How the Readability Level of Prior Text Impacts Comprehension of Subsequent Text

The purpose of this thesis research is to explore how the difficulty of text impacts the ability to process subsequent text. The basic idea is that difficult text might deplete cognitive or attentional resources, creating difficulties for readers as they try to continue reading afterwards. A pilot study was conducted to gather preliminary data prior to the two final studies. High (easy to read) and low (difficult to read) readability passages were created and presented prior to a target passage. The results from the pilot study suggested that there may be differences in comprehension based on the readability and difficulty level of preceding text. Study one investigated how the length of the preceding passage impacted comprehension of subsequent text. A 2 readability (easy vs hard) x 2 length (short vs long) ANOVA failed to find any significant main effects or a significant interaction. Study two investigated whether differences in working memory capacity might make some people more susceptible to the effects of difficulty on comprehension on subsequent text. An ANCOVA failed to find a significant effect of readability on comprehension of the subsequent text after controlling for verbal working memory scores. Working memory scores were also not a significant predictor of comprehension scores

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

The crucial role of soil when modelling the impact of climate change on crop production

peer reviewedAgMIP - The Agricultural Model Intercomparison and Improvement Projec

The AgMIP Soil and Crop Rotation Initiative

AgMIP - The Agricultural Model Intercomparison and Improvement Projec

Simulation of maize evapotranspiration: An inter-comparison among 29 maize models

201

Prediction of Evapotranspiration and Yields of Maize: An Inter-comparison among 29 Maize Models

An important aspect that determines the ability of crop growth models to predict growth and yield is their ability to predict the rate of water consumption or evapotranspiration (ET) of the crop, especially for rain-fed crops. If, for example, the predicted ET rate is too high, the simulated crop may exhaust its soil water supply before the next rain event, thereby causing growth and yield predictions that are too low. In a prior inter-comparison among maize growth models, ET predictions varied widely, but no observations of actual ET were available for comparison. Therefore, another study has been initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). This time observations of ET using the eddy covariance technique from an 8-year-long experiment conducted at Ames, IA are being used as the standard. Simulation results from 29 models have been completed. In the first “blind” phase for which only weather, soils, and management information were furnished to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. A detailed statistical analysis of the daily ET data from 2011, a “typical” rainfall year, showed that, as expected, the median of all the models was more accurate across several criteria (correlation, root mean square error, average difference, regression slope) than any particular model. However, some individual models were better than the median for a particular criteria. Predictions improved somewhat in later stages when the modelers were provided additional leaf area and growth information that allowed them to “calibrate” some of the parameters in their models to account for varietal characteristics, etc