Accurate crown characterization of large isolated olive trees is vital for adjusting spray doses in three-dimensional crop agriculture. Among the many methodologies available, laser sensors have ...proved to be the most reliable and accurate. However, their operation is time consuming and requires specialist knowledge and so a simpler crown characterization method is required. To this end, three methods were evaluated and compared with LiDAR measurements to determine their accuracy: Vertical Crown Projected Area method (VCPA), Ellipsoid Volume method (VE) and Tree Silhouette Volume method (VTS). Trials were performed in three different kinds of olive tree plantations: intensive, adapted one-trunked traditional and traditional. In total, 55 trees were characterized. Results show that all three methods are appropriate to estimate the crown volume, reaching high coefficients of determination: R2 = 0.783, 0.843 and 0.824 for VCPA, VE and VTS, respectively. However, discrepancies arise when evaluating tree plantations separately, especially for traditional trees. Here, correlations between LiDAR volume and other parameters showed that the Mean Vector calculated for VCPA method showed the highest correlation for traditional trees, thus its use in traditional plantations is highly recommended.
Observations from the Gravity Recovery and Climate Experiment (GRACE) satellite missionprovide quantitative estimates of the global water budget components. However, these estimatesare uncertain as ...they show discrepancies when different parameters are used in the processing ofthe GRACE data. We examine trends in ocean mass, ice loss from Antarctica, Greenland, arcticislands and trends in water storage over land and glaciers from GRACE data (2005–2015) andexplore the associated uncertainty. We consider variations in six different GRACE processingparameters, namely the processing centre of the raw GRACE solutions, the geocentre motion,the Earth oblateness, the filtering, the leakage correction and the glacial isostatic adjustment(GIA). Considering all possible combinations of the different processing parameters leads toan ensemble of 1500 post-processed GRACE solutions, which is assumed to cover a significantpart of the uncertainty range of GRACE estimates. The ensemble-mean trend in all globalwater budget components agree within uncertainties with previous estimates based on differentsources of observations. The uncertainty in the global water budget is±0.27 mm yr−1at the 90per cent confidence level (CL) over 2005–2015. We find that the uncertainty in the geocentremotion and GIA corrections dominate the uncertainty in GRACE estimate of the globalwater budget. Their contribution to the uncertainty in GRACE estimate is respectively±0.21and±0.12 mm yr−1(90 per cent CL). This uncertainty in GRACE estimate implies anuncertainty in the net warming of the ocean and the Earth energy budget of±0.25 W m−2(90per cent CL) when inferred using the sea level budget approach.
Background. The sensitivity of the MVista Histoplasma antigen enzyme immunoassay (MiraVista Diagnostics) has been evaluated in disseminated histoplasmosis in patients with AIDS and in the "epidemic" ...form of acute pneumonia. Moreover, there has been no evaluation of the sensitivity of antigenemia detection in disseminated histoplasmosis after the implementation of methods to dissociate immune complexes and denature released antibodies. The goal of this study was to determine the sensitivity of the current antigen assay in different categories of histoplasmosis. Methods. Urine and serum specimens obtained from 218 patients with histoplasmosis and 229 control subjects, including 30 with blastomycosis, were tested. Results. Antigenuria was detected in 91.8% of 158 patients with disseminated histoplasmosis, 83.3% of 6 patients with acute histoplasmosis, 30.4% of 46 patients with subacute histoplasmosis, and 87.5% of 8 patients with chronic pulmonary histoplasmosis; antigenemia was present in 100% of 31 tested cases of disseminated histoplasmosis. Among patients with disseminated cases, antigenuria was detected more often and at higher concentrations in immunocompromised patients and those with severe disease. Specificity was 99.0% for patients with nonfungal infections (n = 130) and in healthy subjects (n = 69), but cross-reactivity occurred in 90% of patients with blastomycosis. Conclusions. The sensitivity of antigen detection in disseminated histoplasmosis is higher in immunocompromised patients than in immunocompetent patients and in patients with more severe illness. The sensitivity for detection of antigenemia is similar to that for antigenuria in disseminated infection.
Olive is a key crop in Europe, especially in countries around the Mediterranean Basin. Optimising the parameters of a spray is essential for sustainable pesticide use, especially in high-input ...systems, such as the super-intensive hedgerow system. Parameters may be optimised by adjusting the applied volume and airflow rate of sprays, in addition to the liquid to air proportion and the relationship between air velocity and airflow rate. Two spray experiments using a commercial airblast sprayer were conducted in a super-intensive orchard to study how varying the liquid volume rate (testing volumes of 182, 619, and 1603lha−1) and volumetric airflow rate (with flow rates of 11.93, 8.90, and 6.15m3s−1) influences the coverage parameters and the amount and distribution of deposits in different zones of the canopy.. Our results showed that an increase in the application volume raised the mean deposit and percentage coverage, but decreased the application efficiency, spray penetration, and deposit homogeneity. Furthermore, we found that the volumetric airflow rate had a lower influence on the studied parameters than the liquid volume; however, an increase in the airflow rate improved the application efficiency and homogeneity to a certain threshold, after which the spray quality decreased. This decrease was observed in the high-flow treatment. Our results demonstrate that intermediate liquid volume rates and volumetric airflow rates are required for the optimal spraying of pesticides on super-intensive olive crops, and would reduce current pollution levels.
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•We tested the effect of volume and airflow rates on spray applications.•The tests were carried out in a super-intensive orchard.•Increased application volume resulted in increased spray deposit and coverage.•Increased volume decreased the application efficiency and deposit homogeneity.•The volumetric airflow rate had a lower influence than the volume.
•The thermal behaviour of rice straw under pyrolysis and combustion was compared.•A simplified kinetic triplet and the mechanism of reaction were obtained.•The initial design parameters of a spouted ...bed reactor were discussed.
The processes of pyrolysis and combustion of rice straw will be carried out in a spouted bed reactor. Both thermo-chemical processes were simulated in the first stage by multi-rate linear non-isothermal thermogravimetric (TGA) experiments using Ar and O2 as carrier gas respectively. The results obtained from the TGA measurements, the kinetic methodology based on the combination of the iso-conversional methods Friedman, Flynn–Wall–Ozawa, Kissinger–Akahira–Sunose, Vyazovkin and the use of Master Plots assessed by Perez-Maqueda criterion have permitted to describe mathematically both thermo-chemical reactions. Lower operational temperatures and higher kinetic parameters (Ea, n, A) were required to carry out combustion reactions respect to those for pyrolysis. These results will be the initial parameters that will define both thermo-chemical processes in a spouted bed reactor.
Studies of the impact of climate change on water resources usually follow a top to bottom approach: a scenario of emissions is used to run a GCM simulation, which is downscaled (RCM and/or ...stastistical methods) and bias-corrected. Then, this data is used to force a hydrological model. Seldom, impact studies take into account all relevant uncertainties. In fact, many published studies only use one climate model and one downscaling technique. In this study, the outputs of an atmosphere–ocean regional climate model are downscaled and bias-corrected using three different techniques: a statistical method based on weather regimes, a quantile-mapping method and the method of the anomaly. The resulting data are used to force a distributed hydrological model to simulate the French Mediterranean basins. These are characterized by water scarcity and an increasing human pressure, which cause a demand in assessments on the impact of climate change hydrological systems. The purpose of the study is mainly methodological: the evaluation of the uncertainty related to the downscaling and bias-correction step. The periods chosen to compare the changes are the end of the 20th century (1970–2000) and the middle of the 21st century (2035–2065). The study shows that the three methods produce similar anomalies of the mean annual precipitation, but there are important differences, mainly in terms of spatial patterns. The study also shows that there are important differences in the anomalies of temperature. These uncertainties are amplified by the hydrological model. In some basins, the simulations do not agree in the sign of the anomalies and, in many others, the differences in amplitude of the anomaly are very important. Therefore, the uncertainty related to the downscaling and bias-correction of the climate simulation must be taken into account in order to better estimate the impact of climate change, with its uncertainty, on a specific basin. The study also shows that according to the RCM simulation used and to the periods studied, there might be significant increases of winter precipitation on the Cévennes region of the Massif Central, which is already affected by flash floods, and significant decreases of summer precipitation in most of the region. This will cause a decrease in the average discharge in the middle of the 21st in most of the gauging stations studied, specially in summer. Winter and, maybe spring, in some areas, are the exception, as discharge may increase in some basins.
Unlike the traditional subgrid scale parameterizations used in climate models, current machine learning (ML) parameterizations are only tuned offline, by minimizing a loss function on outputs from ...high‐resolution models. This approach often leads to numerical instabilities and long‐term biases. Here, we propose a method to design tunable ML parameterizations and calibrate them online. The calibration of the ML parameterization is achieved in two steps. First, some model parameters are included within the ML model input. This ML model is fitted at once for a range of values of the parameters, using an offline metric. Second, once the ML parameterization has been plugged into the climate model, the parameters included among the ML inputs are optimized with respect to an online metric quantifying errors on long‐term statistics. We illustrate our method with two simple dynamical systems. Our approach significantly reduces long‐term biases of the ML model.
Plain Language Summary
In numerical climate models, processes occurring at scales smaller than the model resolution (e.g., convection, turbulence) need to be represented by “parameterizations.” Parameterizations provide a simplified yet numerically affordable version of the modeled processes. Recently, parameterizations are also developed using machine learning (ML) by fitting to outputs from high resolution climate models. This method can lead to long‐term biases when incorporating the ML parameterizations into the climate model. And, there is no possibility in the current approach to calibrate the ML parameterization to alleviate these biases. We propose here an innovative approach to calibrate ML parameterizations once they have been fitted to a learning sample. Our approach has been successfully tested on two toy models. A first set of experiments focus on the retrieval of the value of parameters used to generate a reference data set. In the second experiment, the value of some parameters not included in the neural network (NN) has been biased, resulting in errors in long‐term statistics. Finding the optimal value of the NN input parameter has significantly improved the accuracy of the resulting model. Our method could be applied to improve the prediction of long‐term variables in climate models.
Key Points
Tunable parameters are included to the inputs of a neural network (NN) parameterization
The tunable NN parameters are optimized by using a kriging method
Long‐term statistical properties of the NN‐based model are tuned without any new learning procedure
The equilibrium climate sensitivity, that is, the global‐mean surface‐air temperature change in response to a doubling of the carbon dioxide concentration is a widely used metric in climate change ...studies. Its exact value is rarely known because its estimation requires a long integration time of several thousand years. We propose a method to estimate an accurate value of the equilibrium response from fully coupled climate models at a reasonable computational cost. Using this method, our state‐of‐the‐art climate model CNRM‐CM6‐1 reaches a stationary state after only few hundred of years of integration. This “Fast‐Forward” method consists of an optimal two‐step forcing pathway designed using the framework of a two‐layer energy balance model. It can be applied easily to any coupled climate model.
Key Points
A simple method for estimating the equilibrium climate sensitivity is proposed
The method allows to simulate the stationary climate corresponding to any given radiative perturbation with a limited computational cost
The method can be applied to any atmosphere‐ocean coupled climate model
The action of thermo-mechanical degradation induced by mechanical recycling of poly(ethylene terephthalate) was simulated by successive injection moulding cycles. Degradation reactions provoked chain ...scissions and a reduction in molar mass mainly driven by the reduction of diethyleneglycol to ethylene glycol units in the flexible domain of the PET backbone, and the formation of –OH terminated species with shorter chain length. The consequent microstructural changes were quantified taking into account a three-fraction model involving crystalline, mobile amorphous (MAF) and rigid amorphous fractions (RAF). A remarkable increase of RAF, to a detriment of MAF was observed, while the percentage of crystalline fraction remained nearly constant. A deeper analysis of the melting behaviour, the segmental dynamics around the glass-rubber relaxation, and the macroscopic mechanical performance, showed the role of each fraction leading to a loss of thermal, viscoelastic and mechanical features, particularly remarkable after the first processing cycle.
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