Impact of weather conditions on variability in sunflower yield over years in eastern parts of Croatia and Hungary

2012 ◽  
Vol 60 (4) ◽  
pp. 397-405 ◽  
Author(s):  
A. Mijić ◽  
I. Liović ◽  
V. Kovačević ◽  
P. Pepó
Keyword(s):  
Meteorological Data ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Vegetation Period ◽  
Soil Conditions ◽  
Field Crops ◽  
Oil Crops ◽  
Air Temperatures ◽  
Important Field ◽  
The Impact

Oil crops constitute the second most important field crops worldwide and are important both in Hungary and Croatia. Among the oil crops, sunflower has a significant role in Hungary (∼550,000 ha) and Croatia (∼30,000 ha). The main aim of this study was to compare sunflower yields and their variation over years (2000–2007) in the eastern parts of Hungary and Croatia, with the emphasis on the impact of rainfall and temperature regime, and using a rain factor (RFm) calculated monthly as the quotient of precipitation (mm) and mean air temperatures (°C). The results showed that the year had a different effect on the yield of sunflower in the different counties of eastern Hungary and Croatia, because of their different soil conditions. The results proved that the highest yields of sunflower (2140–2710 kg ha−1) were obtained in years when the rainfall before and during the vegetation period was 110–130 mm and 350–420 mm, which was very similar to the 30-year mean data (82–108 mm and 305–346 mm, respectively). The strongest correlations (positive and negative r values) between meteorological data and sunflower yields were found in counties with unfavourable soil conditions. In counties with better soil fertility the correlation coefficients were smaller, indicating that better soil conditions can compensate for unfavourable year effects (especially temporary shortage of rainfall or unfavourable rainfall distribution).

scholarly journals Impact of Environmental Conditions and Agronomic Practices on the Prevalence of Fusarium Species Associated with Ear- and Stalk Rot in Maize

Pathogens ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 236 ◽  
Author(s):  
Annette Pfordt ◽  
Lucia Ramos Romero ◽  
Simon Schiwek ◽  
Petr Karlovsky ◽  
Andreas von Tiedemann
Keyword(s):  
Crop Rotation ◽  
Animal Feed ◽  
Meteorological Data ◽  
Fusarium Species ◽  
Weather Conditions ◽  
Vegetation Period ◽  
Temperature And Precipitation ◽  
Local Species ◽  
Local Range ◽  
The Impact

Fusarium species are common pathogens on maize and reduce the product quality through contamination with mycotoxins thus jeopardizing safety of both animal feed and human food products. Monitoring of Fusarium infected maize ears and stalks was conducted in Germany to determine the range of Fusarium species present in the field and to assess the impact of tillage, crop rotation, and weather conditions on the frequency of Fusarium species. From 2016 till 2018, a total of 387 infected ears and 190 stalk segments from 58 locations in Germany were collected. For each sample location, site-specific agronomic data on tillage and previous crops as well as meteorological data on precipitation, air temperature, and relative humidity during the vegetation period were recorded. The most frequent Fusarium species detected in maize ears were Fusarium graminearum, F. verticillioides and F. temperatum, whereas, F. graminearum, F. equiseti, F. culmorum, and F. temperatum were the species prevailing on maize stalks. Differences in the local species composition were found to be primarily associated with weather variations between the years and the microclimate at the different locations. The results indicate that mean temperature and precipitation in July, during flowering, has the strongest impact on the local range of Fusarium spp. on ears, whereas the incidence of Fusarium species on stalks is mostly affected by weather conditions during September. Ploughing significantly reduced the infection with F. graminearum and F. temperatum, while crop rotation exerted only minor effects.

scholarly journals METHODOLOGICAL PROBLEMS OF THE MATHEMATICAL ESTIMATION OF THE IMPACT OF METEOROLOGICAL FACTORS ON ADAPTIVE FUNCTIONS OF THE HUMAN

2019 ◽  
Vol 96 (3) ◽  
pp. 253-257
Author(s):  
Nurlan K. Smagulov ◽  
A. A. Adilbekova
Keyword(s):  
High School ◽  
Mathematical Analysis ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Correlation Coefficients ◽  
Dose Effect ◽  
Output Factors ◽  
The Central Nervous System ◽  
Methodological Problems ◽  
The Impact

The work is dedicated to methodological problems of the mathematical assessment of the impact of meteorological factors on the adaptive function of the teachers of the High School Institutions. Objects of research. Male teachers of the High School Institution, aged of from 24 to 49 years. Meteorological data were evaluated during the investigation of anthropometric indices of height and weight, individual-typological features and the physiological assessment of the central nervous system, cardiovascular system of the High School teachers. Statistical analysis was performed with the use of Statistica 6.0 package and special statistical software. Paired correlation coefficients obtained as a result of calculation were used to estimate the proportion of the influence of input factors (arguments) on the output factors (functions). A mathematical analysis has allowed to reveal leading meteorological factors that cause a certain level of functional exhaustion during the investigated period. The use of a non-linear correlation analysis allowed to enhance significantly the ability for analytical processing of the results, increase of the effectiveness and the possibility of interpreting the interaction of factors in achieving optimal adaptation of teachers in the working process and to identify the role of meteorological factors in this process. Knowledge of leading factors and the percentage of their contribution to the functional state allowed to give the more accurate assessment of stress to the organism in specific circumstances. The ultimate aim of the mathematical analysis should be not only to find the critical value defined the priority factor characterizing the degree of of information load, but the critical combination of all priority factors causing disruption and the beginning of “start-up” adaptation process in the system “dose-effect. “

Medical Emergencies During a Half Marathon Race – The Influence of Weather

2019 ◽  
Vol 40 (05) ◽  
pp. 312-316 ◽  
Author(s):  
Eric Carlström ◽  
Mats Borjesson ◽  
Gunnar Palm ◽  
Amir Khorram-Manesh ◽  
Fredrik Lindberg ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Weather Conditions ◽  
Daily Maximum ◽  
Equivalent Temperature ◽  
Physiological Equivalent Temperature ◽  
Marathon Race ◽  
Air Temperatures ◽  
Medical Emergencies ◽  
Pet Index ◽  
Maximum Air Temperature

AbstractThe aim was to analyze the influence of weather conditions on medical emergencies in a half-marathon, specifically by evaluating its relation to the number of non-finishers, ambulance-required assistances, and collapses in need of ambulance as well as looking at the location of such emergencies on the race course. Seven years of data from the world’s largest half marathon were used. Meteorological data were obtained from a nearby weather station, and the Physiological Equivalent Temperature (PET) index was used as a measure of general weather conditions. Of the 315,919 race starters, 104 runners out of the 140 ambulance-required assistances needed ambulance services due to collapses. Maximum air temperature and PET significantly co-variated with ambulance-required assistances, collapses, and non-finishers (R2=0.65–0.92; p=0.001–0.03). When air temperatures vary between 15–29°C, an increase of 1°C results in an increase of 2.5 (0.008/1000) ambulance-required assistances, 2.5 (0.008/1000) collapses (needing ambulance services), and 107 (0.34/1000) non-finishers. The results also indicate that when the daily maximum PET varies between 18–35°C, an increase of 1°C PET results in an increase of 1.8 collapses (0.006/1000) needing ambulance services and 66 non-finishers (0.21/1000).

Relationship Between Pavement Temperature and Weather Data: Wisconsin Field Study to Verify Superpave Algorithm

1998 ◽  
Vol 1609 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Peter J. Bosscher ◽  
Hussain U. Bahia ◽  
Suwitho Thomas ◽  
Jeffrey S. Russell
Keyword(s):  
Standard Deviation ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Test Site ◽  
Temperature Data ◽  
Pavement Design ◽  
Asphalt Binders ◽  
Weather Data ◽  
Air Temperatures ◽  
Pavement Temperature

Six test sections were constructed on US-53 in Trempealeau County by using different performance-graded asphalt binders to validate the Superpave pavement temperature algorithm and the binder specification limits. Field instrumentation was installed in two of the test sections to monitor the thermal behavior of the pavement as affected by weather. The instrumentation was used specifically to monitor the temperature of the test sections as a function of time and depth from the pavement surface. A meteorological station was assembled at the test site to monitor weather conditions, including air temperature. Details of the instrumentation systems used and analysis of the data collected during the first 22 months of the project are presented. The analysis was focused on development of a statistical model for estimation of low and high pavement temperatures from meteorological data. The model was compared to the Superpave recommended model and to the more recent model recommended by the Long-Term Pavement Performance (LTPP) program. The temperature data analysis indicates a strong agreement between the new model and the LTPP model for the estimation of low pavement design temperature. However, the analysis indicates that the LTPP and Superpave models underestimate the high pavement design temperature at air temperatures higher than 30°C. The temperature data analyses also indicate that there are significant differences between the standard deviation of air temperatures and the standard deviation of the pavement temperatures. These differences raise some questions about the accuracy of the reliability estimates used in the current Superpave recommendations.

scholarly journals Estimation of the impact of short-term fluctuations in inputs on temporally aggregated outputs of process-oriented models

2003 ◽  
Vol 5 (3) ◽  
pp. 169-180 ◽  
Author(s):  
Å. Forsman ◽  
C. Andersson ◽  
A. Grimvall ◽  
M. Hoffmann
Keyword(s):  
Root Zone ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Parametric Models ◽  
Time Lags ◽  
Least Squares Regression ◽  
Arable Soils ◽  
Time Step ◽  
Process Oriented ◽  
The Impact

Process-oriented models driven by highly resolved meteorological inputs and comprising a short internal time step are sometimes used to predict substance fluxes in air, soil and water over fairly long periods of time. To ascertain whether regression-based input–output analyses in such cases can provide adequate parametric models of the impact of daily and monthly fluctuations in inputs on annual outputs, we studied the SOIL/SOILN model of vertical transport of heat, water and nitrogen through arable soils. Annual leaching of nitrate from the root zone was regarded as the response variable, and regressors were selected from among the set of all linear combinations of daily or monthly values of five different meteorological inputs. We found that, although several of the underlying processes described by the SOIL/SOILN model are non-linear, both ordinary and partial least squares regression (OLS and PLS) identified the subsets of input variables with the strongest influence on the model output, and the dominating time lags between model inputs and outputs. Furthermore, highly resolved explanatory variables were a prerequisite for good performance of linear predictors of temporally aggregated outputs and, to discern the full dynamic behaviour of the model, it was necessary to analyse the response to artificially generated daily meteorological data representing a very large number of different weather conditions. PLS had one advantage over OLS: a smooth pattern in the regression coefficients facilitated physical interpretation of the derived impulse–response weights.

ASSESSMENT OF SOYBEAN SELECTION NUMBERS

1970 ◽  
pp. 24-26
Author(s):  
Е. V. Gureeva
Keyword(s):  
Seed Quality ◽  
Weather Conditions ◽  
Vegetation Period ◽  
Climatic Conditions ◽  
High Yield ◽  
Modern World ◽  
Plant Seed ◽  
Oil Crops ◽  
European Part Of Russia ◽  
Seed Growing

In modern world plant growing soybeans are among the most important protein-oil crops and continue to gain popularity among Russian farmers. The total area under soybean in season 17/18 increased to 2.64 million hectares (+ 18% against season 16/17), with an increase of 21% in the European part of Russia, and 1.64 million tons of oilseeds were harvested. To obtain a high yield with good seed quality, it is very important to create very early, highly productive, ecologically adapted varieties for specific soil and climatic conditions. In the conditions of the Institute of Seed Growing and Agrotechnology - the branch of the FBBUU FNAC VIM in 2013-2017. in breeding nurseries an analysis of the variability of quantitative soybean characteristics was carried out. It has been established that such a feature as the duration of the growing season is characterized by weak variability (6.1%). The average variable characteristics include the number of productive nodes on the plant, the mass of seeds from 1 plant and the mass of 1000 seeds. The widest range of variability (27.3-41.8%) is observed in terms of: plant height, number of branches and beans on the plant, seed yield. In our studies, the lowest coefficient of variation (Cv) was found in the George variety - 24.8%. Studies have shown that the yield of seed varieties of varieties over the years ranged from 0.79 to 3.04 t / ha. The evaluation of the soybean breeding material for productivity in different years of research in meteorological conditions showed that the most productive and stable, irrespective of weather conditions, are H 24/11 and H 2/14 varieties with a vegetation period of 102 days.

Regional analysis of winter wheat yields under different ecological conditions in Hungary and Croatia

2011 ◽  
Vol 59 (1) ◽  
pp. 23-33 ◽  
Author(s):  
P. Pepó ◽  
V. Kovačević
Keyword(s):  
Winter Wheat ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Regional Analysis ◽  
Crop Protection ◽  
Weather Conditions ◽  
Water Shortage ◽  
Spring Temperature ◽  
Temperature Regimes ◽  
The Impact

Wheat is the second most important field crop on arable lands in Hungary and Croatia. Yield variations between years are high in both countries. In the short term these variations are mainly the result of the weather parameters specific to individual growing seasons. The aim of this study was to compare variations in winter wheat yields over years in four counties in Hungary and five in Croatia, with the emphasis on the impact of rainfall and mean air temperature regimes. The results showed that rainfall in spring was most decisive for winter wheat yields. The highest winter wheat yields were obtained when the rainfall in the winter half-year ranged from 230–260 mm and the spring rainfall from 180–230 mm. The precipitation in the growing season is much higher in eastern Croatia than in eastern Hungary, so water shortage is a more pronounced environmental problem for wheat in Hungary. This is probably why wheat yields were lower in eastern Hungary than in eastern Croatia in the period tested. Pearson correlation analysis on the yields and meteorological data between 1990 and 2009 revealed a positive correlation between spring rainfall and the yield, and a negative correlation between spring temperature and the yield. The results proved that yields were determined not only by weather conditions, but by many other factors (crop rotation, tillage, fertilization, variety, crop protection, etc.).

scholarly journals Characteristics and Meteorological Factors of Severe Haze Pollution in China

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Chao He ◽  
Song Hong ◽  
Hang Mu ◽  
Peiyue Tu ◽  
Lu Yang ◽  
...  
Keyword(s):  
Fossil Fuels ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Large Contribution ◽  
Low Wind Speed ◽  
Haze Pollution ◽  
Secondary Aerosols ◽  
Pm2.5 Concentration

A severe haze pollution incident caused by unfavorable weather conditions and a northern air mass occurred in eastern, northern, northwestern, and southwestern China from January 15 to January 22, 2018. To comparatively analyze variations in PM2.5 pollution, hourly monitoring data and 24 h meteorological data were collected. Air quality observations revealed large spatiotemporal variation in PM2.5 concentrations in Handan, Zhengzhou, Xi’an, Yuncheng, Chengdu, Xiangyang, and Jinan. The daily mean PM2.5 concentrations ranged from 111.35 to 227.23 μg·m−³, with concentration being highest in Zhengzhou. Hourly mean PM2.5 concentration presented multiple U-shaped curves, with higher values at night and lower values during the day. The ratios of PM2.5 to PM10 were large in target cities and the results of multiscale geographic weighted regression model (MGWR) and Pearson correlation coefficients showed that PM2.5 had a significant positive or negative correlation with PM10, CO, NO2, and SO2. The concentration of PM2.5 was closely related to the combustion of fossil fuels and other organic compounds, indicating the large contribution of secondary aerosols to PM2.5 concentrations. The analysis of meteorological conditions showed that low temperature, low wind speed, and high relative humidity could aggravate the accumulation of regional pollutants in winter. Northwestern trajectory clusters were predominant contributions except in Jinan, and the highest PM2.5 concentrations in target cities were associated with short trajectory clusters in winter. The potential sources calculated by Weight Potential Source Contribution Function (WPSCF) and Weight Concentration-Weighted Trajectory (WCWT) models were similar and the highest values of the WPSCF (>0.5) and the WCWT (>100 μg·m−³) were mainly distributed in densely populated, industrial, arid, and semiarid regions.

scholarly journals Measuring the Impact of Statistical Techniques for Computation of Weighting Factors in Avalanche Forecasting Model

2019 ◽  
Vol 8 (4) ◽  
pp. 886-891
Keyword(s):  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Skill Score ◽  
Forecasting Model ◽  
Avalanche Forecasting ◽  
Weighting Factors ◽  
The Impact

Avalanche forecasting is an important measure required for the safety of the people residing in hilly regions. Snow avalanches are caused due to the changes that occur in the snow and weather conditions. The prominent changes, that cause the variations which further culminate into an avalanche, can be given higher significance in the forecasting model by application of appropriate weights. These weights are decided based on the relation of each weather parameter to snow avalanche occurrence by the forecaster with the help of historical data. A method is proposed in the current work that can help in removing this subjectivity by using correlation coefficients. Present work explores the use of Pearson correlation coefficient, Spearman rank correlation coefficient and Kendall Tau correlation coefficient to obtain the weighting factors for each parameter used for avalanche forecasting. These parameters are further used in the cosine similarity based nearest neighbour model for avalanche forecasting. Bias and Peirce’s Skill Score are performance measures used to evaluate the outcome of the experimental work.

scholarly journals Retrieval of aerosol optical properties over Beijing: a comparison between SKYNET and AERONET

2019 ◽  
Author(s):  
Xianyi Yang ◽  
Huizheng Che ◽  
Hitoshi Irie ◽  
Quanliang Chen ◽  
Ke Gui ◽  
...  
Keyword(s):  
Optical Properties ◽  
Meteorological Data ◽  
Aerosol Particles ◽  
Fine Particulate Matter ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Distribution Patterns ◽  
Single Scattering ◽  
Aerosol Optical Properties ◽  
Pollution Event

Abstract. This study assesses the performance of SKYNET in comparison to AERONET (Aerosol Robotic Network) for retrieving aerosol optical properties (AOPs) in Beijing, China. The results obtained from simultaneous measurements show high correlation coefficients (> 0.994) for aerosol optical depth (AOD) at each wavelength. The highest correlation coefficient for Ångström exponent is 0.825, at 500–870 nm. The single scattering albedo (SSA) of SKYNET is systematically larger than that of AERONET at each wavelength, and adjusting the SVA (solid view angle) and SA (surface albedo) input values can easily affect the value of SKYNET SSA. The volume size distribution patterns derived from the two networks’ instruments are both bimodal, which is typical, while the coarse-mode volume of SKYNET is larger than that of AERONET on average. According to the frequency distribution of aerosol particles, coarser aerosol particles often present in autumn and finer particles usually exist in winter, and there are more absorbent aerosol particles in winter. SKYNET data, combined with meteorological data, CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations) data, backward trajectories, and WPSCF (weighted potential source contribution function) and WCWT (weighted concentrated weighted trajectory) analyses are used to analyze a serious pollution event in winter over Beijing. The results suggest that it was not only affected by local emissions but also by regional transport. The AOPs under three weather conditions (clean, dusty, haze) in Beijing are discussed. The values of AOD on haze days are about 10.3, 10.0, 8.7, 6.3 and 6.2 times larger than those on clean days at 400, 500, 670, 870 and 1020 nm, respectively; and under haze conditions, the PM2.5 (fine particulate matter) is about 7.6 times larger than that under clean conditions. The values of AOD on dusty days are about 7.1, 7.4, 7.0, 5.3 and 5.2 times larger than those on clean days at 400, 500, 670, 870 and 1020 nm, respectively; and under haze conditions, the PM2.5 is about 5.2 times larger than that under clean conditions.

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