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Aim: Lianas are an important component of forest structure in the tropics, accounting for up to 45% of total stems. Mounting evidence that tropical forests are undergoing structural changes, with a growing abundance of lianas reducing forest carbon storage potential, imparts a sense of urgency to study the drivers that control liana abundance and biomass, particularly in Africa where data come from a few, small-scale studies. Location: Gabon, Africa. Methods: In the first countrywide study of lianas, we implemented the most ambitious, large-scale forest inventory in tropical Africa to date, quantifying the density, basal area and biomass of large lianas (≥10 cm in diameter) using a systematic, random design of 104 plots located across Gabon. Additionally, we examined the relative importance of environmental variables (mean annual precipitation, mean annual temperature, seasonality, soil nitrogen, soil fertility), disturbance (effect of gaps, forest type) and forest structure (large tree biomass) in driving macroscale variation in the abundance of large lianas. Results: In total, we surveyed 1354 large lianas, and found the density, basal area and biomass of large lianas in Gabon to be comparable to that in other tropical forests. The success of large lianas was positively related to soil N, but most strongly correlated with forest structure, particularly large tree biomass. The strength of the association between large lianas and large trees increased with tree size class. Main conclusions: Forest structure and the availability of large trees may be more important predictors of the abundance and distribution of large lianas in African tropical forests than environmental variables and disturbance. Changing environmental conditions are likely to have little direct effect on large lianas, but climate change, defaunation and land-use activities that diminish forest structure and reduce the number of large trees could have strong indirect effects on large lianas in Central African forests.

Invasive species are a major cause of environmental change and are often costly to control. Decision theory should offer managers guidance to formulate the optimal allocation of resources. Unfortunately, current decision theory models typically do not consider invasion dynamics and do not make full use of the best models of biological spread and best biological data from theoretical models. We developed a decision theory model that integrated population dynamics, spread, uncertainty, and changes in management policies. We applied this model to leafy spurge (Euphorbia esula), a high-priority invasive weed in North America. We used field data to construct a biological model that included stochastic population dynamics and spatial spread and integrated it with decision theory using stochastic dynamic programming (SDP). The SDP model considered three control strategies: no control, biological control, and herbicide control. Solutions from the SDP model determined the optimal strategy to apply at a given state for any time horizon. The optimal strategy depended on the area and density of leafy spurge and varied with the time horizon; therefore, dynamic control is important in management programs. Biological control was consistently indicated as the optimal strategy for all time horizons. Herbicide control was the optimal strategy for small areas with high-density infestation for long time horizons. We conclude that dynamic control, forecasting, and the time horizon are important considerations for invasive species managers who are under financial, logistical, and time constraints.

There is a current need to simulate leaching and runoff of pesticide from rice (Oryza sativa L.) paddies for assessing environmental impacts on a valuable agricultural system. The objective of this study was to develop a model for determining predicted environmental concentration (PEC) in soil, runoff, and ground water through the linkage of two models, rice water quality model (RICEWQ) and vadose zone transport model (VADOFT), to simulate pesticide fate and transport within a rice paddy and underlying soil profile. Model performance was evaluated with a field data set obtained from a 2-yr field experiment in 1997 and 1998 in northern Italy. The predictions of amount of pesticide running off from the paddy field and accumulating in the paddy sediment were in agreement with measured values. Leaching into the vadose zone accounted for approximately 19% of the applied dose, but only a small amount of chemical (<0.1%) was predicted to reach ground water at a 5-m depth due to sorption and transformation in the soil. The permeability of the soil and the water management practices in the paddy field were shown to have a strong influence on pesticide fate. These factors need to be well characterized in the field if model predictions are to be successful. The combined model developed in this work is an effective tool for exposure assessments for soil, surface water, and ground water, in the particular conditions of rice cultivation.

The need to quantitatively predict pesticide runoff and erosion under cropping system management has gained increasing importance. In Europe, predictive models have not yet been fully validated because of the lack of field data sets. The objective of this study was to validate the capability of PRZM (Pesticide Root Zone Model) 3.12 to predict water runoff, sediment erosion, and associated transport of atrazine (6-chloro-N2-ethyl-N4-isopropyl-1,3,5-triazine-2,4-diamine), terbuthylazine (N2-tert-butyl-6-chloro-N4-ethyl-1,3,5-triazine-2,4-diamine), and metolachlor [2-chloro-6'-ethyl-N-(2-methoxy-l-methylethyl)acet-o-toluidide] under common tillage management practices found in northern Italy. A 2-yr field data set was used to evaluate the model. Results showed that the model could qualitatively simulate significant differences of water runoff, soil erosion, and associated herbicide losses between conventional tillage (CT) and minimum tillage (MT) for a winter barley (Hordeum vulgare L.) cover crop. For MT, water runoff, soil erosion, herbicide losses in water runoff and eroded sediment, and the proportion of herbicide loss via sediment erosion were significantly lower than for CT. The model failed to correctly simulate event-based herbicide concentration, water runoff, and soil erosion. The model usually underestimated pesticide runoff events with high rainfall intensity and low daily precipitation volume, and overestimated runoff events with low intensity and high volume. The main reason was that the description of runoff and erosion processes is rather empirical in the model and not physically based. Moreover, model calculations do not adequately reflect the relationships between soil erosion intensity and chemical concentration in sediment losses, leading to discrepancies between predictions and field observations.

Model predictions are often seriously affected by uncertainties arising from many sources. Ignoring the uncertainty associated with model predictions may result in misleading interpretations when the model is used by a decision-maker for risk assessment. In this paper, an analysis of uncertainty was performed to estimate the uncertainty of model predictions and to screen out crucial variables using a Monte Carlo stochastic approach and a number of statistical methods, including ANOVA and stepwise multiple regression. The model studied was RICEWQ (Version 1.6.1), which was used to forecast pesticide fate in paddy fields. The results demonstrated that the paddy runoff concentration predicted by RICEWQ was in agreement with field measurements and the model can be applied to simulate pesticide fate at field scale. Model uncertainty was acceptable, runoff predictions conformed to a log-normal distribution with a short right tail, and predictions were reliable at field scale due to the narrow spread of uncertainty distribution. The main contribution of input variables to model uncertainty resulted from spatial (sediment-water partition coefficient and mixing depth to allow direct partitioning to bed) and management (time and rate of application) parameters, and weather conditions. Therefore, these crucial parameters should be carefully parameterized or precisely determined in each site-specific paddy field before the application of the model, since small errors of these parameters may induce large uncertainty of model outputs.

Lignocellulosic biomass in bale form has a low bulk density. Current in-field balers can achieve a bulk density of merely 120 to 180 kg dry matter (DM) m⁻³, whereas modern high-compression cutting balers produce up to 230 kg m⁻³. Mechanical compression is a straightforward technique to increase the material density, which significantly improves the efficiency of transportation and storage, and simplifies handling. Traditional compression technology mainly produces pellets, but conceivably bales could be compressed to a higher density as well. To design compression machinery in general, it is essential to determine the mechanical properties of biomass under compression and compression energy consumption. In addition, the material rebound percentage after compression is needed to design low-cost containerization methods for highly compressed bales. In this research, we established pressure–bulk density relationships and calculated Poisson’s ratio for Miscanthus (Miscanthus × giganteus, Poaceae/Gramineae) and switchgrass (Panicum virgatum L. Poacea/Gramineae). We also calculated rebound percentages for Miscanthus in two particle sizes. The results showed that the energy consumption for compression of Miscanthus and switchgrass is low, ranging from 0.01 to 0.05 % of the inherent heating value of the materials. Poisson’s ratios of Miscanthus and switchgrass ranged from 0.2 to 0.3 for various particle sizes. The rebound percentage was found as 28 % for unground Miscanthus and 23 % for Miscanthus ground to 6.35-mm particles. A common opinion is that high-level compression of biomass may reduce its energy content. Although in this research some of the biomass was exposed to an extreme pressure of 750 MPa, microscopic imagery revealed no fractions in the cell walls, leading to the conjecture that compression does not negatively impact the conversion potential of the biomass.

Liana abundance appears to be increasing, possibly to the detriment of trees, but methods for measuring liana biomass are undependable. We show that five commonly used allometric equations produce disparate results and discuss two large information gaps—Central African lianas and large lianas—that currently preclude accurate liana biomass estimation.

Lignocellulosic biomass in bale form has a low bulk density. Current in-field balers can achieve a bulk density of merely 120 to 180 kg dry matter (DM) m.sup.-3, whereas modern high-compression cutting balers produce up to 230 kg m.sup.-3. Mechanical compression is a straightforward technique to increase the material density, which significantly improves the efficiency of transportation and storage, and simplifies handling. Traditional compression technology mainly produces pellets, but conceivably bales could be compressed to a higher density as well. To design compression machinery in general, it is essential to determine the mechanical properties of biomass under compression and compression energy consumption. In addition, the material rebound percentage after compression is needed to design low-cost containerization methods for highly compressed bales. In this research, we established pressure-bulk density relationships and calculated Poisson's ratio for Miscanthus (Miscanthus x giganteus, Poaceae/Gramineae) and switchgrass (Panicum virgatum L. Poacea/Gramineae). We also calculated rebound percentages for Miscanthus in two particle sizes. The results showed that the energy consumption for compression of Miscanthus and switchgrass is low, ranging from 0.01 to 0.05 % of the inherent heating value of the materials. Poisson's ratios of Miscanthus and switchgrass ranged from 0.2 to 0.3 for various particle sizes. The rebound percentage was found as 28 % for unground Miscanthus and 23 % for Miscanthus ground to 6.35-mm particles. A common opinion is that high-level compression of biomass may reduce its energy content. Although in this research some of the biomass was exposed to an extreme pressure of 750 MPa, microscopic imagery revealed no fractions in the cell walls, leading to the conjecture that compression does not negatively impact the conversion potential of the biomass.

The variability and sensitivity of crop evapotranspiration (ET) measurements at field scale are still poorly understood in the irrigated farmland of arid region in Northwest China. The spatial and temporal dynamics and sensitivity of field ET are fundamental for the scaling up and validation of ET estimates from remote sensing data. In the study, we analysed the dynamics, impact factors and sensitivity of spring wheat ET during the growing season in Northwest China. Results indicated that there was a significant effect of first irrigation event on the spatial and temporary variability of ET. At the tillering-shooting stage (before the first irrigation event), spatial variability of ET was the lowest and gradually increased with crop growth. In some experimental plots, spring wheat ET had a significantly higher temporal stability than other plots except for the tillering-shooting stage. The sample sites with higher temporal stability could be used for long-term monitoring samples and for up scaling of ET measurements. In comparison with LAI and ET₀, surface soil moisture change ∆θ ₀–₂₀ cₘ was the most sensitive variable of ET measurements, which could be used as the auxiliary variable to improve the ET accuracy. With the soil moisture measurements, the relative error of ET was 9.4 % with only half the number of ET sampling data.

Pesticides are very important in European rice production. For appropriate environmental protection, it is useful to predict the potential impact of pesticides after application, in paddy fields, in paddy runoff, and in the surrounding water, by calculating predicted environmental concentrations (PECs). In this paper, a joint simulation is described, coupling a field-scale pesticide fate model (RICEWQ) and a transportation model (RIVWQ) to evaluate the potential for predicting environmental concentrations of pesticides in the paddy field and adjacent surface water bodies and comparing the predicted values with the monitoring data. The results demonstrate that the application of the calibrated field-scale RICEWQ model is a conservative method to predict the PEC at the watershed level, overestimating the observed data; the coupled RICEWQ and RIVWQ models could be adequately used to predict PECs in the surrounding water at watershed level and in the higher tier risk assessment procedure.[PUBLICATION ABSTRACT]