scholarly journals Maize seedling emergence in response to climatic variability in a tropical rainforest area

2021 ◽  
Vol 117 (2) ◽  
pp. 1
Author(s):  
Chris Adegoke FAYOSE ◽  
Morakinyo Abiodun Bamidele FAKOREDE
Keyword(s):  
Air Temperature ◽  
Climatic Variability ◽  
Seedling Emergence ◽  
Maize Seedling ◽  
University Teaching ◽  
Stepwise Multiple Regression ◽  
Path Coefficient ◽  
Climatic Data ◽  
Heat Unit ◽  
Maize Varieties

<p>Environmental factors causing low seedling emergence often observed in tropical maize (Zea mays L.) are poorly documented. This study was conducted to investigate the effects of weather factors on maize seedling emergence at the Obafemi Awolowo University Teaching and Research Farm (OAU TRF). Five maize varieties sown weekly, in 3-replicate RCBD experiments throughout the 2016 and 2017 cropping seasons, were used to monitor emergence percentage (E %), emergence index (EI) and emergence rate index (ERI). Climatic data were obtained from the automatic weather station located on the farm. Analysis of variance revealed highly significant (P ≤ 0.01) environmental effect for all traits. Soil moisture (Sm), relative humidity, air temperature, heat unit and soil heat flux (SHF) showed significant (P ≤ 0.05) correlation coefficients with all traits, but there was no relationship between the emergence traits and grain yield. Stepwise multiple regression and sequential path coefficient analyses indicated that increased Sm, rather than rainfall per se, increased the speed of emergence. Minimum air temperature and SHF with direct effects, and heat unit with indirect effect, negatively affected emergence the most. Relatively low Tmin and SHF, along with just enough Sm maximized seedling emergence in the rainforest agro-ecology of southwestern Nigeria.</p>

scholarly journals <i>Brief communication</i> "A statistical validation for the cycles found in air temperature data using a Morlet wavelet-based method"

2010 ◽  
Vol 17 (3) ◽  
pp. 269-272 ◽  
Author(s):  
S. Nicolay ◽  
G. Mabille ◽  
X. Fettweis ◽  
M. Erpicum
Keyword(s):  
Air Temperature ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Weather Forecast ◽  
Temperature Data ◽  
Temperature Time Series ◽  
Climatic Data ◽  
Statistical Validation ◽  
Confidence Levels ◽  
Temperature Time

Abstract. Recently, new cycles, associated with periods of 30 and 43 months, respectively, have been observed by the authors in surface air temperature time series, using a wavelet-based methodology. Although many evidences attest the validity of this method applied to climatic data, no systematic study of its efficiency has been carried out. Here, we estimate confidence levels for this approach and show that the observed cycles are significant. Taking these cycles into consideration should prove helpful in increasing the accuracy of the climate model projections of climate change and weather forecast.

Monitoring Agricultural Drought Using El Niño and Southern Oscillation Data

2005 ◽  
Author(s):  
Lino Naranjo Díaz
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Climatic Variability ◽  
Past Century ◽  
Climatic Data ◽  
Weather Patterns ◽  
El Niño Events ◽  
Ring Analysis ◽  
El Nino Events

Almost all the studies performed during the past century have shown that drought is not the result of a single cause. Instead, it is the result of many factors varying in nature and scales. For this reason, researchers have been focusing their studies on the components of the climate system to explain a link between patterns (regional and global) of climatic variability and drought. Some drought patterns tend to recur frequently, particularly in the tropics. One such pattern is the El Niño and Southern Oscillation (ENSO). This chapter explains the main characteristics of the ENSO and its data forms, and how this phenomenon is related to the occurrence of drought in the world regions. Originally, the name El Niño was coined in the late 1800s by fishermen along the coast of Peru to refer to a seasonal invasion of south-flowing warm currents of the ocean that displaced the north-flowing cold currents in which they normally fished. The invasion of warm water disrupts both the marine food chain and the economies of coastal communities that are based on fishing and related industries. Because the phenomenon peaks around the Christmas season, the fishermen who first observed it named it “El Niño” (“the Christ Child”). In recent decades, scientists have recognized that El Niño is linked with other shifts in global weather patterns (Bjerknes, 1969; Wyrtki, 1975; Alexander, 1992; Trenberth, 1995; Nicholson and Kim, 1997). The recurring period of El Niño varies from two to seven years. The intensity and duration of the event vary too and are hard to predict. Typically, the duration of El Niño ranges from 14 to 22 months, but it can also be much longer or shorter. El Niño often begins early in the year and peaks in the following boreal winter. Although most El Niño events have many features in common, no two events are exactly the same. The presence of El Niño events during historical periods can be detected using climatic data interpreted from the tree ring analysis, sediment or ice cores, coral reef samples, and even historical accounts from early settlers.

scholarly journals 692. Effects of Regional Climatic Variability on West Nile Virus Outbreaks in the United States

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S249-S250
Author(s):  
Raghesh Varot Kangath ◽  
Buddhika Maduraperuma ◽  
Juliana Souza Borges ◽  
Rajasreepai Ramachandrapai
Keyword(s):  
United States ◽  
High Temperature ◽  
Population Density ◽  
Climatic Variability ◽  
The United States ◽  
Climatic Data ◽  
Cca Ordination ◽  
Neuroinvasive Disease ◽  
South Central ◽  
Risk Areas

Abstract Background Transmission of WNV to humans in the United States typically occurs between June and September since warm temperatures accelerate mosquito life cycle. Precipitation can cause increase in aquatic breeding but outbreaks often depends upon human water management. We examine epidemiology, patterns of WNV disease transmission, and identification of high-risk areas in the United States from 2003 to 2014. Methods Trends and relationships of WNV cases and climatic factors were analyzed among the regions of the United States from 2003 to 2014. Human WNV tabulate data and climatic data were obtained from Centers for Disease Control, and NOAA and Climate Data Guide, respectively. Canonical correspondence analysis (CCA) was performed using variables: (i) neuroinvasive disease cases, non-neuroinvasive disease cases, deaths, presumptiveviremic blood donors, (ii) precipitation, temperature, Palmer Drought Severity Index (PDSI) and population density. The CCA ordination was explained the variability between WNV disease cases andclimatic variables. Biplots were used to visualize the associations between WNV cases and climatic anomalies. Results We compared the state wise WNV disease cases in relation to climatic and population density in the United States from 2003 to 2014. A total of 4,064 cases in 2006, 956 cases in 2010 and, 2,141 cases in 2014 were reported in the 32 states of the United States. Colorado state reported the highest WNV cases in 2003 (2,947 cases; 33%), followed by Texas in 2012 (1,868 cases; 35%) and California in 2014 (801 case; 37%). CCA ordination showed distinguishable clustering patterns between south central (Texas, Louisiana, Mississippi, Arkansas, and Oklahoma) and northern Great Plains (North Dakota, South Dakota, and Nebraska) regions (Figure 1). High temperature and prolong drought were the most important variable predictor for high WNV outbreak. Conclusion Vector control methods focusing on prevention must be implemented to avoid epidemics of WNV if high temperature is leading to an unusual drought especially at the risk areas, such as Texas and California. However, high temperature with moist spell anomalies in the south central region showed a negative influence on WNV outbreak. Disclosures All authors: No reported disclosures.

scholarly journals Relationships between seed yield and yield components in narbon bean (Vicia narbonensis L.) by path analysis

1970 ◽  
Vol 37 (1) ◽  
pp. 27-32 ◽  
Author(s):  
Mevlüt Türk ◽  
Necmettin Çelik ◽  
Gamze Bayram ◽  
Emine Budakli
Keyword(s):  
Plant Height ◽  
Seed Yield ◽  
Harvest Index ◽  
Stepwise Multiple Regression ◽  
Path Coefficient ◽  
Vicia Narbonensis ◽  
Biological Yield ◽  
Primary Selection ◽  
Positive Direct ◽  
Weight Number

Results of correlation analysis indicated that seed yield in narbon bean (Vicia narbonensis L.) was correlated positively with harvest index, biological yield, weight, number of seed and number of pod per plant and also plant height and number of plant per m2. Path coefficient analyses revealed that harvest index and biological yield had higher positive direct effects on seed yield than other variables. Stepwise multiple regression analysis showed that 95.1% of total variation in seed yield could be explained by the variation in harvest index, biological yield and plant height. Results suggest that harvest index and biological yield are primary selection criteria for improving seed yield in narbon bean.   

scholarly journals The Impact of Infiltration on Heating Systems Dimensioning in Estonian Climate

2020 ◽  
Vol 172 ◽  
pp. 05004
Author(s):  
Raimo Simson ◽  
Taaniel Rebane ◽  
Martin Kiil ◽  
Martin Thalfeldt ◽  
Jarek Kurnitski
Keyword(s):  
Air Temperature ◽  
Indoor Air ◽  
Energy Performance ◽  
Simulation Software ◽  
Heat Losses ◽  
Climatic Data ◽  
Heating Systems ◽  
Air Temperatures ◽  
The Impact ◽  
Calculation Models

In this study we analysed the climatic conditions for infiltration estimation, different calculation methods and infiltration impact on heat load for heating systems dimensioning. To determine the wind conditions at low air temperatures of the coastal- and inland climatic zones in Estonia, 42 years of climatic data for Tallinn and Tartu were investigated. Calculation models with detailed air leakages were constructed of a single and two-storey detached house using dynamic simulation software IDA ICE. Simulations were carried out with the constructed calculation models, simulating various wind and sheltering conditions to determine the heating load of the buildings under measured wind conditions at the design external air temperatures. The simulation results were compared with results calculated with European Standard EN 12831:2017, methodology given in the Estonian regulation for calculating energy performance of buildings and with simulations using the default settings in IDA ICE based on the ASHRAE design day conditions. The percentage of heat losses caused by infiltration was found as 13-16% of all heat losses for the studied buildings. Simulations with historical climate periods showed that even in windy weather conditions the heating system dimensioned by the methods analysed may not be able to provide the required indoor air temperature. Analysis using the coldest and windiest periods showed that when systems are dimensioned by the studied methods, the highest decline in indoor air temperature occurs on the windiest day and not on the coldest day. The impact of high wind speeds and low sheltering conditions resulted up to 50% of all heat losses.

Production of Belarussian soyabeans (Glycine max) in the UK: predicting time to emergence

1999 ◽  
Vol 132 (4) ◽  
pp. 381-385 ◽  
Author(s):  
A. O'DELL ◽  
D. H. SCARISBRICK ◽  
D. A. BAKER
Keyword(s):  
Glycine Max ◽  
Reliable Method ◽  
Seedling Emergence ◽  
Planting Date ◽  
Reliable System ◽  
Heat Unit ◽  
Planting Dates ◽  
Coefficients Of Variation ◽  
Soil Temperatures ◽  
The Uk

A field experiment was carried out on soyabean (Glycine max (L.) Merr.) to measure the effect of planting date (soil temperature) on seedling emergence. Seeds were sown at weekly intervals on seven planting dates from April until the end of May in SE England in 1997. Planting date had no significant effect on final percentage emergence but had a highly significant effect on time to emergence. The coefficients of variation (c.v.) for the number of days to emergence (calendar days) were high (43–45%), and therefore not a reliable method for predicting emergence. Three accumulated heat unit (AHU) methods based on air and soil temperatures were compared with the calendar day method to determine the most reliable system for predicting seedling emergence. Accumulated soil temperatures above a base of 9·0 °C had the lowest c.v.s (8–15%) and were shown to be the most reliable predictor of emergence.

Identifying Rogue Air Temperature Stations Using Cluster Analysis of Percentile Trends

2005 ◽  
Vol 18 (8) ◽  
pp. 1275-1287 ◽  
Author(s):  
Scott M. Robeson ◽  
Jeffrey A. Doty
Keyword(s):  
Cluster Analysis ◽  
Air Temperature ◽  
Large Body ◽  
Large Temperature ◽  
Daily Maximum ◽  
Climatic Data ◽  
Single Linkage ◽  
Frequency Distributions ◽  
Temperature Trends ◽  
Average Linkage

Abstract A new and efficient method for identifying “rogue” air temperature stations—locations with unusually large air temperature trends—is presented. Instrumentation problems and spatially unrepresentative local climates are sometimes more apparent in air temperature extremes, yet can have more subtle impacts on variations in mean air temperature. As a result, using data from over 1300 stations in North America, the tails of daily air temperature frequency distributions were examined for unusual trends. In particular, linear trends in the 5th percentile of daily minimum air temperature during the winter months and the 95th percentile of daily maximum air temperature during the summer were analyzed. Cluster analysis then was used to identify stations that were distinct from other locations. Both single- and average linkage clustering were evaluated. By identifying individual stations along the entire periphery of the percentile trend space, single-linkage clustering appears to produce better results than that of average linkage. Average linkage clustering tends to group together several stations with large trends; however, only a handful of these stations appear distinctly different from the large body of trends toward the center of the percentile trend space. Maps of the rogue stations show that most are in close proximity to numerous other stations that were not grouped into the rogue cluster, making it unlikely that the unusually large temperature trends were due to regional climatic variations. As with all approaches for evaluating data quality, time series plots and station history information also must be inspected to more fully understand inhomogeneous variations in historical climatic data.

Measurement Requirements for Climate Monitoring of Upper-Air Temperature Derived from Reanalysis Data

2006 ◽  
Vol 19 (5) ◽  
pp. 854-871 ◽  
Author(s):  
Dian J. Seidel ◽  
Melissa Free
Keyword(s):  
Air Temperature ◽  
Statistical Tests ◽  
Reanalysis Data ◽  
Climatic Data ◽  
Single Observation ◽  
Half Century ◽  
Climate Monitoring ◽  
System Requirements ◽  
Incomplete Sampling

Abstract Using a reanalysis of the climate of the past half century as a model of temperature variations over the next half century, tests of various data collection protocols are made to develop recommendations for observing system requirements for monitoring upper-air temperature. The analysis focuses on accurately estimating monthly climatic data (specifically, monthly average temperature and its standard deviation) and multidecadal trends in monthly temperatures at specified locations, from the surface to 30 hPa. It does not address upper-air network size or station location issues. The effects of reducing the precision of temperature data, incomplete sampling of the diurnal cycle, incomplete sampling of the days of the month, imperfect long-term stability of the observations, and changes in observation schedule are assessed. To ensure accurate monthly climate statistics, observations with at least 0.5-K precision, made at least twice daily, at least once every two or three days are sufficient. Using these same criteria, and maintaining long-term measurement stability to within 0.25 (0.1) K, for periods of 20 to 50 yr, errors in trend estimates can be avoided in at least 90% (95%) of cases. In practical terms, this requires no more than one intervention (e.g., instrument change) over the period of record, and its effect must be to change the measurement bias by no more than 0.25 (0.1) K. The effect of the first intervention dominates the effects of subsequent, uncorrelated interventions. Changes in observation schedule also affect trend estimates. Reducing the number of observations per day, or changing the timing of a single observation per day, has a greater potential to produce errors in trends than reducing the number of days per month on which observations are made. These findings depend on the validity of using reanalysis data to approximate the statistical nature of future climate variations, and on the statistical tests employed. However, the results are based on conservative assumptions, so that adopting observing system requirements based on this analysis should result in a data archive that will meet climate monitoring needs over the next 50 yr.

scholarly journals Assessment of high (diurnal) to low (seasonal) frequency variations of isoprene emission rates using a neural network approach

2008 ◽  
Vol 8 (7) ◽  
pp. 2089-2101 ◽  
Author(s):  
C. Boissard ◽  
F. Chervier ◽  
A. L. Dutot
Keyword(s):  
Air Temperature ◽  
Low Frequency ◽  
Photosynthetic Photon Flux Density ◽  
Photon Flux ◽  
Photon Flux Density ◽  
Climatic Data ◽  
Emission Rates ◽  
Neural Network Approach ◽  
Isoprene Emission ◽  
Frequency Variations

Abstract. Using a statistical approach based on artificial neural networks, an emission algorithm (ISO-LF) accounting for high to low frequency variations was developed for isoprene emission rates. ISO-LF was optimised using a data base (ISO-DB) specifically designed for this work, which consists of 1321 emission rates collected in the literature and 34 environmental variables, measured or assessed using National Climatic Data Center or National Centers for Environmental Predictions meteorological databases. ISO-DB covers a large variety of emitters (25 species) and environmental conditions (10° S to 60° N). When only instantaneous environmental regressors (instantaneous air temperature T0 and photosynthetic photon flux density L0) were used, a maximum of 60% of the overall isoprene variability was assessed with the highest emissions being strongly underestimated. ISO-LF includes a total of 9 high (instantaneous) to low (up to 3 weeks) frequency regressors and accounts for up to 91% of the isoprene emission variability, whatever the emission range, species or climate investigated. ISO-LF was found to be mainly sensitive to air temperature cumulated over 3 weeks (T21) and to L0 and T0 variations. T21, T0 and L0 only accounts for 76% of the overall variability.

scholarly journals Experimental measurements for improved understanding and simulation of snowmelt events in the Western Tatra Mountains

2016 ◽  
Vol 64 (4) ◽  
pp. 316-328 ◽  
Author(s):  
Pavel Krajčí ◽  
Michal Danko ◽  
Jozef Hlavčo ◽  
Zdeněk Kostka ◽  
Ladislav Holko
Keyword(s):  
Spatial Variability ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Flood Forecasting ◽  
Snow Accumulation ◽  
Small Scale ◽  
Climatic Data ◽  
Tatra Mountains ◽  
Snow Water

AbstractSnow accumulation and melt are highly variable. Therefore, correct modeling of spatial variability of the snowmelt, timing and magnitude of catchment runoff still represents a challenge in mountain catchments for flood forecasting. The article presents the setup and results of detailed field measurements of snow related characteristics in a mountain microcatchment (area 59 000 m2, mean altitude 1509 m a. s. l.) in the Western Tatra Mountains, Slovakia obtained in winter 2015. Snow water equivalent (SWE) measurements at 27 points documented a very large spatial variability through the entire winter. For instance, range of the SWE values exceeded 500 mm at the end of the accumulation period (March 2015). Simple snow lysimeters indicated that variability of snowmelt and discharge measured at the catchment outlet corresponded well with the rise of air temperature above 0°C. Temperature measurements at soil surface were used to identify the snow cover duration at particular points. Snow melt duration was related to spatial distribution of snow cover and spatial patterns of snow radiation. Obtained data together with standard climatic data (precipitation and air temperature) were used to calibrate and validate the spatially distributed hydrological model MIKE-SHE. The spatial redistribution of input precipitation seems to be important for modeling even on such a small scale. Acceptable simulation of snow water equivalents and snow duration does not guarantee correct simulation of peakflow at short-time (hourly) scale required for example in flood forecasting. Temporal variability of the stream discharge during the snowmelt period was simulated correctly, but the simulated discharge was overestimated.

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