Celia M. Rodriguez-Dominguez

Vulnerability to cavitation in Olea europaea current-year shoots: further evidence of an open-vessel artifact associated with centrifuge and air-injection techniques

Different methods have been devised to analyze vulnerability to cavitation of plants. Although a good agreement between them is usually found, some discrepancies have been reported when measuring samples from long-vesseled species. The aim of this study was to evaluate possible artifacts derived from different methods and sample sizes. Current-year shoot segments of mature olive trees (Olea europaea), a long-vesseled species, were used to generate vulnerability curves (VCs) by bench dehydration, pressure collar and both static- and flow-centrifuge methods. For the latter, two different rotors were used to test possible effects of the rotor design on the curves. Indeed, high-resolution computed tomography (HRCT) images were used to evaluate the functional status of xylem at different water potentials. Measurements of native embolism were used to validate the methods used. The pressure collar and the two centrifugal methods showed greater vulnerability to cavitation than the dehydration method. The shift in vulnerability thresholds in centrifuge methods was more pronounced in shorter samples, supporting the open-vessel artifact hypothesis as a higher proportion of vessels were open in short samples. The two different rotor designs used for the flow-centrifuge method revealed similar vulnerability to cavitation. Only the bench dehydration or HRCT methods produced VCs that agreed with native levels of embolism and water potential values measured in the field.

Most stomatal closure in woody species under moderate drought can be explained by stomatal responses to leaf turgor

Reduced stomatal conductance (gs ) during soil drought in angiosperms may result from effects of leaf turgor on stomata and/or factors that do not directly depend on leaf turgor, including root-derived abscisic acid (ABA) signals. To quantify the roles of leaf turgor-mediated and leaf turgor-independent mechanisms in gs decline during drought, we measured drought responses of gs and water relations in three woody species (almond, grapevine and olive) under a range of conditions designed to generate independent variation in leaf and root turgor, including diurnal variation in evaporative demand and changes in plant hydraulic conductance and leaf osmotic pressure. We then applied these data to a process-based gs model and used a novel method to partition observed declines in gs during drought into contributions from each parameter in the model. Soil drought reduced gs by 63-84% across species, and the model reproduced these changes well (r(2)  = 0.91, P < 0.0001, n = 44) despite having only a single fitted parameter. Our analysis concluded that responses mediated by leaf turgor could explain over 87% of the observed decline in gs across species, adding to a growing body of evidence that challenges the root ABA-centric model of stomatal responses to drought.

Classification models for automatic identification of daily states from leaf turgor related measurements in olive

Abstract The leaf patch clamp pressure (LPCP) probe is being used to remotely assess leaf turgor pressure. Recently, different shapes of the LPCP daily curves have been suggested as potential water stress indicators for irrigation scheduling. These curves shapes, called states, have been studied and related to different water stress levels for olives. To our knowledge, the only way to differentiate these curves shapes or states is through the visual observation of the dynamics of the LPCP records during the day, which is highly time-consuming and reduces its potential to automatically schedule irrigation. The aims of this study were: (i) to obtain a random forest model to automatically identify the states from daily LPCP curves recorded in olive trees, by using visually identified states to train the model; (ii) to improve the identification of state II through a second random forest model, relating this state to the midday stem water potential, and; (iii) to obtain a random forest model to identify the states based on ranges of stem water potential. We used LPCP daily curves collected in a commercial olive orchard from 2011 to 2015. The states were visually identified for the days on which concomitant measurements of stem water potential and leaf stomatal conductance were made. We had a data set of 307 LPCP daily curves, being 157 curves in state I, 78 in state II and 71 in state III. The two biggest inflection points of the LPCP curves were used to adjust the models through the use of the R package “randomForest”, using the Leave-p-Out Cross-Validation method. With the first model, which was obtained from the whole dataset, its data regarding the inflection points and the visually identified states, we obtained an overall accuracy of 94.37%. With the second model, obtained with the use of the data regarding curves visually identified as state II only, the overall accuracy was of 88.64%. This model was adjusted to be used after the first model, to narrow the stem water potential range of state II curves. Finally, the third model was obtained using the whole dataset and the states established from ranges of stem water potential. This last model did not consider the visual identification, and yielded an overall accuracy of 88.08%. Our results facilitate the use of LPCP probes, since it allows for the automatic identification of the states related to leaf turgor pressure, a key information to schedule irrigation.

Providing adaptations for Special Education Mobile Learning

The development of mobile technologies is opening new possibilities in the field of education, specifically when it involves people with special educational needs (SEN). Mobility gives freedom to carry out learning activities at any moment or place. However, given the variety in SEN, it is difficult to create educational contents that fit for everybody. Rather, it seems appropriate to incorporate adaptation mechanisms that take into account the user profile, context or progress to modify the learning activities and adjust interaction, contents or presentation to the user’s needs. In this paper, an abstract architecture is presented to guide in the construction of mobile applications for SEN. It comprises five models: activities, user, evaluation, cooperation and authoring, with different adaptable features. To illustrate the proposal, the architecture has been implemented in two learning tools, Picaa and Sigueme, which have been successfully used by children with special needs.