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.

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. 4. Effects on dairy cattle production

2009 ◽  
Vol 49 (3) ◽  
pp. 222 ◽  
Author(s):  
J. R. Roche ◽  
L. R. Turner ◽  
J. M. Lee ◽  
D. C. Edmeades ◽  
D. J. Donaghy ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Dry Matter ◽  
Protein Concentration ◽  
Milk Protein ◽  
Animal Production ◽  
Dry Matter Intake ◽  
Cattle Production ◽  
Mineral Concentration ◽  
Herbage Quality ◽  
Time Of Year

Prevailing weather conditions are one factor that influences herbage growth and quality, and therefore may have a substantial impact on animal production. The objective of the present study was to quantify the associations between weather, herbage quality and mineral concentration, and animal production. Daily weather data and weekly records of herbage quality and mineral concentration, as well as dairy cattle production, were available from a research farm and nearby weather station across the years 1995 to 2001, inclusive. Animal production variables of interest included individual cow milk production and composition, body condition score, and liveweight, as well as group herbage dry matter intake. Results indicate moderate relationships between some weather- and herbage-related variables and dairy cattle production variables, although most relationships appeared to be an artefact of temporal variation, as evidenced by weakening of correlations following adjustment for animal parity, stage of lactation, and week of the year at calving. Prior to adjustment for the confounding factors, the negative associations between milk yield and all temperature-related variables (r = –0.46 to –0.34) were most notable. Following adjustment for time of year, milk yield became positively associated with sunlight hours (r = 0.14). Negative relationships were demonstrated between temperature-related variables and milk protein concentration (r = –0.08), regardless of time of year. Milk protein concentration was positively associated with herbage metabolisable energy content (r = 0.06), water-soluble carbohydrate (r = 0.11), and organic matter digestibility (r = 0.06) concentrations, and negatively associated with ether extract (r = –0.07), acid detergent fibre (r = –0.06), and neutral detergent fibre (r = –0.05) concentrations. Weather, herbage quality and mineral concentration explained up to 22% more variation in dairy cattle production variables over and above farmlet and time of year, with a greater effect on dry matter intake than the other production parameters.

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.

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.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals A method for simulating risk profiles of wheat yield in data-sparse conditions

2021 ◽  
pp. 1-12
Author(s):  
G. Bracho-Mujica ◽  
P.T. Hayman ◽  
V.O. Sadras ◽  
B. Ostendorf
Keyword(s):  
Reference Site ◽  
Climate Impacts ◽  
Weather Data ◽  
Climate Data ◽  
High Quality ◽  
Risk Profiles ◽  
Record Length ◽  
Daily Weather Data ◽  
Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

scholarly journals Impact of Extreme Weather on Healthcare Utilization by People with HIV in Metropolitan Miami

2021 ◽  
Vol 18 (5) ◽  
pp. 2442
Author(s):  
Daniel Samano ◽  
Shubhayu Saha ◽  
Taylor Corbin Kot ◽  
JoNell E. Potter ◽  
Lunthita M. Duthely
Keyword(s):  
Relative Risk ◽  
The United States ◽  
Extreme Weather ◽  
South Florida ◽  
Heat Index ◽  
Extreme Weather Events ◽  
Weather Data ◽  
Steady Increase ◽  
Daily Weather Data ◽  
The Impact

Extreme weather events (EWE) are expected to increase as climate change intensifies, leaving coastal regions exposed to higher risks. South Florida has the highest HIV infection rate in the United States, and disruptions in clinic utilization due to extreme weather conditions could affect adherence to treatment and increase community transmission. The objective of this study was to identify the association between EWE and HIV-clinic attendance rates at a large academic medical system serving the Miami-Dade communities. The following methods were utilized: (1) Extreme heat index (EHI) and extreme precipitation (EP) were identified using daily observations from 1990–2019 that were collected at the Miami International Airport weather station located 3.6 miles from the studied HIV clinics. Data on hurricanes, coastal storms and flooding were collected from the National Oceanic and Atmospheric Administration Storms Database (NOAA) for Miami-Dade County. (2) An all-HIV clinic registry identified scheduled daily visits during the study period (hurricane seasons from 2017–2019). (3) Daily weather data were linked to the all-HIV clinic registry, where patients’ ‘no-show’ status was the variable of interest. (4) A time-stratified, case crossover model was used to estimate the relative risk of no-show on days with a high heat index, precipitation, and/or an extreme natural event. A total of 26,444 scheduled visits were analyzed during the 383-day study period. A steady increase in the relative risk of ‘no-show’ was observed in successive categories, with a 14% increase observed on days when the heat index was extreme compared to days with a relatively low EHI, 13% on days with EP compared to days with no EP, and 10% higher on days with a reported extreme weather event compared to days without such incident. This study represents a novel approach to improving local understanding of the impacts of EWE on the HIV-population’s utilization of healthcare, particularly when the frequency and intensity of EWE is expected to increase and disproportionately affect vulnerable populations. More studies are needed to understand the impact of EWE on routine outpatient settings.

scholarly journals Predicting emergency department visits in a large teaching hospital

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Nathan Singh Erkamp ◽  
Dirk Hendrikus van Dalen ◽  
Esther de Vries
Keyword(s):  
Emergency Department ◽  
The Netherlands ◽  
Teaching Hospital ◽  
Time Trend ◽  
Prediction Models ◽  
Emergency Department Visits ◽  
Weather Data ◽  
Negative Consequences ◽  
Weather Variables ◽  
Ed Visits

Abstract Background Emergency department (ED) visits show a high volatility over time. Therefore, EDs are likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Previous studies on the predictive value of weather variables on ED visits show conflicting results. Also, no such studies were performed in the Netherlands. Therefore, we evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors. Methods Data on all ED visits from June 2016 until December 31, 2019, were extracted. The 2016–2018 data were used as training set, the 2019 data as test set. Weather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute. Weather observations in proximity of the hospital were used to predict the weather in the hospital’s catchment area by applying the inverse distance weighting interpolation method. The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit. Results The number of daily ED visits shows a positive time trend and a large impact of calendar events (higher on Mondays and Fridays, lower on Saturdays and Sundays, higher at special times such as carnival, lower in holidays falling on Monday through Saturday, and summer vacation). The weather itself was a better predictor than weather volatility, but only showed a small effect; the calendar-only prediction model had very similar coefficients to the calendar+weather model for the days of the week, time trend, and special time periods (both MAPE’s were 8.7%). Conclusions Because of this similar performance, and the inaccuracy caused by weather forecasts, we decided the calendar-only model would be most useful in our hospital; it can probably be transferred for use in EDs of the same size and in a similar region. However, the variability in ED visits is considerable. Therefore, one should always anticipate potential unforeseen spikes and dips in ED visits that are not shown by the model.

scholarly journals The epidemiology of the common cold IV. The effect of weather

1965 ◽  
Vol 63 (3) ◽  
pp. 427-439 ◽  
Author(s):  
O. M. Lidwell ◽  
R. W. Morgan ◽  
R. E. O. Williams
Keyword(s):  
Common Cold ◽  
Partial Correlation ◽  
Water Vapour Pressure ◽  
Outdoor Temperature ◽  
Weather Variables ◽  
Smooth Curves ◽  
Correlation Studies ◽  
The Common ◽  
Expected Values ◽  
Time Of Year

An investigation has been made of the association between weather and the numbers of colds reported on a given day. The seasonal trends were eliminated by working with the differences between the observed values on any day and the expected values derived from smooth curves fitted to the averages for the time of year.Examination of nine weather variables for the day on which the colds were reported and for each of the 29 preceding days showed that only two, mean day temperature and water-vapour pressure at 9 a.m., were significantly correlated with the numbers of colds. Partial correlation studies showed that the strongest association was with lowered mean day temperature between 2 and 4 days before the reported onset of symptoms.Regression analysis demonstrated that the magnitudes of the associations were sufficient to account for the greater part of the seasonal variation in the incidence of the common cold in both London and Newcastle. A small effect of atmospheric pollution appeared in this analysis.These results suggest that some effect of low outdoor temperature promotes transmission of the virus or the development of disease.

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