Forest Primary Production Research Articles

Estimating local-scale forest GPP in Northern Europe using Sentinel-2: Model comparisons with LUE, APAR, the plant phenology index, and a light response function

Northern forest ecosystems make up an important part of the global carbon cycle. Hence, monitoring local-scale gross primary production (GPP) of northern forest is essential for understanding climatic change impacts on terrestrial carbon sequestration and for assessing and planning management practices. Here we evaluate and compare four methods for estimating GPP using Sentinel-2 data in order to improve current available GPP estimates: four empirical regression models based on either the 2-band Enhanced Vegetation Index (EVI2) or the plant phenology index (PPI), an asymptotic light response function (LRF) model, and a light-use efficiency (LUE) model using the MOD17 algorithm. These approaches were based on remote sensing vegetation indices, air temperature (Tair), vapor pressure deficit (VPD), and photosynthetically active radiation (PAR). The models were parametrized and evaluated using in-situ data from eleven forest sites in North Europe, covering two common forest types, evergreen needleleaf forest and deciduous broadleaf forest. Most of the models gave good agreement with eddy covariance-derived GPP. The VI-based regression models performed well in evergreen needleleaf forest (R2 = 0.69–0.78, RMSE = 1.97–2.28 g C m−2 d−1, and NRMSE = 9–11.0%, eight sites), whereas the LRF and MOD17 performed slightly worse (R2 = 0.65 and 0.57, RMSE = 2.49 and 2.72 g C m−2 d−1, NRMSE = 12 and 13.0%, respectively). In deciduous broadleaf forest all models, except the LRF, showed close agreements with the observed GPP (R2 = 0.75–0.80, RMSE = 2.23–2.46 g C m−2 d−1, NRMSE = 11–12%, three sites). For the LRF model, R2 = 0.57, RMSE = 3.21 g C m−2 d−1, NRMSE = 16%. The results highlighted the necessity of improved models in evergreen needleleaf forest where the LUE approach gave poorer results., The simplest regression model using only PPI performed well beside more complex models, suggesting PPI to be a process indicator directly linked with GPP. All models were able to capture the seasonal dynamics of GPP well, but underestimation of the growing season peaks were a common issue. The LRF was the only model tending to overestimate GPP. Estimation of interannual variability in cumulative GPP was less accurate than the single-year models and will need further development. In general, all models performed well on local scale and demonstrated their feasibility for upscaling GPP in northern forest ecosystems using Sentinel-2 data.

Responses of nutrient resorption to interannual precipitation variability and nitrogen addition in a pine plantation

AbstractPlant nutrient concentration and resorption play a major role in determining primary production and development of forests. Yet how plant nutrient concentration and resorption respond to altered interannual precipitation and how interannual precipitation variability mediates the responses of plant nutrient concentration and resorption to N addition remain uncertain. Here, we examined the responses of needle nutrient (N and P) concentrations of all aged classes, nutrient resorption efficiency, and proficiency to N addition (10 g N m−2 year−1) over a 4‐year period (from 2016 to 2019) that experienced high variability in precipitation in a Mongolian pine plantation in a semiarid region of northeast China. There were significant interannual variations in N and P concentrations in all aged needles and litters, N and P resorption efficiency, as well as soil inorganic N and available P concentrations in the control plots. Increased precipitation resulted in increased needle nutrient concentrations across the four consecutive years. However, needle nutrient concentrations did not covary with soil available nutrients. The responses of needle N resorption efficiency (NRE) to N addition differed across the four years, with N addition decreasing NRE in wet years, but not in dry years. Taken together, our results showed that foliar nutrient concentrations are sensitive to altered annual precipitation and that different interaction effects exist between N addition and sampling year for different nutrient variables. Such differential responses highlight the challenge of predicting effects of N addition on nutrient cycling over temporal scales.

Predicting ecosystem productivity based on plant community traits.

With the rapid accumulation of plant trait data, major opportunities have arisen for the integration of these data into predicting ecosystem primary productivity across a range of spatial extents. Traditionally, traits have been used to explain physiological productivity at cell, organ, or plant scales, but scaling up to the ecosystem scale has remained challenging. Here, we show the need to combine measures of community-level traits and environmental factors to predict ecosystem productivity at landscape or biogeographic scales. We show how theory can extend the production ecology equation to enormous potential for integrating traits into ecological models that estimate productivity-related ecosystem functions across ecological scales and to anticipate the response of terrestrial ecosystems to global change.

Mixed planting enhances soil multi‐nutrient cycling by homogenizing microbial communities across soil vertical scale

AbstractSoil multi‐nutrient cycling (SMC) driven by microbial communities determines the primary productivity of forests. While functional microbes and microbial community composition are thought to contribute to SMC, the importance of different microbial communities in SMC enhancement in forest ecosystems are largely unknown. Here, we aimed to decode the underlying mechanisms of bacterial and fungal communities in the surface (0–20 cm) and subsurface soils (20–40 cm) that drive SMC in two afforestation models: pure plantation (Chinese fir) and mixed plantation (different tree varieties). Mixed planting significantly enhanced the SMC along the soil vertical spatial scale, implying that mixed planting effectively alleviated soil degradation caused by pure planting. This was attributed to the homogenization effect of mixed planting on bacterial and fungal community assemblies across the soil vertical scales. Specifically, fungal communities mainly contributed to SMC in the surface soil in mixed plantations, and bacterial communities were the primary drivers of SMC in the subsurface soil. Furthermore, multiple regression and structural equation modeling confirmed that specific key fungal taxa preferentially mediate the transformation of available phosphorus and soil organic carbon, and contribute 77.0% of SMC variation in the surface soil, while bacterial taxa mainly drive the transformation of ammonium nitrogen and soil organic carbon, and account for 60.0% of SMC variation in the subsurface soil. This study highlights the different patterns of bacterial and fungal community assembly, and their function in SMC along the soil vertical spatial scale, and advances the understanding of the roles of soil microbial communities in degraded forest soil remediation.

Simulating the Impacts of Drought and Warming in Summer and Autumn on the Productivity of Subtropical Coniferous Forests

The impacts of drought and/or warming on forests have received great attention in recent decades. Although the extreme drought and/or warming events significantly changed the forest demography and regional carbon cycle, the seasonality quantifying the impacts of these climate extremes with different severities on the productivity of subtropical coniferous forests remains poorly understood. This study evaluated the effects of seasonal drought and/or warming on the net primary productivity (NPP) of subtropical coniferous forests (i.e., Cunninghamia lanceolata and Pinus massoniana forests) from Hengyang–Shaoyang Basin in southern China using the Ecosystem Demography model, Version 2.2 (ED-2.2) and based on the datasets from forest inventory, meteorological reanalysis, and remotely sensed products. The results showed that the goodness of fit of the DBH-height allometric equations was better than that of the default in ED-2.2 after model calibration; the ED-2.2 model qualitatively captured the seasonality of NPP in the subtropical coniferous forests; and the mismatch between simulated annual NPP and MODIS-NPP (MOD17A3HGF) became smaller over time. The effect of seasonal drought on NPP was greater than that of warming; the decline rate of NPP gradually increased and decreased with time (from July to October) under the seasonal drought and warming scenarios, respectively; NPP decreased more seriously under the combined drought-warming scenario in October, with an average decrease of 31.72%, than the drought-only and warming-only scenarios; seasonal drought had an obvious legacy impact on productivity recovery of subtropical coniferous forests, but it was not the case for warming. With the increase in drought severity, the average values of soil available water and NPP together showed a downward trend. With the increase in warming severity, the average values of canopy air space temperature increased, but NPP decreased. Seasonal drought and/or warming limit forest production through decreasing soil moisture and/or increasing canopy air space temperature, which impact on plant photosynthesis and productivity, respectively. Our results highlight the significance of taking into account the impacts of seasonal warming and drought when evaluating the productivity of subtropical coniferous forests, as well as the significance of enhancing the resistance and resilience of forests to future, more severe global climate change.

Лесные ресурсы Республики Гана

The Republic of Ghana is among the 50 countries in the world with the highest biodiversity of plant and animal species. The network of forest reserves in the country is the basis for the rich biodiversity conservation. One third of the Ghana’s territory was covered with natural rainforests at the beginning of the 20th century. These forests are now classified as reserves and vulnerable areas. Ghana has two main types of vegetation: savannahs and forests. Their features are determined by the amount of precipitation, moisture level, geology and soils. An inventory of Ghana’s forest resources revealed that the area of forests had been reduced by 19 %. Biodiversity conservation, climate change mitigation, ecotourism and the improvement of living conditions have become important areas in Ghana’s forestry. This has led to significant changes in forest management and logging technologies. The large-scale afforestation programs initiated in the country support the conservation of many populations of flora and fauna. The forest sector includes forest management and woodworking industry, which contribute significantly to currency earnings in Ghana via the export of wood products. Revenue from the export of primary forest products amounted to USD 230.2 million in 2015. Ghana uses 26 types of wood species to produce various parts of garden furniture for export. The forest sector employs more than 100,000 workers. Ghana earns enormous revenues from the export of sawn timber and wood products, so there is an afforestation program. The government’s policy is requiring all timber companies that cut down trees to plant seedlings for the restoration of rainforests. Ghana is ready for a large-scale afforestation campaign so as to contribute to solving the problem of deforestation and climate change that humanity is facing.

Survey and Ethnobotanical Investigations of Key Non-Timber Forest Products in Home Gardens and Sacred Forests in Cross River State, Nigeria

A survey and ethnobotanical investigations were done in six Local Government Areas (LGAs), Cross River State, Nigeria, to examine the social and economic value and management practices of the primary Non-Timber Forest Products (NTFPs) in home gardens and sacred forests. The six LGAs were purposively selected based on the vegetation types. The vegetation types include tropical high forest/freshwater swamp mosaic as ecotype 1, tropical high forest as ecotype 2 and tropical high forest/savannah mosaic as ecotype 3 for Akpabuyo and Akamkpa, Ikom and Obubra, and Obudu and Boki. The study was conducted in 12 sacred forests and 60 home gardens in 30 randomly selected communities within the 11six LGAs. A stratified multi-stage random sampling was used with group interviews, personal observation and structured questionnaires as study tools. Also, a simple close traverse was adopted for area determination for home gardens and sacred forests, while the stock survey approach was used for identification, enumeration and measurements of NTFPs/ tree species in the study areas. One thousand and fifty questionnaires were administered to respondents randomly selected from 30 communities in the six LGAs to provide data on NTFPs. Results showed that 69.5% and 61.5% of the respondents were involved in home garden farming and sacred forest conservation, while 64.2% were farmers and only 21% were involved in NTFPs production to supplement farm income. The product contributed significantly to household food security, and the majority of the respondents (62.9%) were women. Volume production in ecotypes 1, 2 and 3 was 18.40, 14.00 and 13.18 m³/ha, respectively. The analysis of variance showed that the number of stem/ha, the dbh (cm), the based area (m²/ha) and the volume (m³/ha) were not significantly different in the three ecotypes. The results also showed that ethnobotanical practices of NTFPs in home gardens and sacred forests have assisted in having proper methods of conservation and sustainable management of the existing forest products. Recommendations were proffered to eradicate or ameliorate the perceived problems. Finally, this study provides relevant information which would be inestimable in promoting and improving improper management of NTFPs in home gardens and sacred forests.

Estimation of gross primary productivity of Indian Sundarbans mangrove forests using field measurements and Landsat 8 Operational Land Imager data

Estimation of gross primary productivity of Indian Sundarbans mangrove forests using field measurements and Landsat 8 Operational Land Imager data

Emerging signals of declining forest resilience under climate change

Forest ecosystems depend on their capacity to withstand and recover from natural and anthropogenic perturbations (that is, their resilience)1. Experimental evidence of sudden increases in tree mortality is raising concerns about variation in forest resilience2, yet little is known about how it is evolving in response to climate change. Here we integrate satellite-based vegetation indices with machine learning to show how forest resilience, quantified in terms of critical slowing down indicators3–5, has changed during the period 2000–2020. We show that tropical, arid and temperate forests are experiencing a significant decline in resilience, probably related to increased water limitations and climate variability. By contrast, boreal forests show divergent local patterns with an average increasing trend in resilience, probably benefiting from warming and CO2 fertilization, which may outweigh the adverse effects of climate change. These patterns emerge consistently in both managed and intact forests, corroborating the existence of common large-scale climate drivers. Reductions in resilience are statistically linked to abrupt declines in forest primary productivity, occurring in response to slow drifting towards a critical resilience threshold. Approximately 23% of intact undisturbed forests, corresponding to 3.32 Pg C of gross primary productivity, have already reached a critical threshold and are experiencing a further degradation in resilience. Together, these signals reveal a widespread decline in the capacity of forests to withstand perturbation that should be accounted for in the design of land-based mitigation and adaptation plans.

Response of Spruce Forest Ecosystem CO2 Fluxes to Inter-Annual Climate Anomalies in the Southern Taiga

Climate extremes and anomalies modify the CO2 ecosystem–atmosphere exchange of the boreal forests and consequently alter the terrestrial carbon stocks and the atmospheric greenhouse gas concentrations. The effects of the anomalous weather conditions on the CO2 net ecosystem exchange (NEE), total ecosystem respiration (TER), and gross primary production (GPP) of the typical southern taiga nemorose spruce forest were analyzed using continuous eddy covariance flux measurements in the 2015–2020 period. The forest was found to be a source of atmospheric CO2 in 2016, 2017, 2019, and 2020 (the mean annual GPP/TER ratio was between 0.87 and 0.97). In 2018, the forest was found to act as a CO2 sink (GPP/TER = 1.47) when a positive temperature anomaly in the growing season was accompanied by a negative precipitation anomaly and increased global radiation. The early start of the CO2 uptake in the anomalously warm winter of 2019/2020 did not result in an increase in the annual GPP/TER ratio (0.90). The comparison of the flux data obtained from the nemorose spruce forest on the well-drained soils with the data obtained from paludified spruce forest in the same landscape showed that the mean annual GPP/TER ratio of the sites alternatively responded to the mentioned anomalies. This study suggests that a variety of soil moisture regimes across the southern taiga spruce forests provide a non-uniformity in the response reactions of the CO2 ecosystem–atmosphere exchange on the climate anomalies.

Stand carbon storage and net primary production in China’s subtropical secondary forests are predicted to increase by 2060

BackgroundForest ecosystems play an important role in carbon sequestration, climate change mitigation, and achieving China's target to become carbon (C) neutral by 2060. However, changes in C storage and net primary production (NPP) in natural secondary forests stemming from tree growth and future climate change have not yet been investigated in subtropical areas in China. Here, we used data from 290 inventory plots in four secondary forests [evergreen broad-leaved forest (EBF), deciduous and evergreen broad-leaved mixed forest (DEF), deciduous broad-leaved forest (DBF), and coniferous and broad-leaved mixed forest (CDF)] at different restoration stages and run a hybrid model (TRIPLEX 1.6) to predict changes in stand carbon storage and NPP under two future climate change scenarios (RCP4.5 and RCP8.5).ResultsThe runs of the hybrid model calibrated and validated by using the data from the inventory plots suggest significant increase in the carbon storage by 2060 under the current climate conditions, and even higher increase under the RCP4.5 and RCP8.5 climate change scenarios. In contrast to the carbon storage, the simulated EBF and DEF NPP declines slightly over the period from 2014 to 2060.ConclusionsThe obtained results lead to conclusion that proper management of China’s subtropical secondary forests could be considered as one of the steps towards achieving China’s target to become carbon neutral by 2060.

Combining NDVI, PRI and the quantum yield of solar-induced fluorescence improves estimations of carbon fluxes in deciduous and evergreen forests

We used automated spectroradiometers to continuously monitor changes in the optical parameters of phenological and photosynthetic traits in beech and spruce forests. We examined seasonal variations in the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and solar-induced fluorescence in the oxygen A band (SIFA) that was estimated using a 3-FLD discrimination method from radiance data. The optical parameters tracked the activation and cessation of photosynthesis in spring and autumn. Data at photon fluxes >1200 μmol m−2 s−1 during extended noon hours were used to link the seasonal PRI and SIFA variations to the dynamics of photosynthesis. Seasonal PRI was significantly correlated with photosynthetic light-use efficiency (LUE) with R2 values of 0.66 and 0.48 for the measurements in beech and spruce forests, respectively. SIFA emissions were significantly correlated with the gross primary production (GPP) of the evergreen spruce forest (R2 = 0.47), but R2 was only 0.13 when measured in the beech forest. The correlations between the optical parameters and GPP or LUE, however, tended to be lower when using a dataset with constant NDVI. Introducing an equation combining NDVI, PRI, and the quantum yield of SIFA emission increased R2 for LUE estimation to 0.77 in the spruce forest and 0.63 in the beech forest. GPP was estimated from the parametric equation with improved accuracy reaching R2 = 0.53 and RMSE = 5.95 μmol CO2 m−2 s−1 in spruce forest and R2 = 0.58 and RMSE = 5.23 μmol CO2 m−2 s−1 in beech forest. Parametric equations were more efficient in estimating photosynthesis in datasets that consisted of an entire season's data. By combining NDVI, PRI and the quantum yield of SIFA, we could thus substantially improve estimations of carbon fluxes in diverse deciduous and evergreen canopies.

Forest canopy nitrogen uptake can supply entire foliar demand

Abstract Plant canopies intercept, process and potentially assimilate atmospheric nitrogen (N) additions, but the forest‐scale effects of canopy processes on N cycling and plant nutrition are not clear. Substantial method artefacts and scaling issues exist in previous experimental studies which measure relevant N fluxes either at (a) natural abundance, (b) via a 15N tracer or (c) by incorporation of additional 15N into measured biomass, meaning these processes are often overlooked or discounted. Here, working in a mature Sitka spruce plantation and under ambient conditions, we used all three of these methods independently at stand, tree and branch scale to assess the effects of canopy interception on capture, assimilation and incorporation of inorganic N deposition by canopy biomass. We consistently found that above 70% of N deposition was unaccounted for (i.e. assimilated, processed beyond detectability, volatilised or retained) when passing through the canopy, independent of the assessment method. Using short‐term 15N tracers, we found that this unaccounted N was retained in canopy tissues. Apparent uptake from monitoring of ecohydrological fluxes was sustained and consistent, suggesting that the tree canopies were a sink for N deposition. Seasonal variation in recovery also suggested biological activities influenced fluxes and transformations of this ion within the canopy. The total amount of this canopy N uptake was greater than the mean annual N in litterfall, implying that this flux was supporting the turnover of the canopy. When we used 15N to directly assess uptake into branch biomass we also did not find evidence for immediate relocation to other parts of the tree. Our findings suggest that at our N‐limited site, canopy uptake of N deposition is likely to be as important as root uptake of N but may be limited to supplying N to the canopy rather than the whole tree. Further research in this area is crucial to better understand the interactions of future changes in N deposition on primary production and carbon storage in forests. A free Plain Language Summary can be found within the Supporting Information of this article.

Estimating mangrove forest gross primary production by quantifying environmental stressors in the coastal area

Mangrove ecosystems play an important role in global carbon budget, however, the quantitative relationships between environmental drivers and productivity in these forests remain poorly understood. This study presented a remote sensing (RS)-based productivity model to estimate the light use efficiency (LUE) and gross primary production (GPP) of mangrove forests in China. Firstly, LUE model considered the effects of tidal inundation and therefore involved sea surface temperature (SST) and salinity as environmental scalars. Secondly, the downscaling effect of photosynthetic active radiation (PAR) on the mangrove LUE was quantified according to different PAR values. Thirdly, the maximum LUE varied with temperature and was therefore determined based on the response of daytime net ecosystem exchange and PAR at different temperatures. Lastly, GPP was estimated by combining the LUE model with the fraction of absorbed photosynthetically active radiation from Sentinel-2 images. The results showed that the LUE model developed for mangrove forests has higher overall accuracy (RMSE = 0.0051, R2 = 0.64) than the terrestrial model (RMSE = 0.0220, R2 = 0.24). The main environmental stressor for the photosynthesis of mangrove forests in China was PAR. The estimated GPP was, in general, in agreement with the in-situ measurement from the two carbon flux towers. Compared to the MODIS GPP product, the derived GPP had higher accuracy, with RMSE improving from 39.09 to 19.05 g C/m2/8 days in 2012, and from 33.76 to 19.51 g C/m2/8 days in 2015. The spatiotemporal distributions of the mangrove GPP revealed that GPP was most strongly controlled by environmental conditions, especially temperature and PAR, as well as the distribution of mangroves. These results demonstrate the potential of the RS-based productivity model for scaling up GPP in mangrove forests, a key to explore the carbon cycle of mangrove ecosystems at national and global scales.

Spatiotemporal Evolution of the Carbon Fluxes from Bamboo Forests and their Response to Climate Change Based on a BEPS Model in China

Carbon flux is the main basis for judging the carbon source/sink of forest ecosystems. Bamboo forests have gained much attention because of their high carbon sequestration capacity. In this study, we used a boreal ecosystem productivity simulator (BEPS) model to simulate the gross primary productivity (GPP) and net primary productivity (NPP) of bamboo forests in China during 2001–2018, and then explored the spatiotemporal evolution of the carbon fluxes and their response to climatic factors. The results showed that: (1) The simulated and observed GPP values exhibited a good correlation with the determination coefficient (R2), root mean square error (RMSE), and absolute bias (aBIAS) of 0.58, 1.43 g C m−2 day−1, and 1.21 g C m−2 day−1, respectively. (2) During 2001–2018, GPP and NPP showed fluctuating increasing trends with growth rates of 5.20 g C m−2 yr−1 and 3.88 g C m−2 yr−1, respectively. The spatial distribution characteristics of GPP and NPP were stronger in the south and east than in the north and west. Additionally, the trend slope results showed that GPP and NPP mainly increased, and approximately 30% of the area showed a significant increasing trend. (3) Our study showed that more than half of the area exhibited the fact that the influence of the average annual precipitation had positive effects on GPP and NPP, while the average annual minimum and maximum temperatures had negative effects on GPP and NPP. On a monthly scale, our study also demonstrated that the influence of precipitation on GPP and NPP was higher than that of the influence of temperature on them.

Sensitivity analysis of Biome-BGCMuSo for gross and net primary productivity of typical forests in China

Process-based models are widely used to simulate forest productivity, but complex parameterization and calibration challenge the application and development of these models. Sensitivity analysis of numerous parameters is an essential step in model calibration and carbon flux simulation. However, parameters are not dependent on each other, and the results of sensitivity analysis usually vary due to different forest types and regions. Hence, global and representative sensitivity analysis would provide reliable information for simple calibration. To determine the contributions of input parameters to gross primary productivity (GPP) and net primary productivity (NPP), regression analysis and extended Fourier amplitude sensitivity testing (EFAST) were conducted for Biome-BGCMuSo to calculate the sensitivity index of the parameters at four observation sites under climate gradient from ChinaFLUX. Generally, GPP and NPP were highly sensitive to C:Nleaf (C:N of leaves), Wint (canopy water interception coefficient), k (canopy light extinction coefficient), FLNR (fraction of leaf N in Rubisco), MRpern (coefficient of linear relationship between tissue N and maintenance respiration), VPDf (vapor pressure deficit complete conductance reduction), and SLA1 (canopy average specific leaf area in phenological phase 1) at all observation sites. Various sensitive parameters occurred at four observation sites within different climate zones. GPP and NPP were particularly sensitive to FLNR, SLA1 and Wint, and C:Nleaf in temperate, alpine and subtropical zones, respectively. The results indicated that sensitivity parameters of China's forest ecosystems change with climate gradient. We found that parameter calibration should be performed according to plant functional type (PFT), and more attention needs to be paid to the differences in climate and environment. These findings contribute to determining the target parameters in field experiments and model calibration.

Effect of diffuse radiation on gross primary productivity of typical planted forests in eastern China

Effect of diffuse radiation on gross primary productivity of typical planted forests in eastern China

NILAI USAHA INDUSTRI PRIMER HASIL HUTAN KAYU PADA CV SINAR JAYA DI KECAMATAN CIGUDEG KABUPATEN BOGOR

Community forests are the source of raw materials for the Primary Wood Forest Products Industry at CV Sinar Jaya in Cigudeg District, Bogor Regency. Optimizing the use of these raw materials can affect the business value of the wood processing industry. The purpose of this study was to determine the yield value and to analyze the business value of sawmill activities at CV. Sinar Jaya. The research method was carried out by means of quantitative analysis methods of collecting production data by direct measurement and interviews as well as calculating the value of economic income obtained from sawmill activities and overall waste utilization, the size of wood raw materials includes 10 centimeters to 30 centimeters with a length of 2 ,8 meters to 3 meters. The results showed that the average yield value is 55%, productivity is 1.51 m3/hour round wood, sawn wood is 0.83 m3/hour production cost is Rp 1,181,600,000.0/year, Income value is Rp. 1,509,600,0000.00/year, net profit value is Rp. 328,000.00/year, the value of marketing costs is Rp. 0 the value of the benefit cost ratio is 1.28/year and the work performance value of logs is Rp. 12,712.89 sawn wood Rp. 22,867.14. To increase the yield, the skills of the sawing workforce must be improved to minimize damage toeach sortimen. In order for business value to increase, productivity must be intensified and the availability of raw materials must be sustainable.

Examination of aboveground attributes to predict belowground biomass of young trees

Just as the aboveground tree organs represent the interface between trees and the atmosphere, roots act as the interface between trees and the soil. In this function, roots take-up water and nutrients, facilitate interactions with soil microflora, anchor trees, and also contribute to the gross primary production of forests. However, in comparison to aboveground plant organs, the biomass of roots is much more difficult to study. In this study, we analyzed 19 European datasets on above- and belowground biomass of juvenile trees of 14 species to identify generalizable estimators of root biomass based on tree sapling dimensions (e.g. height, diameter, aboveground biomass). Such estimations are essential growth and sequestration modelling. In addition, the intention was to study the effect of sapling dimension and light availability on biomass allocation to roots. All aboveground variables were significant predictors for root biomass. But, among aboveground predictors of root biomass plant height performed poorest. When comparing conifer and broadleaf species, the latter tended to have a higher root biomass at a given dimension. Also, with increasing size, the share of belowground biomass tended to increase for the sapling dimensions considered. In most species, there was a trend of increasing relative belowground biomass with increasing light availability. Finally, the height to diameter ratio (H/D) was negatively correlated to relative belowground biomass. This indicates that trees with a high H/D are not only more unstable owing to the unfavorable bending stress resistance, but also because they are comparatively less well anchored in the ground. Thus, single tree stability may be improved through increasing light availability to increase the share of belowground biomass.

Exploring Populus phenological response to climate change using observational data and ecosystem modelling

The length of the growing season for deciduous trees in temperate and boreal forests is determined by the timing of bud burst and autumn senescence. It is generally assumed that a warmer climate leads to a longer growing season due to earlier bud burst and delayed autumn senescence and thereby increased gross primary production (GPP) of forests. In this study, we analysed past (1873–1951) and current (2008–2020) phenological observations on bud burst and senescence from aspen trees (Populus tremula) grown in Sweden. The observations indicated a reduction in temperature sensitivity of bud burst between the time periods, likely associated with warmer winters and reduced exposure to chilling. The phenological observations were used in the evaluation of an ecosystem model. Biases in modelling spring and autumn leaf cover development influenced the seasonal and annual GPP estimates. The overestimation of the modelled GPP was more pronounced in spring than in autumn, reflecting the GPP limitations by leaf cover development in spring, and by daylength and temperature conditions in autumn. Calibration of the spring phenology parameters, using the accumulated temperature sums and chilling days at the observed timing of bud burst, significantly improved model performance compared to the original parameterisation. The calibrated ecosystem model projections representing RCP8.5 suggested 15 days earlier timing of bud burst and enhanced mean annual GPP by the end of the century compared with current climate conditions in Sweden.