Transpiração e crescimento foliar de crisântemo em função da fração de água transpirável no substrato

The objective of this work was to evaluate the response of transpiration and leaf growth of chrysanthemum (Dendranthema grandiflorum) cultivars to available water in the substrate, represented by the fraction of transpirable substrate water (FTbSW). The experiments were performed in two periods, in a completely randomized design, with four chrysanthemum cultivars (Cherie White, Bronze Repin, Yoapple Valley, and Calabria), under two substrate water conditions (with or without water stress), with 10 replicates. Plants were grown in a greenhouse, in 2.8-L pots with substrate. FTSbW, transpiration, and leaf growth were measured daily, during the period of water deficit. The average threshold FTSbW, indicating that transpiration and leaf growth began to be affected, was respectively 0.63 and 0.68 for 'Cherie White', 0.60 and 0.69 for 'Bronze Repin', 0.53 and 0.59 for 'Yoapple Valley', and 0.51 and 0.54 for 'Calabria'. Available water decrease in the substrate reduces leaf growth before restricting transpiration. The Cherie White and Bronze Repin cultivars are more tolerant to water deficit by closing the stomata earlier and retaining more water in the substrate than the Yoapple Valley and Calabria cultivars.O objetivo deste trabalho foi avaliar a resposta da transpiração e do crescimento foliar de cultivares de crisântemo (Dendranthema grandiflorum) ao conteúdo de água disponível no substrato, representado pela fração de água transpirável no substrato (FATSb). Os experimentos foram realizados em dois períodos, em delineamento inteiramente casualizado, com quatro cultivares de crisântemo (Cherie White, Bronze Repin, Yoapple Valley e Calabria), em duas condições hídricas (com ou sem deficiência hídrica), com 10 repetições. As plantas foram cultivadas em casa de vegetação, em vasos de 2,8 L preenchidos com substrato. A FATSb, a transpiração e o crescimento foliar foram determinados diariamente durante o período de deficiência hídrica. As FATSb críticas médias, indicativas de que a transpiração e o crescimento foliar começam a ser afetados, foram respectivamente de 0,63 e 0,68 para 'Cherie White', 0,60 e 0,69 para 'Bronze Repin', 0,53 e 0,59 para 'Yoapple Valley', e 0,51 e 0,54 para 'Calabria'. A diminuição da água disponível no substrato provoca a redução do crescimento foliar antes de restringir a transpiração. As cultivares Cherie White e Bronze Repin são mais tolerantes ao deficit hídrico por fechar os estômatos antes e conservar mais a água no substrato do que as cultivares Yoapple Valley e Calabria

Transpiration and leaf growth of potato clones in response to soil water deficit

Potato (Solanum tuberosum ssp. Tuberosum) crop is particularly susceptible to water deficit because of its small and shallow root system. The fraction of transpirable soil water (FTSW) approach has been widely used in the evaluation of plant responses to water deficit in different crops. The FTSW 34 threshold (when stomatal closure starts) is a trait of particular interest because it is an indicator of tolerance to water deficit. The FTSW threshold for decline in transpiration and leaf growth was evaluated in a drying soil to identify potato clones tolerant to water deficit. Two greenhouse experiments were carried out in pots, with three advanced clones and the cultivar Asterix. The FTSW, transpiration and leaf growth were measured on a daily basis, during the period of soil drying. FTSW was an efficient method to separate potato clones with regard to their response to water deficit. The advancedclones SMINIA 02106-11 and SMINIA 00017-6 are more tolerant to soil water deficit than the cultivar Asterix, and the clone SMINIA 793101-3 is more tolerant only under high solar radiation

Prediction of CEC using fractal parameters by artificial neural networks

The prediction of cation exchange capacity from readily available soil properties remains a challenge. In this study, firstly, we extended the entire particle size distribution curve from limited soil texture data and, at the second step, calculated the fractal parameters from the particle size distribution curve. Three pedotransfer functions were developed based on soil properties, parameters of particle size distribution curve model and fractal parameters of particle size distribution curve fractal model using the artificial neural networks technique. 1 662 soil samples were collected and separated into eight groups. Particle size distribution curve model parameters were estimated from limited soil texture data by the Skaggs method and fractal parameters were calculated by Bird model. Using particle size distribution curve model parameters and fractal parameters in the pedotransfer functions resulted in improvements of cation exchange capacity predictions. The pedotransfer functions that used fractal parameters as predictors performed better than the those which used particle size distribution curve model parameters. This can be related to the non-linear relationship between cation exchange capacity and fractal parameters. Partitioning the soil samples significantly increased the accuracy and reliability of the pedotransfer functions. Substantial improvement was achieved by utilising fractal parameters in the clusters

An emergency response decision matrix against terrorist attacks with improvised device in chemical clusters

Chemical industrial areas may constitute potential targets for deliberate actions by terrorists. Terrorists having sufficient knowledge of chemical process operations or plant layout may take advantage of improvised explosive devices (IEDs) to cause major events such as fire, explosion and toxic gas dispersion with cross-border consequences in chemical clusters. Thus, an efficient cluster-wise emergency plan to enhance the promptness and efficacy of responding to such attacks is crucial. In this study, the effects of blast wave caused by IEDs are assessed and its potentiality in triggering domino scenarios are analysed. A decision tree is developed to determine the emergency level of each company within the cluster based on the attack outcomes. Furthermore, an alert notification system is set based on a decision matrix. Finally, the identified emergency levels and the alert levels are presented in form of a multi-plant decision matrix. The application of the developed methodology is demonstrated in a case study.</p

Prediction of CEC Using Fractal Parameters by Artificial Neural Networks

The prediction of cation exchange capacity from readily available soil properties remains a challenge. In this study, firstly, we extended the entire particle size distribution curve from limited soil texture data and, at the second step, calculated the fractal parameters from the particle size distribution curve. Three pedotransfer functions were developed based on soil properties, parameters of particle size distribution curve model and fractal parameters of particle size distribution curve fractal model using the artificial neural networks technique. 1 662 soil samples were collected and separated into eight groups. Particle size distribution curve model parameters were estimated from limited soil texture data by the Skaggs method and fractal parameters were calculated by Bird model. Using particle size distribution curve model parameters and fractal parameters in the pedotransfer functions resulted in improvements of cation exchange capacity predictions. The pedotransfer functions that used fractal parameters as predictors performed better than the those which used particle size distribution curve model parameters. This can be related to the non-linear relationship between cation exchange capacity and fractal parameters. Partitioning the soil samples significantly increased the accuracy and reliability of the pedotransfer functions. Substantial improvement was achieved by utilising fractal parameters in the clusters