Weather, herbage quality and milk production in pastoral systems. 1. Temporal patterns and intra-relationships in weather variables

2009 ◽  
Vol 49 (3) ◽  
pp. 192 ◽  
Author(s):  
J. R. Roche ◽  
L. R. Turner ◽  
J. M. Lee ◽  
D. C. Edmeades ◽  
D. J. Donaghy ◽  
...  
Keyword(s):  
Potential Evapotranspiration ◽  
Temporal Trends ◽  
Animal Production ◽  
Weather Data ◽  
Weather Variables ◽  
Weather Factors ◽  
Pastoral Systems ◽  
Substantial Impact ◽  
Production Region ◽  
Daily Weather Data

Prevailing weather conditions are one factor that influences herbage growth and quality, and therefore may have a substantial impact on animal production. Before investigating relationships between weather factors, herbage growth and quality, and animal production, it is beneficial to first quantify temporal trends in weather variables. The objective of the present study was to investigate the existence of temporal weather trends in a predominantly dairy production region of New Zealand, and to quantify the level of intra-dependency among the weather variables measured. Daily weather data across the years 1995 to 2001 were merged. Fitted sinusoidal functions demonstrated cyclic temporal trends in weather throughout the year. Air and soil temperatures, radiation, and potential evapotranspiration were highly repeatable within fortnight. Repeatability of all other weather variables was low; for example repeatability of rainfall was ≤7%. Linear relationships were also observed among all weather variables. All air and soil temperature measurements were highly positively correlated with each other (r = 0.53–0.99), and with evaporation (r = 0.40–0.68) and potential evapotranspiration (r = 0.43–0.79), while maximum air temperature was positively correlated with radiation (r = 0.61). Further investigation is required to quantify the effect of temporal weather trends on herbage growth and quality, and subsequent animal production.

Weather, herbage quality and milk production in pastoral systems. 3. Inter-relationships and associations between weather variables and herbage growth rate, quality and mineral concentration

2009 ◽  
Vol 49 (3) ◽  
pp. 211 ◽  
Author(s):  
J. R. Roche ◽  
L. R. Turner ◽  
J. M. Lee ◽  
D. C. Edmeades ◽  
D. J. Donaghy ◽  
...  
Keyword(s):  
Growth Rate ◽  
Animal Production ◽  
Water Soluble ◽  
Weather Data ◽  
Weather Variables ◽  
Mineral Concentration ◽  
Substantial Impact ◽  
Daily Weather Data ◽  
Herbage Quality ◽  
Time Of Year

Prevailing weather conditions influence herbage growth and quality, and therefore may have a substantial impact on animal production. Before investigating relationships between weather factors, herbage quality and animal production, it is beneficial to first quantify interactions between herbage quality characteristics and mineral concentrations. The objective of the present study was to investigate the association between weather and herbage growth rate, quality and mineral concentration under rotational grazing systems. Daily weather data and weekly records of herbage quality and mineral concentration from a research dairy farm were available across the years 1995 to 2001, inclusive. Herbage growth rates were also recorded on a monthly basis. Results imply moderate correlations between some weather variables and herbage quality and mineral concentration. Generally, the strength of the absolute correlations between weather and herbage-related variables decreased following adjustment of the herbage-related variables for month of year and research farmlet. Negative correlations existed between rainfall and herbage water-soluble carbohydrate (r = –0.19) and organic matter digestibility concentration (r = –0.13) and metabolisable energy content (r = –0.14), independent of time of year and farmlet. Weather explained up to 14% of the variation in herbage nutrient content over and above that explained by time of year and farmlet. Significantly different correlations existed across time between some weather and herbage-related variables, indicating that the relationships may differ across seasons. Results from the present study, in conjunction with information on the effect of herbage quality and/or mineral concentration on animal production, will be valuable in improving our understanding of weather influences on herbage growth, quality and mineral concentration.

Weather, herbage quality and milk production in pastoral systems. 2. Temporal patterns and intra-relationships in herbage quality and mineral concentration parameters

2009 ◽  
Vol 49 (3) ◽  
pp. 200 ◽  
Author(s):  
J. R. Roche ◽  
L. R. Turner ◽  
J. M. Lee ◽  
D. C. Edmeades ◽  
D. J. Donaghy ◽  
...  
Keyword(s):  
Temporal Trends ◽  
Quality Characteristics ◽  
Animal Production ◽  
Water Soluble ◽  
Magnesium Concentration ◽  
Mineral Concentration ◽  
Substantial Impact ◽  
Herbage Quality ◽  
Metabolisable Energy ◽  
Mineral Concentrations

Prevailing weather conditions influence herbage growth and quality, and therefore may have a substantial impact on animal production. Before investigating relationships between weather factors, herbage quality, and animal production, it is beneficial to first quantify temporal trends in herbage quality characteristics and mineral concentrations. The objective of the present study was to investigate the existence of temporal trends in herbage quality characteristics and mineral concentrations, and to quantify the intra-dependency among these variables. Weekly herbage quality and mineral concentration data from a research farm were collected from 1995 to 2001, inclusive. Fitted sinusoidal functions demonstrated cyclic temporal trends across herbage quality variables, but there was little cyclic temporal variation in the majority of herbage mineral concentration variables. The repeatability of herbage quality measurements was low to moderate (22% for ether extract to 54% for metabolisable energy). Linear relationships were observed within all herbage quality variables and herbage mineral concentration variables. Neutral detergent fibre and acid detergent fibre concentrations were strongly positively correlated with each other (r = 0.87), and negatively correlated with herbage digestibility (r = –0.64 and –0.74, respectively), water-soluble carbohydrate concentration (r = –0.52 and –0.68, respectively) and metabolisable energy content (r = –0.60 and –0.75, respectively). The absolute correlations among most herbage minerals were poor (r <0.30). However, magnesium concentration was positively correlated with calcium (r = 0.54), copper (r = 0.56), and manganese (r = 0.37) concentrations, and negatively correlated with zinc (r = –0.56) concentration. Further investigation is required into the relationships between temporal weather and herbage quality trends, and their impact on animal production.

scholarly journals PENGARUH CUACA TERHADAP RETURN SAHAM DI BURSA EFEK INDONESIA

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Yulis Maulida Berniz ◽  
Cici Widowati
Keyword(s):  
Regression Analysis ◽  
Stock Returns ◽  
Analytical Method ◽  
Data Center ◽  
Stock Exchange ◽  
Weather Data ◽  
Weather Factors ◽  
Daily Weather ◽  
Daily Data ◽  
Daily Weather Data

Weather factors that significantly affect stock returns on the Indonesia Stock Exchange weather hypothesis is positive and significant effect on stock returns in the Indonesia Stock Exchange. Research using daily data in the form of stock returns obtained from the data center capital markets and daily weather data obtained from the Meteorology and Geophysics Agency (BMG) in Jakarta in the form of ordinal levels. The analytical method used in this research is regression analysis linier.

scholarly journals The influence of seasonality and weather changes on premature birth incidence

2017 ◽  
Vol 90 (3) ◽  
pp. 273-278 ◽  
Author(s):  
Daniel Muresan ◽  
Adelina Staicu ◽  
Gabriela Zaharie ◽  
Claudiu Marginean ◽  
Ioana Cristina Rotar
Keyword(s):  
Preterm Birth ◽  
Gestational Age ◽  
Meteorological Factors ◽  
Weather Data ◽  
Premature Births ◽  
Weather Factors ◽  
Positive Correlation ◽  
Human Pregnancy ◽  
Daily Weather Data ◽  
Moderate Positive Correlation

Background. Although the effects of meteorological factors on the general population health  are widely documented, little is known about their influence upon human pregnancy and birth. The present study aim to analyze the influence of the atmospheric conditions upon premature births.Method. One hundred and eight nine cases of premature births were included in the study with a gestational age between 24 to 37 weeks of amenorrhea. Cases with antepartum fetal death and those with uncertain gestational age have been excluded. Daily weather data were obtained using http://www.wunderground.com site.A Pearson's product-moment correlation was run to assess the relationship between weekly preterm birth incidence and the total number of premature births and the mean maximum and minimum temperature (Tmax, Tmin), maximum and minimum average humidity (Umax, Umin), maximum and minimum atmospheric pressure mean (P max, P min), average wind speed and average quantity precipitations, calculated for one calendar week.Results. Approximately 7.7% of all births during the study period occurred before 37 weeks of gestation, the main reason for hospitalization being premature rupture of membranes (45%). The analysis revealed a moderate positive correlation between weekly preterm birth incidence and the average temperature ( r = 0.306, n = 52, p = 0.027) and a moderate positive correlation between weekly preterm birth incidence and temperature variation (r = 0.307, n = 52, p =0.007). Our study found no significant statistic correlation between the humidity variation, pressure variation, and wind speed.Conclusions. The incidence of premature births can be influenced by variations of specific weather factors, especially during the weeks characterized by large fluctuations in temperature. The results obtained might inspire the construction of multicenter studies to investigate thorough the adverse effects of some meteorological factors that can influence the outcomes of human pregnancy.

scholarly journals An Empirical Note on Delhi Weather Effects in the Indian Stock Market

2019 ◽  
Vol 8 (4) ◽  
pp. 1203-1208
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Weather Data ◽  
Weather Factors ◽  
Stock Market Returns ◽  
Weather Factor ◽  
Stock Indices ◽  
Daily Weather Data ◽  
Empirical Note ◽  
Indian Stock Market

This research paper investigates the dynamic linkage, between three weather factors and two top stock Indices in India, namely, BSE SENSEX and NSE NIFTY. In order to study the weather factor on stock indices, daily weather data of Delhi and daily closing stock price of BSE SENSEX and NSE NIFTY, from January 1st 2001 to 31st December 2017, were collected and analyzed. The study found that the Delhi weather namely humidity influence BSE Sensex returns. The investing community may note the findings, for making intelligent investment decisions. The findings would be useful to investors, speculators and officials managing the Indian Securities Exchanges. This is the first empirical study testing the relationship between stock market returns and weather factors in the City of Delhi in India

Climatologically Aided Mapping of Daily Precipitation and Temperature

2005 ◽  
Vol 44 (10) ◽  
pp. 1501-1510 ◽  
Author(s):  
Richard D. Hunter ◽  
Ross K. Meentemeyer
Keyword(s):  
Daily Precipitation ◽  
Accuracy Assessment ◽  
Base Station ◽  
Weather Data ◽  
Validation Dataset ◽  
Weather Variables ◽  
Daily Weather ◽  
Daily Weather Data ◽  
Climate Maps ◽  
Precipitation And Temperature

Abstract Accurately mapped meteorological data are an essential component for hydrologic and ecological research conducted at broad scales. A simple yet effective method for mapping daily weather conditions across heterogeneous landscapes is described and assessed. Daily weather data recorded at point locations are integrated with long-term-average climate maps to reconstruct spatially explicit estimates of daily precipitation and temperature extrema. The method uses ordinary kriging to interpolate base station data spatially into fields of approximately 2-km grain size. The fields are subsequently adjusted by 30-yr-average climate maps [Parameter-Elevation Regression on Independent Slopes Model (PRISM)], which incorporate adiabatic lapse rates, orographic effects, coastal proximity, and other environmental factors. The accuracy assessment evaluated an interpolation-only approach and the new method by comparing predicted and observed values from an independent validation dataset. The results of the accuracy assessment are compared for a 24-yr period for California. For all three weather variables, mean absolute errors (MAE) of the climate-imprint method were considerably smaller than those of the interpolation-only approach. MAE for predicted daily precipitation was ±2.5 mm, with a bias of +0.01. MAE for predicted daily minimum and maximum temperatures were ±1.7° and ±2.0°C, respectively, with corresponding biases of −0.41° and −0.38°C. MAE differed seasonally for all three weather variables, but the method was stable despite variation in the number of base stations available for each day.

Lightning and fire weather in eastern coastal fynbos shrublands: seasonality and long-term trends

2013 ◽  
Vol 22 (3) ◽  
pp. 288 ◽  
Author(s):  
Tineke Kraaij ◽  
Richard M. Cowling ◽  
Brian W. van Wilgen
Keyword(s):  
Fire Regime ◽  
Temporal Trends ◽  
Fire Danger ◽  
Weather Data ◽  
Lightning Strike ◽  
The South ◽  
South Eastern ◽  
Daily Weather Data ◽  
Cloud To Ground Lightning ◽  
Season Of Burn

Daily weather data (since 1939) from four localities in the south-eastern, coastal part of the Cape Floral Kingdom (‘south-eastern-CFK’) were used to calculate daily fire danger indices (FDIs). Cloud-to-ground lightning strike distributions (2006–10) were explored for geographical and temporal trends. Low or moderate fire danger conditions were the norm year round, and even large fires occurred under these conditions. Lightning occurred throughout the landscape at fairly low densities (mean = 0.4 strikes km–2 year–1) and in all seasons, increasing somewhat during summer. Lightning presence increased with increasing rainfall, relative humidity, temperature and wind speed. Lightning seasonality in the south-eastern-CFK did not differ from that in the south-western-CFK. Our results provide evidence of a largely aseasonal fire regime in eastern coastal fynbos shrublands: FDIs peaked in winter (due to low rainfall and hot, dry katabatic winds) but were not associated with a winter fire regime; lightning and the co-occurrence of lightning and elevated FDIs were aseasonal and were correlated with the incidence of lightning-ignited fires throughout the year. The implication for management is that season of burn is largely unimportant. Mean annual FDI increased significantly over the study period, a trend which is likely to manifest in increased frequency and severity of fire, some of which has already been observed.

scholarly journals PENGARUH CUACA TERHADAP RETURN SAHAM DI BURSA EFEK INDONESIA

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Yulis Maulida Berniz ◽  
Cici Widowati
Keyword(s):  
Regression Analysis ◽  
Stock Returns ◽  
Analytical Method ◽  
Data Center ◽  
Stock Exchange ◽  
Weather Data ◽  
Weather Factors ◽  
Daily Weather ◽  
Daily Data ◽  
Daily Weather Data

Weather factors that significantly affect stock returns on the Indonesia Stock Exchange weather hypothesis is positive and significant effect on stock returns in the Indonesia Stock Exchange. Research using daily data in the form of stock returns obtained from the data center capital markets and daily weather data obtained from the Meteorology and Geophysics Agency (BMG) in Jakarta in the form of ordinal levels. The analytical method used in this research is regression analysis linier.

scholarly journals Distribution and incidence of viruses in Irish seed potato crops

2015 ◽  
Vol 54 (2) ◽  
pp. 98-106 ◽  
Author(s):  
F. Hutton ◽  
J.H. Spink ◽  
D. Griffin ◽  
S. Kildea ◽  
D. Bonner ◽  
...  
Keyword(s):  
Potato Virus ◽  
Significant Proportion ◽  
Potato Production ◽  
Weather Data ◽  
Management Approach ◽  
Potato Seed ◽  
Virus Diseases ◽  
Aphid Control ◽  
Daily Weather Data ◽  
Potato Crops

Abstract Virus diseases are of key importance in potato production and in particular for the production of disease-free potato seed. However, there is little known about the frequency and distribution of potato virus diseases in Ireland. Despite a large number of samples being tested each year, the data has never been collated either within or across years. Information from all known potato virus testing carried out in the years 2006–2012 by the Department of Agriculture Food and Marine was collated to give an indication of the distribution and incidence of potato virus in Ireland. It was found that there was significant variation between regions, varieties, years and seed classes. A definition of daily weather data suitable for aphid flight was developed, which accounted for a significant proportion of the variation in virus incidence between years. This use of weather data to predict virus risk could be developed to form the basis of an integrated pest management approach for aphid control in Irish potato crops.

Exploring Weather Data to Predict Activity Attendance in Event-based Social Network

2021 ◽  
Vol 15 (2) ◽  
pp. 1-25
Author(s):  
Jifeng Zhang ◽  
Wenjun Jiang ◽  
Jinrui Zhang ◽  
Jie Wu ◽  
Guojun Wang
Keyword(s):  
Real Life ◽  
Prediction Performance ◽  
Weather Data ◽  
Gradient Boosting ◽  
User Interest ◽  
Weather Factors ◽  
Event Duration ◽  
Outdoor Activities ◽  
Weather Factor ◽  
Event Based

Event-based social networks (EBSNs) connect online and offline lives. They allow online users with similar interests to get together in real life. Attendance prediction for activities in EBSNs has attracted a lot of attention and several factors have been studied. However, the prediction accuracy is not very good for some special activities, such as outdoor activities. Moreover, a very important factor, the weather, has not been well exploited. In this work, we strive to understand how the weather factor impacts activity attendance, and we explore it to improve attendance prediction from the organizer’s view. First, we classify activities into two categories: the outdoor and the indoor activities. We study the different ways that weather factors may impact these two kinds of activities. We also introduce a new factor of event duration. By integrating the above factors with user interest and user-event distance, we build a model of attendance prediction with the weather named GBT-W , based on the Gradient Boosting Tree. Furthermore, we develop a platform to help event organizers estimate the possible number of activity attendance with different settings (e.g., different weather, location) to effectively plan their events. We conduct extensive experiments, and the results show that our method has a better prediction performance on both the outdoor and the indoor activities, which validates the reasonability of considering weather and duration.

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