39 to (nearly entirely decomposed) These shown that micro enviroment in baried location affected decomposion rates Litter bags baried in high humid sites with rich humus and micro organism acted strongly had high decomposion rate After barying 10 months fine roots had decomposion rate of an average 0.54 Like that after ca 20 months death fine roots will be composed entirely to give back nutrients to the soil Decomposion rate of fine roots dependend mainly on climate conditions where high humid high temperature and high rain falls promoting microorganism action then the decomposion rate increased Decomposition rate of fine roots with diameter (ϕ ≤ mm) was hihgher than that of which with diameter (1 < ϕ ≤ mm) In the time from March to June decompostion rate of fine roots was 0.284 while that of larger fine roots was 0.213 (Tran Van Do cộng 2015) c Fine roor production Fine root live, die and decompose in little influenced forest (figure 3.10a) and in restoration forest (figure 3.10b) were no difference Fine roots were decomposed of 0.006 g/m2/day in little influenced forest and 0.011 g/m2/day in restoration forest Death fine roots were the same in both forest status While total fine root production in little influenced was 0.20 g/m2/day and in restoration forest was 0.18 g/m 2/day Total fine root 21 production in little influenced forest was 0.73±0.28 and in restoration forest was 0.66±0.25 tons/ha/year Besides, fine root biomass depends on forest objects research region forest age climate conditions soils Like that research on fine root production had been conducted for each forest climate and soil zone differently From that the understand about fine root production the role of fine root in forest ecosystems and carbon cycle in forest ecosystems sufficient and perfect Total biomass produced below ground was synthezed in table 3.18 shown that BGB of minor-influenced forest was 4.24±0.96 tons/ha/year (in which coarse root biomass account of 85.1%) and in restoration forest was 4.16±0.93 tons/ha/year with 84.5% of coarse root a b Figure 3.10a Fine root decomposed (d) die (m) and live (p) in little influenced forest; b Fine root decomposed (d) die (m) and live (p) in restoration forest Table 3.18 BGB of experiment forest Fine root (tons/ha/year) Coarse root (tons/ha/year) Total (tons/ha/year) Little influenced forest 0.73±0.28 3.5±0.68 4.24±0.96 Restoration forest 0.66±0.25 3.5±0.68 4.16±0.93 3.2.2.3 Respiration of soil micro-organism Results had differences due to measurement time and forest status (figure 3.11) From figure 3.11 had seen that respiration of microorganism in both forest status in August (rainy season) was higher than in June (dry season) Respiration in restoration forest was higher than that in little influenced forest Figure 3.11 Respiration of soil micro organism in forests of Kon Ha Nung Respiration of soil micro organism in little influenced forest was lower than that in restoration at any time (figure 3.11) The mean value of respiration of soil micro organism was 3±0.2 g/m 2/day (equivalent with 1.5 g carbon/m2/day) for little influenced forest and 3.4 g/m 2/day (equivalent with 1.7 g carbon/m2/day) for restoration forest (table 3.19) Table 3.19 Average respiration of micro organism in forest ò Kon Ha Nung Biomass (g/m /day) Little influenced forest 3.0±0.2 Restoration forest 3.4±0.3 22 Carbon (g/m2/day) 1.5±0.1 1.7±0.15 So that, average respiration of soil micro organism in forests of Ko Ha Nung was 10.95±0.73 tons/ha/year dry biomass in little influenced forest lower than that in restoration forest 12.41±1.10 tons/ha/year 3.2.3 Total biomass accumulated annually Table 3.20 Total biomass- increase of research forests AGB Little influenced forest Biomass 10.33±0.83 (tons/ha/year) Caron 5.2 (tons/ha/year) Restoration forest Biomass 15.16±1.2 (tons/ha/year) Carbon 7.6 (tons/ha/year) BGB Respiration Total 4.24±0.96 10.95±0.73 3.62±1.06 2.12 5.45 1.8 4.16±0.93 12.41±1.10 7.02±1.03 0.99 6.2 3.5 Results synthezed in table 3.20 shown that biomass increase annually in little influenced forest was 3.62±1.06 tons/ha/year lower than that in restoration with 7.02±1.03 tons/ha/year Biomass accumulation capacity of natural forests in north west region was 7.82 tons/ha/year for little influenced forest (IIIB) was 13.36 tons/ha/year for restoration forest Biomass accumulation capacity in north east region was 6.8 tons/ha/year for little influenced forest was 7.6 tons/ha/year for restoration forest (Trần Văn Đô et al 2016) Like that, biomass calculation capacity of evergreen broadleaf forests were different in ecological zones and depended strongly on forest status General trend was minor-influenced forests accumulated lower biomass than restoration forests in all ecological zones These are corresponded with natural rules 3.3 Enhance the accurate of root biomass estimating methods Belowground net primary production (BNPP) in forests included fine root production and coarse root production It is not easy to estimate BNPP because roots are small numerous and underground Destructive sampling by excavating entire root systems of individual sampled trees can miss up to 30% of coarse roots This is a costly labor-intensive method and it may be prohibited in context of forest ban The thesis applied a new developed method to estimate coarse root production without destructive sampling of trees The method is developed and applied to an evergreen 23 broadleaf forest in Vietnam (Tran Van Do cộng 2018) [103] Measurements of diameter and length of coarse roots at times tj and ti (Δt; tj ≥ ti) are used to estimate root mass (Cr) from root length and diameter squared (Ø2·L) The results indicated that the combined error of estimating mass of coarse roots using such relationship was a 3.4 % overestimate The coarse root production in this study was 0.99 g/m2/day It is concluded that the present method to estimate coarse root production using allometry betwen Cr and Ø2·L is relatively easy and applicable to any forests where small blocks of soil can be excavated to measure and remeasure coarse roots over time 3.3.1 Enhance the accurate of coarse root biomass estimating method 3.3.1.1 Allometry establishment A total of 268 coarse root segments were collected in the 31-m soil blocks (1 m × m × m) There were root segments in the >5 cm diameter class root segments in the 3.0–5.0 cm diameter class 11 root segments in the 2.0–3.0 cm diameter class and 95 root segments in the 0.2–1.0 cm diameter class (Table 3.21) The total length of the coarse root segments collected in the soil blocks was 16.590 cm and the total dry mass was 16.257 g (Table 3.21) The increase in diameter of the coarse roots was size-dependent with smaller roots on average increasing more in diameter than larger roots The diameters of the larger coarse root classes increased less (Table 3.21) Coarse roots had diamter of 0.2 - 1.0 cm contained up to 60% water whereas roots >5 cm in diameter contained 34% water The range of moisture content was greater in the smalle diameter classes The validation statstics for different root diameter classes and a combined diameter class are shown in table 3.22 The calibrated model for roots >5.0 cm in diameter had the lowest percentage bias of –2.3 Bias increased progressively for the 0.2–1.0 cm diameter class (–4.1) the 2.0–3.0 cm diameter class (–5.1) the 1.0–2.0 cm diameter class (–8.0) and the 3.0–5.0 cm diameter class (–13.3) The general model for the mixed diameter class of all root segments had –6.6 percent bias The allometric relationship based on all coarse root segments combined had a correlation coefficient of determination (R ) of 0.84 (Table 3.20) and it overestimated total coarse root biomass by 18.8% (Table 3.22) The combined estimation errors were smaller when the five coarse root diameter classes were applied individually with their corresponding allometric relationships (Table 3.22) 24 Table 3.21 Length, diameter increase, dry mass and moisture of coarse roots Diameter class (cm) 0.2 - 1.0 1.0 - 2.0 2.0 - 3.0 3.0 - 5.0 >5.0 Total Diameter increase (mean±SE) 1.34 ± 0.12 1.06 ± 0.11 0.95 ± 0.08 0.88 ± 0.06 0.63 ± 0.07 Range of water content (%) Number of root segments Range of root length (cm) Range of dry root mass (g) 152 - 125 - 129 48 - 60 95 11 268 - 98 - 89 - 78 27 - 48 16.590 - 288 - 418 44 - 876 400 - 1.188 16.257 43 - 50 40 – 46 36 - 42 32 - 34 Table 3.22 Validation for models of different diameter classes and mixed diameter Diameter class (cm) 0.2-1.0 1.0-2.0 2.0-3.0 3.0-5.0 3.0-5.0 3.0-5.0 Percentage bias RMSPE MAPE -4.1 -8.0 -5.1 -13.3 -13.3 -13.3 31.7 36.3 7.8 35.6 35.6 35.6 19.8 19.7 13.6 29.0 29.0 29.0 Table 3.23 Estimated parameters of relationship equations for diameter classes Diameter class (cm) 0.2-1.0 1.0-2.0 2.0-3.0 3.0-5.0 >5.0 All roots R2 Estmated parameters a 0.68 1.8289 0.76 0.5568 0.88 0.2056 0.87 2.7416 0.67 2.1717 0.9758 0.9131 b 0.6104 1.0085 1.1820 0.7213 0.8147 0.9758 Standard error a 0.0781 0.0902 0.1337 0.0861 0.1501 0.0504 b 0.0653 0.0672 0.0540 0.0394 0.1088 0.0424 Estmation error of coarse root biomass (%) 11.6 3.9 0.3 5.2 0.6 18.8 The relationship for coarse roots with diameter >5 cm had a correlation coefficient of 0.67 and overestimated 0.6 percent of the root mass The relationship for coarse roots in the 3.0–5.0 cm diameter class had a correlation coefficient of 0.87 and overestimated the root mass by 5.2 percent The relationship for coarse roots in the 2.0–3.0 cm diameter class had a correlation coefficient of 0.88 and underestimated root mass by 25 0.3 percent The relationship for coarse roots in the 1.0–2.0 cm diameter class had a correlation coefficient of 0.76 and overestimated root mass by 3.9 percent The relationship for coarse roots in the 0.2–1.0 cm diameter class overestimated by 11.6 percent (Table 3.23) The combined error for estimating coarse root mass using these five relationships was a 3.4% overestimate compared with an overestimate of 18.8 percent for the single combined model Root mass is a function of root diameter and length From collected number of root segments root length and diameter and mass (table 3.20) it was established relationship equation between survey information for all coarse roots in form of linear equation as y=0.913x+0.064 (figure 3.8) equation (2.6) in methodology The equation had a correlation coefficient of (R = 0.975) and can be used to estimate coarse root biomass without collecting root data by traditional destructive method Replace the parameters of equation 2.6 in equation 2.5 we have: Cr = 1.1588x(Ø2×L)0.913 Research results shown that coarse root biomass had of 18.8% overestimated so that the accurate of relationship equation was high and can be used to estimate coarse root biomass of the forests 3.3.1.2 Improvement of coarse root estimation method The method used in this thesis was modified to be simple and easy to apply with a minimum of equipment and without destructive sampling of trees Trees are not excavated; rather a sample of coarse roots is collected to establish the allometric relationship of root mass to diameter and length Repeated measurements over time of root diameter and length are used to estimate coarse root production for Δt It is easier to collect all coarse roots (Ø > mm) in sampling soil blocks using the present method than to collect all coarse roots of sampled trees using previously published methods which may miss 30 percent of all coarse roots when sampling sampled trees (Ogino 1977, Niiyama et al 2010) In this thesis, we divided the coarse root segments into five diameter classes (Ø = 0.2–1.0 >1.0–2.0 >2.0–3.0 >3.0–5.0 cm and >5.0 cm) and established five corresponding allometric relationships, which reduced composite estimation and resulted in a combined overestimate error of 3.4% Meanwhile, if we combined all root segments (Ø ≥ mm) and established one allometric relationship for all diameter classes, the coarse root biomass was overestimated by 18.8 percent 3.3.2 Improvement of fine root estimation method 26 In this thesis had been used the continous inflow estimate (CIE) developde and used by Osawa and Aizawa (2012) and had been modified by Tran Van Do et al (2015) to estimate fine root production Such modified methods use soil core technique for mass of living fine roots and that of death fine roots, and litter bag technique for decomposition ratio of death fine roots CIE method had been used to estimate fine root production, mortality and decomposition with a framework of two size classes of fine root and quantified the root mass transfering into coarse roots (>2 mm) Research results of Tran Van Do et al… (2015) indicated that larger fine roots were overestimated while divided fine root in two classes Using the framework with two diameter classes leading up to 21.3% overestimate of fine root production, that estimated without distinction of root size classes, when the amount of inter-class mass transfer was ignored Moreover, using a shorter observation interval leaded to overestmate of fine root production than that in longer observation interval Production estimated in observation interval of month was higher of 21.4% in compared with observation of months The use soil core technique with CIE methods through diameter class of fine roots is to minimize underestimates parameters of fine root dynamics through calculation of decomposition and death fine roots It trended to lead to 312% higher of total fine root production than that without distinction diameter class of fine roots when the estimates are based on two size classes without the consideration of mass transfer to coarse roots It also leads to overestimate of 14-21% of total fine root production without distinction diameter class of fine roots, when inter-class mass transfer was included In summary, the thesis showed that decomposition, mortality and production of fine roots could be evaluated using CIE method combined with burying scan mashine through explicitly considering root size classes and inter-class mass transfer of fine roots CONCLUSIONS WEAK-POINT AND RECOMMENDATIONS Conclusions (1) Valuating the biomass accumulation capacity of evergreen broadleaf forests in Kon Ha Nung: (i) AGB- increase includes death (litter fall biomass) and live biomass was estimated of 10.33±0.83 tons/ha/year in little influenced forests and 15.16± 1.20 tons dry/ha/year in restoration forests (ii) BGB-increase included coarse root biomss and fine root 27 biomass was estimated of 4.24± 0.96 tons/ha/year in little influenced forests and 4.27±0.93 tons dry/ha/year (iii) Respiration of soil micro organism in forests at Kon Ha Nung had a mean of 10.9±0.73 tons/ha/year dry biomass in little influenced forests lower than that in restoration forests with a mean of 12.4±1.09 tons/ha/year (iv) The biomass accumulation capacity of evergreen broadleaf forests at Kon Ha Nung was 3.62±1.06 tons/ha/year for little influenced forsts and 7.02±1.03 tons /ha/year for restoration forests (2) Enhance the accuracy of belowground biomass estimating for forest ecosystems: (i) The thesis had applied new developed method to estimate coarse root biomass through allometric relationship between coarse root dry mass (Cr) and (Ø2L) (Ø root diameter and L root length) The method was simple, easy to apply and had high accuracy and can be used for all forests Besides the method does not require destructive sampling of entire sample trees (ii) For estimating fine root biomass the thesis had used the CIE (continuous inflow estimate) that was modified by Tran Van Do et all (2015) In this method, death root decomposition was experiment by litter bag technique usedroot-impermeable water-permeable sheet - RIWP and used soil core technique combined with barying scan maschine to distinguish live root and death root at the experiment times Weak-point: (i) Due to limited fund and time the thesis had considered only in two forest status at Kon Ha Nung (ii) Research interval was short therefore the theoretical foundation and conclussions may be not significant there needs further research to be overcome Recommendations: (i) The research method was new for Vietnam it needs more test in other forest types like conifer forests or decidous forest (ii) Continue to study in wide scope to affirm the methods; (iii) BGB stimating method used in the thesis could be applied to monitor the carbon cycle in Vietnam ... in Kon Ha Nung, Gia Lai The two forest status chosen for the research were: (i) Little influenced forests (RiBTĐ); (ii) Restoration forests (RPH) Research scope: The experimental forest in Kon. .. evergreen broadleaf forest ecosystem in Ko Ha Nung, Gia Lai (2) Determining biomass increase of evergreen broadleaf forest in Kon Ha Nung, Gia Lai Thesis content The thesis had 117 pages, with... forest ecosystems in Kon Ha Nung Practical significance: The thesis has determined biomass accumulation capacity of the evergreen broadleaf forest ecosystems in Kon Ha Nung, Gia Lai Besides, the