Frost at anthesis of wheat reduces grain set. Characteristics of frost in a large section of the wheat belt of one Australian state (N.S.W.) are described. Using cluster analysis, the region can be divided into four homogeneous areas according to five general characteristics of frost. These characteristics are the mean Julian day of first and last frost, the mean number of frost days, the proportion of single-day frosts (days on which a frost did not occur on the following day) from August to October and the proportion of frost free periods from August to October of less than 5 days' duration.All these characteristics were found to,be closely related to altitude in all areas, but not to latitude or longitude. Within each area, regression equations were developed which explained at least 87% of the variation in four of the general characteristics. Thus, altitude appears to be a useful predictor for these characteristics.The validity of each equation was tested using three independent data sets. The mean percentage errors for the mean Julian day of first and last frost and the proportion of single-day frosts from August to October were 5, 5 and 8% respectively. Errors for the mean number of frost days and proportion of frost free periods from August to October of less than 5 days duration were approximately 7% for one site and approximately 20% for two other sites.The characteristics of frost in the period in which most winter crops flower are also described. The characteristics are the mean date of last heavy frost and last frost, and frost spells in each of August, September and October. In order to minimize risk from frost during anthesis and from high temperature and water stress during grain filling, this analysis suggests that anthesis should occur in earlylmid September, late Septemberlearly October and earlylmid October for the western, central and eastern parts of the region respectively. For areas in the north-eastern and south-eastern parts that are above 500 m, anthesis should occur mid/late October and earlylmid November respectively. These analytical findings are consistent with field studies on wheat.A weak negative correlation between the first and the last frost is present in most of the sites of the region. This suggests that if in any particular year the first frost occurs earlier than its long-term mean, then the last frost tends to occur later than its long-term mean.
Rainfall is an important variable in the wheat production areas of Australia. This analysis examines, firstly, the pattern of rainfall over 2.3 million ha of a high-quality wheat-producing region, and secondly, develops regression equations for rainfall prediction over this region. Most of the variation in rainfall pattern across the region is accounted for by differences in October-to-March (summer) rainfall and in April-to-September (winter) rainfall. The summer rainfall differences account for over two thirds of the variation. Based on these two rainfall periods, a partitioning of the study area reveals five distinct regions. The second part of the analysis uses multiple regression to provide a set of equations for rainfall prediction at any location in the region, for a number of rainfall periods. These equations use altitude, longitude and latitude as predictors. Nearly all of the equations explain between 80% and 94% of the variation in rainfall. Differences between regions are accounted for in the analysis, making the equations widely applicable. The validity of the mean rainfall equations was tested on three further sites: the mean prediction error was 6.9%. This approach may be applicable where large land masses with similar geographical features occur.
Introduction: Quantitative measures derived from the electroencephalogram (qEEG) have been used extensively in human medicine to assess the function of the brain under clinical manipulation (such as anesthesia and sedation), as well as in disease states (such as head trauma, coma and stroke). The goal of this study was to determine if qEEG measures could be used to predict the response of cats under isoflurane concentration to noxious stimuli. Methods: Twelve cats aged 1–2 years were included. Neurological and physical examination, blood chemistries and CBCs were normal in all cats. Anesthesia was induced with isoflurane (ISO) in O2, the cat was intubated and the femoral artery catheterized. The following physiologic data were collected continuously throughout the course of the experiment: temperature, blood pressure, respiratory rate, heart rate, and end‐tidal CO2 and ISO concentrations. An Aspect Medical Systems A‐1050 monitor was used to continuously record the raw electroencephalogram (EEG) and several qEEG measures including bispectral index (BIS), spectral edge frequency (SEF), burst suppression ratio (BSR), and total EEG power (POW). ISO concentration was maintained constant for 15 minutes, after which a tail clamp was applied for 1 minute. If the cat responded, the concentration was increased 10% and the procedure repeated. If the cat did not respond, the ISO concentration was decreased 10% and the procedure repeated. The minimum alveolar concentration (MAC) was the midpoint between a positive and negative response and, for each cat, this was determined three times. Results: BIS was significantly lower in cats at the lowest concentration at which there was no response (MAC−, 21.3±28.3) than at the highest concentration at which there was a response (MAC+, 63.4±22.1; p<.01, Wilcoxon signed rank). However, in 3/12 cats (25%), BIS was higher at MAC− than at MAC+. Of the other qEEG measures, only BSR was significantly different between MAC− and MAC+ (50.4±34.4 vs. 6.1±19.4, p<.01). Of the physiologic data collected, only respiratory rate was different between MAC− and MAC+ (29.4±10.0 vs. 45.3±17.9, p<.05). Conclusions: These results suggest that qEEG measures may be useful for anesthesia monitoring in cats, but measures that are less species‐specific than the BIS may hold more promise. In addition, this work suggests that further studies utilizing qEEG measures for evaluation of sedation during ventilation as well as outcome prediction in animals with head trauma and critical illnesses with CNS complications are indicated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.