Use of 3-PG and 3-PGS to simulate forest growth dynamics of Australian tropical rainforests: II. An integrated system for modelling forest growth and scenario assessment within the wet tropics bioregion

Regional model analyses of forest growth are critical for capturing global aspects of tropical rainforest carbon exchange. This research presents the development of a multi-model approach for assessing forest growth and biomass accumulation within the wet tropics bioregion (WTB) based on 10 years of available data and existing model parameter sets. The Tropical Rainforest Growth (TRG) model system employs the 3-PG and 3-PGS models to account for both old-growth rainforest and forest regeneration from seedlings in response to human-induced and natural disturbances. Above-ground biomass (AGB) stocks of the mature forest throughout the WTB for 2000 were estimated to be ∼202 t C/ha. Replacement of areas of old-growth with commercial timber plantations decreased overall AGB stocks to approximately 146 t C/ha. However, plantation carbon accumulation rates were higher than the mature rainforest, representing their potential to accumulate more biomass over a longer analysis time period. As tropical cyclones may significantly alter the carbon stocks of old-growth rainforests, the effect of tropical cyclone Rona on the WTB was assessed. The cyclone had a minimal impact on total AGB stocks within the region, yet these systems are an important factor to be considered in carbon and forest regeneration modelling activities in the tropics. The TRG system is an advanced modelling tool providing a rapid process-based assessment of biomass stocks and accumulation dynamics within Australia's tropical rainforest bioregion and has the potential for application in tropical forest ecosystems at both national and international levels.     

A pragmatic approach to modelling soil and water conservation measures with a catchment scale erosion model

To reduce soil erosion, soil and water conservation (SWC) methods are often used. However, no method exists to model beforehand how implementing such measures will affect erosion at catchment scale. A method was developed to simulate the effects of SWC measures with catchment scale erosion models. The method was implemented by applying the LISEM model to an agricultural catchment on the slopes of Mt. Kenya. The method consisted of a field scale calibration based on P-factors, followed by application at catchment scale. This calibration included factors such as saturated conductivity, Manning's n, roughness and slope angle. It was found that using data on P-factors, such models can be calibrated to give acceptable predictions at pixel scale. However, P-factors were also found to vary with land use type and storm size. Besides, more data on the physical effectiveness of SWC measures are needed. At catchment scale, the effect of SWC was found to be different from that at pixel scale. Most SWC were simulated to be more effective at catchment scale, indicating additional infiltration during transport through the catchment to the outlet. However, slope corrections in case of terraces were found to be less effective at this scale. Nevertheless, a simulation for current land use with current SWC measures indicated that these SWC measures decrease runoff by 28% and erosion by 60%.     

Predicting ash dieback severity and environmental suitability for the disease in forest stands

Ash dieback, caused by the ascomycete fungus Hymenoscyphus fraxineus, has been rapidly expanding across Europe during the last two decades, posing a considerable threat to native ash populations. In this study, we applied regression-based models trained by field data, in conjunction with geographic information systems, to produce spatial predictions of ash dieback severity and environmental suitability for the disease in Czech forests. A model of actual ash dieback severity relates disease extent to silvicultural and environmental characteristics of forest stands and their neighbourhood, while a model of environmental suitability for the disease quantifies the relative susceptibility of sites to the disease, independent of the current silvicultural characteristics. The final predictive maps suggested that fertile lowlands and humid areas bordering Poland and Slovakia were the most endangered regions. Areas at the lowest risk of damage were concentrated in dry areas and in highland and mountain areas in the western part of the country, usually with poor soils on acid bedrock. Predictions of actual disease severity are an effective tool for guiding the current management of infested stands whereas predicting environmental suitability is useful for making long-term strategic decisions, e.g. identifying areas where future ash regeneration and cultivation may be unsuccessful.     

GrazeIn: a model of herbage intake and milk production for grazing dairy cows. 2. Prediction of intake under rotational and continuously stocked grazing management

Decision support tools to help dairy farmers gain confidence in grazing management need to be able to predict performance of grazing animals with easy‐to‐obtain variables on farm. This paper, the second of a series of three, describes the GrazeIn model predicting herbage intake for grazing dairy cows. The model of voluntary intake described in the first paper is adapted to grazing situations taking account of sward characteristics and grazing management, which can potentially affect intake compared to indoor feeding. Rotational and continuously stocked grazing systems are considered separately. Specific effects of grazing management on intake were quantified from an extensive literature review, including the effect of daily herbage allowance and pre‐grazing herbage mass in rotational grazing systems, sward surface height in continuously stocked grazing systems, and daily time at pasture in both grazing systems. The model, based on iterative procedures, estimates many interactions between cows, supplements, sward characteristics and grazing management. The sensitivity of the prediction of herbage intake to sward and management characteristics, as well as the robustness of the simulations and an external validation of the GrazeIn model with an independent data set, is presented in a third paper.     

《现代科学与技术概论》教学模式的改革探讨

通过问卷调查的方式对《现代科学与技术概论》课程的多人分阶段专题式教学模式与传统式教学模式进行了同步调查与比较分析。调查结果表明,专题式教学模式在学生的学习兴趣、学习方法及对教师教学态度的认可度、教学效果的综合评价方面都优于传统式教学模式。其主要优势表现在可以充分发挥教师的教学优势、及时更新前沿知识、开展精髓式教学和辅导;也有利于调动学生的主观能动性和学习兴趣,但专题式教学模式下各教学内容之间的衔接与连贯有待进一步探讨。     

Modelling within‐field spatial variability of crop biomass – weed density relationships using geographically weighted regression

The objective of this study is to offer a new framework for exploring and modelling the spatial variation in crop biomass – weed density relationships, adapting geographically weighted regression (GWR) to include a non‐linear regression model. The relationship between crop biomass and weed density is usually modelled by non‐linear regression models, in which the spatial heterogeneity of the relationship is ignored, although the effect of weeds on crop can differ in relation to topographic and edaphic variability. GWR attempts to capture spatial variability by calibrating a regression model to each location in space. We show the application of the method in different cereal cropping systems, with one or two weed species. The results indicate that GWR can significantly improve model fitting over non‐linear least squares (NLS) in some situations. Furthermore, the parameter estimates can be mapped to illustrate local spatial variations in the regression relationship under study and eventually to relate the spatial variability of the model to the environmental heterogeneity. We discuss the value of the GWR for analysing the observed spatial variability and for improving model development and our understanding of spatial processes.     

Volume estimation of Cryptomeria japonica logs in southern Brazil using artificial intelligence models

This study aimed to test taper functions and artificial intelligence (AI) models in order to estimate merchantable volumes of Japanese cedar (Cryptomeria japonica) trees in a homogenous plantation in southern Brazil. A total of 30 individuals were rigorously scaled and their total volumes were calculated, including those of the following log assortments: veneer, sawn, pulp and energy. Three AI models, i.e. two variants of k-nearest neighbours (KNN) instance-based classification (one and three nearest neighbours) and an artificial neural network (ANN) approach, were compared with three traditional taper models: fifth-order polynomial, fractional powers and the Garay model. The estimated volumes were compared with the actual volumes by means of the standard error (S_yx), bias, precision and accuracy. Total volume estimates proved to be unbiased (maximum bias 5.42%), precise (maximum precision 9.28%) and accurate (maximum accuracy 10.79%) with all of the investigated models. The tested models tended to give lower bias, better precision and accuracy in the middle portion of the stems, but worse estimates at the base and tip (maximum bias ?12.41%). In general, the KNN models improved merchantable volume estimation, particularly KNN1, which is a straightforward and simple method. We conclude that AI techniques have appeal for application in forest inventories and that KNN is a particularly interesting alternative for tree volume estimation.     

土壤样品中重金属化学形态模型的发展与应用

土壤中重金属的化学形态与其生物有效性和环境迁移性密切相关。相比于实验室分析手段,化学模型计算具有能揭示化学形态分布全景指纹和具有预测能力的优势。文章综述了天然环境样品中元素化学形态模型的发展、经验性和机理性模型的特征、离子在天然有机质(以NICA-Donnan模型为例)和金属氧化物(以CD-MUSIC模型为例)吸附的先进表面络合模型、建立环境样品机理性模型面临的挑战。重点就天然有机质、金属氧化物和"活性"重金属总量等模型输入值的确定方法作了介绍,并结合镉和砷分别作为金属阳离子和含氧阴离子的典型代表,具体阐述了模型在土壤中重金属元素化学形态分布上的应用。总结了机理性化学形态模型在天然环境样品应用的发展现状,并对模型应用面临的问题和后期的发展做出了展望。     

Identification of Visceral Leishmaniasis‐Susceptible Areas using Spatial Modelling in Southern Caucasus

This study explores the application of spatial modelling techniques to generate susceptibility maps for a neglected zoonotic disease, visceral leishmaniasis (VL), in an endemic area in southern Caucasus that includes Iran, Armenia and Azerbaijan. The social and physical environment of southern Caucasus has been mainly characterized by the presence of several factors that are strongly associated with VL, which has caused a significant number of infections during the past decade. Three popular spatial modelling techniques, consisting of the weights of evidence, logistic regression and fuzzy logic methods, were evaluated and trained using a study area in north‐western Iran where an inventory of highly infected areas and high‐quality evidential factors was available. Model performance was assessed using the receiver‐operating characteristic (ROC) approach. According to the results of these assessments, the fuzzy logic method with γ = 0.5 was chosen for the prediction of VL incidence in southern Caucasus. The susceptibility map generated using the fuzzy logic method indicated that VL followed a spatial pattern at the conjunction of the three countries, which suggests that the prevalence of VL in southern Caucasus is socio‐ecologically dependent.