Fine Tree Research Articles

Pulse injection-based sensorless switched reluctance motor driver model with machine learning algorithms

In this study relationships of pulse injected idle phase currents are used to predict rotor position with tuned fine tree and ensemble bagged tree algorithm in MATLAB. Different classifier algorithms trained, tested, and the best accurate results are obtained via ensemble bagged tree classifier using idle phase currents. Three-phase 6/4 switched reluctance motor (SRM) with optical position sensors diagnosis pulses has been injected into idle phases and operated at constant load and speed. The measured idle phase currents were rearranged using the time series method and trained with supervised machine learning algorithms. These unprocessed idle phase currents reduce processing time and contribute to the real-time operation of the system. This study proves that SRM can be driven by predicting the active phase to be triggered by trained ensemble bagged tree and tuned fine tree machine learning algorithms from real-time measured idle phase current data.

Sample size estimation for achieving the desired uncertainty for estimates of tree fine root trait parameters

This study investigates efficient strategies for fine tree root sampling, in terms of estimating root trait parameters with desired confidence intervals for least effort and cost. Sampling tree roots is difficult and costly with high variation among samples and wide confidence intervals for parameter estimates. Efficient strategies for fine tree root sampling will estimate root trait parameters with the desired confidence interval for least effort and cost. We compared alternative strategies to sample and estimate fine root surface area density; (1) collecting samples at intervals of 10 cm to a depth of 150 cm for entire tree root systems versus (2) independently taking samples from different randomly-selected 10-cm depth intervals around different trees. We also quantified the pilot sample size needed to reliably estimate the number of samples that would achieve the desired confidence interval. Efficiency of sampling entire tree root systems versus independent samples depended on the structure of the sample data. Pilot sample sizes > 5 per 10-cm soil depth can give reliable estimates of sample sizes required to achieve a 95% confidence interval of ± 10% of the sample mean. The statistical strategies in this paper are not particularly novel or difficult, but are seldom applied to root studies. We contend that they should be used, both to guide efficiency in sampling design and also to assess how realistic it is to expect that estimated sample means will be reliable, in the sense of having confidence intervals of the required width.

Using near-infrared hyperspectral imaging with multiple decision tree methods to delineate black tea quality.

The evaluation of tea quality tended to be subjective and empirical by human panel tests currently. A convenient analytical approach without human involvement was developed for the quality assessment of tea with great significance. In this study, near-infrared hyperspectral imaging (HSI) combined with multiple decision tree methods was utilized as an objective analysis tool for delineating black tea quality and rank. Data fusion that integrated texture features based on gray-level co-occurrence matrix (GLCM) and short-wave near-infrared spectral features were as the target characteristic information for modeling. Three different types of supervised decision tree algorithms (fine tree, medium tree, and coarse tree) were proposed for the comparison of the modeling effect. The results indicated that the performance of models was enhanced by the multiple perception feature fusion. The fine tree model based on data fusion obtained the best predictive performance, and the correct classification rate (CCR) of evaluating black tea quality was 93.13% in the prediction process. This work demonstrated that HSI coupled with intelligence algorithms as a rapid and effective strategy could be successfully applied to accurately identify the rank quality of black tea.

An integrated approach of machine algorithms with multi-objective optimization in performance analysis of event detection

Challenges in the provision of a safe water distribution system have become one of the major concerns to the society. Various models and algorithms have been developed so far to incorporate in the early warning systems. This study focuses on the use of machine learning (ML) algorithms on different contaminated datasets. Fine tree (FT) and linear support vector machine (LSVM) were chosen to classify the events. To select the best combination of event and nonevent data, nondominated sorting genetic algorithm-II is integrated with the algorithms that helps to obtain an optimal solution of minimized false positive rate (FPR) and minimized false negative rate (FNR). Results suggest that both FT and LSVM minimized FPR and FNR very effectively. However, FT performed better than LSVM in a supervised and laboratory-controlled dataset, and it showed its superiority in securing robustness over LSVM and fuzziness-based methods in different uncertain scenarios of the study datasets. Moreover, the study initiated a novel approach by executing FT and LSVM models to classify contamination events in a combination of two datasets of various contaminants. It produced better results compared to the Pearson correlation–Euclidean distance (PE) method applied in the same dataset. In addition, the ML algorithms showed their consistency in detecting most of the simulated events using different ranges of spikes.

Numerical study on flow field and pollutant dispersion in an ideal street canyon within a real tree model at different wind velocities

Studying the effects of trees on airflow and pollutant dispersion in urban street canyons is of considerable significance to clarify the laws of urban micro-scale airflow and pollutant dispersion. To characterize the trees in street canyons, different from the traditional vegetation resistance source method, in this paper, a fine tree model is used for numerical simulations of the airflow and pollutant dispersion in street canyons at varying inflow wind velocities. It is found during the study that the presence of trees physically blocks the airflow in street canyons. The airflow is sheared by the trunk, canopy, or branches, and then circumvents them, especially at a high inflow wind velocity. The low-velocity area in the street canyon distributes to the leeward side of the tree trunk as well as in the canopy area, and a discrete low-velocity distribution exists mainly in the canopy area. The average wind velocity in a street canyon with trees is approximately 39.5% lower at an inflow wind velocity of 1.7 m/s than that in a canyon without trees. In the presence of trees, the pollutant concentration in street canyons increases significantly, and the pollutants significantly accumulate between the tree trunk and the leeward side. With increasing inflow velocity, the pollutant concentration in a street canyon constantly changes but is much higher than that in the absence of trees. At a wind velocity of 5.7 m/s, the average pollutant concentration is 18.6% higher in street canyons with trees than in those without trees.

IOT based Online Load Forecasting using Machine Learning Algorithms

Abstract This paper presents a novel online load forecasting using supervised Machine Learning (ML) algorithms in Internet of Things (IOT) environment. Short Term Load Forecasting (STLF) is an essential aspect for smart grid operations such as power dispatch and load management. IOT is an emerging Technology breaching into every segment of science and engineering. This work presents the possibility of STLF online with accurate prediction models by using ML algorithms. Electrical load consumption data and weather data at a research Lab, JNTUH, Hyderabad is used to train ML algorithms in order to implement STLF. ML algorithms based forecasting models are developed using MATLAB code through cloud computing. Online forecasting is more sophisticated and effective because of its ability to use recent data logs for training and forecasting online. Online forecasting is useful in Online Home Energy Management Systems (OHEMS) for effective energy management. ML algorithms such as Linear Regression (LR), Support Vector Machines (SVM) for regression, Ensemble Bagged (EB) regression, Ensemble Boosted (EBo) regression, Gaussian Process Regression (GPR) and Fine Tree (FT) regression are implemented on the cloud to forecast the power consumption. Performance parameters such as RMSE, MSE and MAE are derived to evaluate the effectiveness of the ML algorithms implemented. Cost effective Arduino Uno, Node MCU/ESP8266, PZEM 004T and DHT 11 sensors are used to fabricate the hardware model in order to acquire the load data for the proposed load forecasting approach. Best suited ML algorithm is suggested for the proposed online forecasting with supporting results.

Multiple Sensing Resources in Cognitive Radio Systems

This paper aims to improve the detection accuracy of the energy detection-based cognitive radio system using machine learning while varying the number of cognitive radios used for sensing. It has been an exploratory study in order to identify a suitable number of machine learning-enabled cognitive radios that could be used to improve the performance of the system in terms of sensitivity. Several machine learning techniques have been tested at a low SNR (-25dB) in order to simulate conditions where energy detectors perform poorly. The number of cognitive radios has been varied from three to eighteen in order to explore the potential of increasing the data available for machine learning. Results have revealed that with three cognitive radios, the overall detection accuracy of 97% could be achieved in the energy-detection based system despite the low SNR condition. It has also been discovered that increasing the number of cognitive radios has improved the True positive rate for primary user detection from 98% to >99% with Fine Tree algorithm but at the expense of the overall accuracy of the system. Therefore, demands of specific applications are required in order to identify the number of cognitive radios to employ. Overall, the research presents the potential of using a minimal resource-demanding cognitive radio sensing method (energy detection) for sensing without compromising the detection accuracy of the system. It also improves the spectrum utilization of the system by reducing the probability of false alarms considerably. It highlights improvements that can be made to the conventional energy-detection system through machine learning while retaining the simplicity of the cognitive radio system

ExxonMobil to invest in Singapore site to produce lube basestocks and distillates

Reducing CO2 emissions from fossil fuel fired power plants has been a major environmental concern over the last decade. Amongst various carbon capture and storage (CCS) technologies, the utilization of solvent-based post-combustion capture (PCC), played a major role in the reduction of CO2 emissions. This paper illustrates the development of machine learning models to predict the outputs of the PCC unit. A fine tree, Matérn Gaussian process regression (GPR), rational quadratic GPR, and squared exponential GPR models were developed and compared with a feed-forward artificial neural network (ANN) model. An accuracy of up to 98% in predicting the process outputs was achieved. Furthermore, the models were utilized to determine the optimum operating conditions for the process using a sequential quadratic programming algorithm (SQP) and genetic algorithm (GA). The use of the machine learning models has proven to be very useful since the complete mechanistic model is too large, and its runtime is too long to allow for rigorous optimal solutions. The machine learning models and optimization problems were developed and solved using MATLAB. The data used in this work was obtained from simulating the process using gPROMS process builder. The inputs of the model were selected to be reboiler duty, condenser duty, reboiler pressure, flow rate, temperature, and the pressure of the flue gas. The models were able to accurately predict the outputs of the process which are the system energy requirements (SER), capture rate (CR), and the purity of condenser outlet stream (PU).

Competition increased fine root biomass in Chinese fir (Cunninghamia lanceolata) plantations in Subtropical China

Fine roots play a prominent role in plants’ acquisition of nutrients and water from the soil, and their amounts and dynamics determine trees’ growth and adaptation to environmental stress. However, the relationship between fine tree roots (including absorptive roots and fine transport roots) and forest biomass or fine understory vegetation roots (understory roots) remains poorly understood, which was the main objective of this study. We selected 20 plots in Chinese fir (Cunninghamia lanceolata) plantation forests of the similar ages (23 years old on average) in different areas with various levels of forest biomass in subtropical China to assess the above-mentioned relationship. Forest biomass was used as a proxy for soil condition.We found that all fine root biomass of trees and understory vegetation were significantly and negatively correlated with forest biomass. To our surprise, both total Chinese fir fine root biomass (absorptive roots and fine transport roots) and fine transport root biomass showed significant and positive correlations with understory root biomass. These results clearly demonstrate that fine tree root biomass is significantly promoted and increased due to competition from understory vegetation where forest biomass is low.

Role of Fine Tree Roots in the Nitrogen Cycle of Boreal Forests

Despite the insignificant contribution to the total forest phytomass, fine roots form 30% of the production of forest ecosystems, playing a crucial role in biogeochemical cycles. However, estimates of the biomass, annual production, dieback, and age of fine roots remain understudied, especially in Russian forests, due to technical difficulties. There are only a few estimates of nitrogen consumption for the annual production of fine roots. It is believed that they comprise up to 75 kg N ha–1 in mature forests. This is up to 60% of the total consumption of nitrogen for annual yield and is comparable with mineralized soil nitrogen. However, these values are approximate because the methods of measuring fine roots production are debatable.

Electrical tree growth and partial discharge in epoxy resin under combined AC and DC voltage waveforms

The effect of DC bias on electrical tree growth and partial discharge (PD) characteristics in epoxy resin (Araldite LY® 5052/Aradur® HY 5052 supplied by Huntsman) is investigated using three waveforms: AC, AC with positive DC bias, and AC with negative DC bias. Needle-plane samples are used. Tree growth is shown to be accelerated by the combined effect of DC and 50 Hz AC, beyond the AC growth rate. Positive and negative DC biased tests result in 62% and 54% reductions in average time to breakdown, respectively. Different tree structures and stages of development are associated with different partial discharge characteristics, with thick dark tree branches associated with high PD magnitudes, whereas fine tree channel growth is linked with PD magnitudes below 1 pC. AC tests showed five distinct stages of tree growth compared to four stages seen in DC biased tests. In particular, trees growing in the ‘reverse direction’ from the planar to the point electrode, which is observed in the latter stages of AC tests, is not seen in the DC biased tests. It is concluded that for composite voltages, the AC component is the essential driver of tree growth but the DC component can accelerate propagation. AC noise may therefore compromise the reliability of insulation in HVDC networks.

Research on geometric features and point cloud properties for tree skeleton extraction

To solve skeleton extraction problems in the tree point cloud model, branch geometric features and local properties of point cloud are utilized to optimize tree skeleton extraction. First of all, according to the attribute information estimation and normal vector adjustment of point cloud neighbor domain, branch segmentation is made by estimated values and geometric features. Skeleton nodes are extracted in the branch subset in segmentations. Then, a graph is constructed based on skeleton node set and tree skeleton is reconstructed in this weighted directed graph. Finally, according to the tree growth characteristics, cubic Hermite curves are utilized to optimize the skeleton curve. This method is applied in the point cloud model of three-kind trees and it is compared with the skeleton extraction method based on voxel switch and point cloud contraction. The experiment results show that this method displays strong anti-interference and high-precision characteristics at branch bifurcation and crossed ending parts of fine tree branches. Thus, features of tree branches can be described more perfectly, obtaining the skeleton curve closer to the main axis.

Contribution of fine tree roots to the silicon cycle in a temperate forest ecosystem developed on three soil types

Abstract. The role of forest vegetation in the silicon (Si) cycle has been widely examined. However, to date, little is known about the specific role of fine roots. The main objective of our study was to assess the influence of fine roots on the Si cycle in a temperate forest in north-eastern France. Silicon pools and fluxes in vegetal solid and solution phases were quantified within each ecosystem compartment, i.e. in the atmosphere, above-ground and below-ground tree tissues, forest floor and different soil layers, on three plots, each with different soil types, i.e. Dystric Cambisol (DC), Eutric Cambisol (EC) and Rendzic Leptosol (RL). In this study, we took advantage of a natural soil gradient, from shallow calcic soil to deep moderately acidic soil, with similar climates, atmospheric depositions, species compositions and management. Soil solutions were measured monthly for 4 years to study the seasonal dynamics of Si fluxes. A budget of dissolved Si (DSi) was also determined for the forest floor and soil layers. Our study highlighted the major role of fine roots in the Si cycle in forest ecosystems for all soil types. Due to the abundance of fine roots mainly in the superficial soil layers, their high Si concentration (equivalent to that of leaves and 2 orders higher than that of coarse roots) and their rapid turnover rate (approximately 1 year), the mean annual Si fluxes in fine roots in the three plots were 68 and 110 kgha-1yr-1 for the RL and the DC, respectively. The turnover rates of fine roots and leaves were approximately 71 and 28 % of the total Si taken up by trees each year, demonstrating the importance of biological recycling in the Si cycle in forests. Less than 1 % of the Si taken up by trees each year accumulated in the perennial tissues. This study also demonstrated the influence of soil type on the concentration of Si in the annual tissues and therefore on the Si fluxes in forests. The concentrations of Si in leaves and fine roots were approximately 1.5–2.0 times higher in the Si-rich DC compared to the Si-poor RL. In terms of the DSi budget, DSi production was large in the three plots in the forest floor (9.9 to 12.7 kgha-1yr-1), as well as in the superficial soil layer (5.3 to 14.5 kgha-1yr-1), and decreased with soil depth. An immobilization of DSi was even observed at 90 cm depth in plot DC (−1.7 kgha-1yr-1). The amount of Si leached from the soil profile was relatively low compared to the annual uptake by trees (13 % in plot DC to 29 % in plot RL). The monthly measurements demonstrated that the seasonal dynamics of the DSi budget were mainly linked to biological activity. Notably, the peak of dissolved Si production in the superficial soil layer occurred during winter and probably resulted from fine-root decomposition. Our study reveals that biological processes, particularly those involving fine roots, play a predominant role in the Si cycle in temperate forest ecosystems, while the geochemical processes appear to be limited.

Electrical treeing and reverse tree growth in an epoxy resin

An electrical treeing process is presented which consists of two types of electrical tree structures that coexist in the same epoxy resin: these have been termed as ‘filamentary trees’ and ‘reverse trees’. In samples with needle-plane electrode geometries, a filamentary tree that consists of fine tree channels propagates from the needle electrode to the plane ground electrode under an applied AC voltage. It is observed that once the filamentary tree has crossed the insulation, trees then grow from the planar electrode towards the needle electrode as reverse trees, eventually leading to dielectric breakdown. The characteristics of the treeing processes have been obtained for a range of samples through optical observations. In addition, partial discharge (PD) activity associated with the growth of a reverse tree is thoroughly characterized. The prior existence of a filamentary tree is a prerequisite for the development of a reverse tree. PD does not appear to be a driving force for the growth of the filamentary trees, whereas high levels of PD are associated with the growth of a reverse tree. This distinction shows that aging can occur undetected by PD, but asset management of the more aggressive treeing stages can use PD analytics.

Below‐ and above‐ground effects of deadwood and termites in plantation forests

AbstractDeadwood is an important legacy structure in managed forests, providing continuity in shelter and resource availability for many organisms and acting as a vehicle by which nutrients can be passed from one stand to the next following a harvest. Despite existing at the interface between below‐ and above‐ground systems, however, much remains unknown about the role woody debris plays in linking these zones. Moreover, it remains untested whether the accelerative effects of wood‐feeding insects on wood decomposition influence tree growth or nutritional status in forests. In this study, we added different quantities of pine logs to the bases of saplings in two‐year‐old loblolly pine (Pinus taedaL.) plantations in Mississippi,USA. We included a treatment in which subterranean termites (Blattodea: Rhinotermitidae:Reticulitermes) were excluded from logs to determine how these insects affect the release of nutrients from wood and, in turn, tree growth. After 51 months of decomposition, we quantified below‐ground effects by measuring microbial biomass, plant‐available forms of N, and ectomycorrhizal diversity associated with fine tree roots. Meanwhile, above‐ground measurements focused on the elemental concentrations in decomposing wood either protected or unprotected from termites and tree metrics related to growth and nutrient status. We found additions of wood to significantly increase nitrate and potential net nitrification relative to reference treatments but detected no significant effects on tree growth, needle nitrogen concentrations, or ectomycorrhizal diversity. Soil nitrate and potential net nitrification were higher under protected vs. unprotected logs, and plant‐available forms of N were mostly more abundant short distances away from both protected and unprotected logs than directly under them. The wood of logs protected from termites had significantly lower concentrations of most elements compared to that of unprotected logs, largely due to the large amounts of soil imported into unprotected logs by termites. Termite exclusion had no measurable effect on tree growth, nutritional status, or ectomycorrhizal diversity, however. Our findings indicate that deadwood and termites both contribute to the spatial heterogeneity of soil properties but may have limited short‐term local effects on tree growth. Longer‐term studies and studies on less fertile sites are needed.

Optimizing resource distribution and crop productivity in hedgerow intercropping by manipulating tree arrangement

The potential of tree arrangement in optimizing radiation and soil water distribution and crop yield of hedgerow intercropping systems was investigated using a Jatropha curcas–Pennisetum clandestinum (kikuyu) system (Ukulinga, South Africa). Treatments (1110 t ha−1) of Jatropha-only (JO), single-row Jatropha and kikuyu (SR), and double-row Jatropha and kikuyu (DR) were used. Treatments had asymmetrical radiation distribution across tree–crop (T–C) interfaces and different radiation interception by trees (JO: 27 %, SR: 11 %, DR: 8 %). Soil water varied among treatments and was asymmetrically distributed showing no consistent trend towards trees. Evapotranspiration was higher (p < 0.05) in SR than DR (13–65 %) and JO (16–37 %) for most part of the season. Estimated fine tree roots distribution was symmetrical in DR but not JO and SR. Total roots in SR were concentrated in the top 0.2 m (91 %) and beneath tree row (30 %), and varied inconsistently with distance from trees. Grass yields increased farther from tree rows and were higher in SR (8.7 t ha−1 in 2006–2007; 4.3 t ha−1 in 2007–2008) than DR (by <9 %). Radiation use efficiency of kikuyu (0.14–0.44 g MJ−1) also increased with distance from trees but irradiance correlated with grass yields poorly (R2 < 0.32) despite high rainfall and no nutrient limitation. Considering total productivity and evapotranspiration, DR was better tree arrangement than SR. Water availability dictated T–C interactions and intercrop yield more than radiation. It was possible to optimize radiation and water distribution and intercrop growth by manipulating tree arrangement without changing density.

Quantitative Analysis for Termites Damage of Wooden Heritage using Ultrasonic Pulse Velocity

Quantitative analysis of termites damage is important in terms of conservation and maintenance of wooden cultural heritage buildings, because termites makes cavities and decreases the section area of wooden structural members. The purpose of this study is to forecast the range and spread of termites damage in the wooden structural members by using ultrasonic pulse velocity method. Ultrasonic pulse velocity has been used as one of non-destructive test to analysis the internal defect by using difference velocity between medium material and cavity. This method would be effective to analysis termites damages. From the result of the ultrasonic velocity test, the loss rate of area effected by termites damage had a strong correlation with ultrasonic velocity. And it is possible to predict the loss rate of area from by termites damage by using regression equation in the case of structural member of fine tree.

广西猫儿山自然保护区生态补偿标准与补偿方式探析

利用问卷调查和条件估值法，以猫儿山自然保护区为例，探讨自然保护区生态补偿机制的构建。结果表明：（1）当地居民对保护区和退耕还林政策不熟悉，如果给相应的补偿，超过一半的居民愿意为保护生态环境而放弃在保护区内从事垦荒、砍伐、放牧等活动。建立保护区后，绝大多数村民在生产生活和家庭收入等方面受到影响。（2）不同村庄农户的家庭受损和机会成本相差较大。对于林地没有被保护区划占的农户，机会成本为10000元/户。对于林地被保护区划占的农户，机会成本为10000元/户+750元/666.7m²×被划占的林地亩数。（3）采用投标卡方法，确定农户受偿意愿为230.66元 666.7m^-2 a^-1。（4）通过卡方检验，受教育程度、民族和居民所在村别均不同程度地影响受偿意愿。文化层次与受偿意愿的关系呈单调变化，瑶族的受偿意愿最高，其次是苗族，汉族的受偿意愿最低，受保护区影响越大的村庄，其受偿意愿也越大。（5）当地居民的受偿方式呈多样化。多数居民希望得到现金补偿，选择多种受偿方式的组合较为普遍。补偿金主要用于基本生活和生产的需要。农户的技术补偿需求主要是优良林木种植技术和果木蔬菜种植技术，最关心的政策补偿是提供就业。;Using the questionnaire and contingent valuation methods, the article analyzed the perceptions of farmers who lived around Guangxi Maoershan Natural Reserve on sloping land conversion program and ecological welfare forest construction, opportunity costs of local residents, their willingness to accept and the compensation modes of accept. The results were showed as follows: (1) local residents were not familiar with the reserve's fundamental purposes and thought that the main purpose was to protect the ecological environment and develop ecological tourism. The majority of the respondents were not familiar with the policy of returning farmland to forest. After constructing the natural reserve, most of the villagers were affected in the family income and life. (2) There was a difference in the losses and opportunity costs of farmers in different villages. The loss mainly included two parts, one was that the earnings of the villages' forest that have been occupied in the reserve were lost, but they did not receive the compensation; the other loss was caused by banning bamboo cutting, digging bamboo shoots, hunting and grazing in the reserve. The farmers' opportunity costs could be divided into two categories: for the farmers whose forest land was not occupied in the protected area, the opportunity cost was 10000 Yuan per year per household, while the opportunity cost was equal to 10000 Yuan per year per household plus 750 Yuan per year per acre multiply by the forest area occupied in the protected zone for the farmers which forest land was occupied in the protected area. (3) Using the bidding cards, the range of the willingness to accept was 211.88-249.43 Yuan per year per acre and the midpoint was 230.66. The farmers' attitudes to the willingness to accept were different. If the forest is occupied in the protection area, they will demand compensation, if they are not affected by the natural reserve, they will not ask for compensation. (4) The chi-square test revealed that except that the influence of gender and age on the average willingness to accept was not significantly, the level of education, nation and the village affected willingness to accept in different degrees. The willingness to accept monotonously changed with educational attainment, the higher the education level of peasants are, the higher their willingness to accept are. The influence of nation on the average willingness to accept was different, the willingness to accept of Yao nationality was the highest, followed by Miao nationality, Han nationality's willingness to accept was the lowest. The stronger the effect is, the bigger the average willingness to accept of the villages is. (5) Local residents' willingness to accept were diverse. Most of farmers wanted to get cash payments, followed by substance compensation. The combinations of the modes of the willingness to accept were common. The compensation funds were mainly used for basic needs of life and agricultural production. The demands for technology of farmers were mainly the technology of fine tree planting and fruit and vegetable planting. The policy that farmers main concern were employment provision.

Importancia de la caracterización de la biomasa de raíces en la simulación de ecosistemas forestales

Gárate, M., Blanco, J.A. 2013. Importance of root biomass characterization when simulating forest ecosystems. Ecosistemas 22(3):66-73. Doi.: 10.7818/ECOS.2013.22-3.10. Sensitivity analysis are an important tool to understand ecological models functioning, as well as to identify their most important parameters. In addition, sensitivity analysis can be used to design more effective field sampling regimes to calibrate ecological models. When studying forest ecosystems, quantitative analysis of the underground fraction is much more costly and complicated than studying the aboveground fraction, especially when studying the dynamics of production and decomposition of large and fine tree roots. In this work we show an example of sensitivity analysis of the forest ecosystem model FORECAST to parameters defining biomass, longevity and nitrogen concentration in tree roots. The model was calibrated to simulate two Scots pine (Pinus sylvestris L.) stands in the Pyrenees of Navarre. Results indicate that fine root turnover is the most influential parameter on model´s tree growth estimations, followed by fine root N concentration and the root biomass/total tree biomass ratio. Furthermore, the model is more sensitive to root-related parameters when calibrated to simulate poor sites with low productivity capacity and higher nutrient limitation.

Why fine tree roots are stronger than thicker roots: The role of cellulose and lignin in relation to slope stability

Plant roots help to reinforce the soil, increase slope stability and decrease water erosion. Root tensile strength plays an important role in soil reinforcement and slope stabilization. The relationship between tensile strength and internal chemical composition of roots is unknown due to limited studies. Thus, it is difficult to determine why root tensile strength tends to decrease with increasing root diameter. In this study, biomechanical and biochemical tests were performed on the roots of Chinese pine (Pinus tabulaeformis) to determine the relationships among tensile strength and the contents of the main chemical composition: cellulose, alpha-cellulose and lignin in the roots with different diameters. Our results confirmed that the tensile strength of Chinese pine roots decreased with increasing root diameter, and this relationship might be a power function. The chemical contents of the roots and root diameter were also related to each other with significant power regression. With increasing root diameter, the cellulose content and alpha-cellulose content increased, but the lignin content decreased. In addition, the lignin content exhibited a significantly positive relationship with tensile strength. Furthermore, the ratios of lignin/cellulose and lignin/alpha-cellulose decreased with increasing root diameter following significant power regressions, and they also demonstrated a positive relationship with tensile strength. Taken together, these results may be useful for studies on root tensile strength, soil reinforcement and slope stability.