scholarly journals Development and Evaluation of Two Pecan Scab Prediction Models

Plant Disease ◽  
2012 ◽  
Vol 96 (9) ◽  
pp. 1358-1364 ◽  
Author(s):  
A. F. Payne ◽  
D. L. Smith
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Disease Severity ◽  
Model Building ◽  
Prediction Models ◽  
Correlated Data ◽  
Dew Point ◽  
Total Solar Radiation ◽  
Rain Event ◽  
Pecan Scab

Fusicladium effusum (syn. Cladosporium caryigenum), causal agent of pecan scab, is the most economically important pathogen of pecan (Carya illinoinensis). A weather-based advisory is currently used in Oklahoma to assess the need for fungicide application and requires the accumulation of scab hours. A scab hour is defined as an hour with average temperature ≥21.1°C and relative humidity ≥90%. To assess the validity of the thresholds in the advisory, repeated ratings of disease severity were taken on fruit at five locations during the 1994–96 and 2009–10 growing seasons, resulting in a total of eight site years. Hourly weather variables were also examined, including temperature, relative humidity, dew point, dew point depression, total solar radiation, and total rainfall. Rain and disease severity were converted to binomial variables where a rain event (≥2.5 mm) and disease severity (≥25%) were coded as 1 and all other events as 0. Logistic regression models adjusted for correlated data were developed using generalized estimating equations. Two models were developed: a temperature/relative humidity model and a dew point/dew point depression model. For the temperature/relative humidity model, the best fitting model included all main effects. Using this model, validation exercises assuming no rain and total solar radiation of 22.5 MJ m–2 resulted in a 0.45 probability of pecan scab development when the temperature was 21°C and relative humidity was 90%. Findings of this model were further validated during field studies that evaluated different combinations of temperature and relative humidity thresholds for scheduling fungicide applications. These analyses indicated that the current thresholds of temperature and relative humidity are viable, but a modification of the relative humidity component should be considered. For the dew point/dew point depression model, a reduced model, including dew point, dew point depression, and the binomial rain variable, was considered adequate for explaining scab events, which suggests that future model building to describe pecan scab epidemics should include dew point, dew point depression, rain, and total solar radiation as independent variables. This article originally appeared in the January issue, Volume 96, pages 117-123. It was changed to correct errors in a measurement conversion that appeared throughout.

scholarly journals Development and Evaluation of Two Pecan Scab Prediction Models

Plant Disease ◽  
2012 ◽  
Vol 96 (1) ◽  
pp. 117-123 ◽  
Author(s):  
A. F. Payne ◽  
D. L. Smith
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Disease Severity ◽  
Model Building ◽  
Prediction Models ◽  
Correlated Data ◽  
Dew Point ◽  
Total Solar Radiation ◽  
Rain Event ◽  
Pecan Scab

Fusicladium effusum (syn. Cladosporium caryigenum), causal agent of pecan scab, is the most economically important pathogen of pecan (Carya illinoinensis). A weather-based advisory is currently used in Oklahoma to assess the need for fungicide application and requires the accumulation of scab hours. A scab hour is defined as an hour of average temperature and relative humidity ≥ 21.1°C and 90%, respectively. To assess the validity of the thresholds in the advisory, repeated ratings of disease severity were taken on fruit each year during the 1994–96 and 2009–10 growing seasons. Hourly weather variables were also examined, including temperature, relative humidity, dew point, dew point depression, total solar radiation, and total rainfall. Rain and disease severity were converted to binomial variables where a rain event (≥2.5mm) and disease severity (≥25%) were coded as 1 and all other events as 0. Logistic regression models adjusted for correlated data were developed using generalized estimating equations. Two models were developed: a temperature/relative humidity model and a dew point/dew point depression model. For the temperature/relative humidity model, the best fitting model included all main effects. Using this model, validation exercises assuming no rain and total solar radiation of 22.5 MJ m–2 resulted in a 0.62 probability of pecan scab development when the temperature was 21°C and relative humidity was 90%. Findings of this model were further validated during field studies that evaluated different combinations of temperature and relative humidity thresholds for scheduling fungicide applications. These analyses indicated that the current thresholds of temperature and relative humidity are viable but a modification of the relative humidity component should be considered. For the dew point/dew point depression model, a reduced model, including dew point, dew point depression, and the binomial rain variable, was considered adequate for explaining scab events, which suggests that future model building to describe pecan scab epidemics should include dew point, dew point depression, rain, and total solar radiation as independent variables.

scholarly journals Solar Radiation, Air Temperature, Relative Humidity, and Dew Point Study: Damak, Jhapa, Nepal

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Arun Kumar Shrestha ◽  
Arati Thapa ◽  
Hima Gautam
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Air Temperature ◽  
Gaussian Function ◽  
Great Influence ◽  
Dew Point ◽  
Upward Trend ◽  
Summer Maximum ◽  
Online Calculator ◽  
Climatic Phenomenon

Monitoring and prediction of the climatic phenomenon are of keen interest in recent years because it has great influence in the lives of people and their environments. This paper is aimed at reporting the variation of daily and monthly solar radiation, air temperature, relative humidity (RH), and dew point over the year of 2013 based on the data obtained from the weather station situated in Damak, Nepal. The result shows that on a clear day, the variation of solar radiation and RH follows the Gaussian function in which the first one has an upward trend and the second one has a downward trend. However, the change in air temperature satisfies the sine function. The dew point temperature shows somewhat complex behavior. Monthly variation of solar radiation, air temperature, and dew point shows a similar pattern, lower at winter and higher in summer. Maximum solar radiation (331 Wm-2) was observed in May and minimum (170 Wm-2) in December. Air temperature and dew point had the highest value from June to September nearly at 29°C and 25°C, respectively. The lowest value of the relative humidity (55.4%) in April indicates the driest month of the year. Dew point was also calculated from the actual readings of air temperature and relative humidity using the online calculator, and the calculated value showed the exact linear relationship with the observed value. The diurnal and nocturnal temperature of each month showed that temperature difference was relatively lower (less than 10°C) at summer rather than in winter.

scholarly journals Influence of Sunshine Hour and Relative Humidity on Total Solar Radiation at Nepalganj, Nepal

Patan Pragya ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 97-104
Author(s):  
Usha Joshi ◽  
P. M. Shrestha ◽  
I. B. Karki ◽  
N. P. Chapagain ◽  
K. N. Poudyal
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Meteorological Parameters ◽  
Clean Energy ◽  
Empirical Models ◽  
Energy Resources ◽  
Total Solar Radiation ◽  
Sunshine Hour ◽  
Empirical Constants ◽  
Better Than

The solar energy is the abundantly available free and clean energy resources in Nepal. There are more than 300 sunny days because of Nepal lies in solar zone in a global map. The total solar radiation was measured by using CMP6 pyranometer at Nepalgunj (lat.:28.10oN, long.: 81.67oEand Alt. 165.0masl). The main objective of this study is to select the better empirical model and its empirical constants for the prediction of TSR for the year come. In this research, six different empirical models and meteorological parameters are utilized in the presence of regression technique for the years 2011 and 2012. Finally the different empirical constants are found. After the error analysis, the Swarthman-Oguniade model is found to perform better than others models. So the empirical constants of this model is utilized to predict the TSR of similar geographical sites of Nepal.

scholarly journals Climate and Covid-19 - Upgrade and solar radiation influences based on Brazil cases

2020 ◽  
Author(s):  
Francisco Mendonça ◽  
Max Anjos ◽  
Erika Collischonn ◽  
Pedro Murara ◽  
Deise Ely F. ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Meteorological Parameter ◽  
Absolute Humidity ◽  
Climatic Conditions ◽  
Dew Point ◽  
Wind Gust ◽  
Pubmed Database ◽  
Temperature And Humidity ◽  
In The Beginning

Abstract Background COVID-19 has confirmed to be a pandemic with global and historical dimensions in the beginning of the 21st century. Climatic conditions are one of the environmental factors that influence communicable diseases, including viral diseases. Despite promising scientific advances into understanding the interaction between climate and COVID-19, a question remains: How can climate influence the pandemic of COVID-19? Methods It was updated the publications available on the climate and COVID-19 using Scopus, Web of Science, and PubMed database from January 1 to May 20, 2020. Statistical analysis, such normality and multicollinearity tests were performed between number of COVID-19 cases and climato-meteorological parameters (temperature, relative humidity, dew point temperature, atmosphere pressure, wind speed, wind gust, rainfall, and solar radiation, nebulosity and insolation ratio) in six Brazilian cities. Results This review reveals that temperature, relative humidity and absolute humidity alone do not able to explain the exponential number of COVID-19 cases. Most studies showed the SARS-CoV-2 satisfactorily can survive in a large range of temperature and humidity in temperature and tropical- humidity climates. Analyzing other meteorological parameter, insolation ratio that is related to the solar radiation and nebulosity, the results and in accordance with other studies suggest the transmission and contagion by SARS-CoV-2 seem to have been enhanced under from medium to low direct solar radiation and covered skies. Conclusions This study showed that the inclusion of other climatic variables, in addition to temperature and humidity, should guide future ecological models on the relationship between climate and COVID-19, especially the insolation ratio influences on the viral transmission in six Brazilian cities. Our findings may support public policies and coordinated actions to reduce and control of COVID-19.

Interaction of triapenthenol and environmental factors on the growth and flower bud formation of Gardenia jasminoides Ellis

1998 ◽  
Vol 78 (4) ◽  
pp. 635-640 ◽  
Author(s):  
A. Kamoutsis ◽  
A. Chronopoulou-Sereli ◽  
C. Holevas
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Growth Period ◽  
Maximum Temperature ◽  
Total Solar Radiation ◽  
Total Radiation ◽  
Flower Buds ◽  
Flower Bud ◽  
Gardenia Jasminoides ◽  
Gardenia Jasminoides Ellis

The interaction effects between total solar radiation, air temperature and relative humidity with different concentrations of the plant growth regulator triapenthenol (Baronet) on the vegetative growth and the formation of flower buds of potted gardenia (Gardenia jasminoides Ellis) plants were studied in glasshouse experiments.Triapenthenol was applied as a soil drench at concentrations of 0, 70, 140 and 280 mg L–1 to plants under each of three radiation levels of about 250 (P1), 90 (P2) and 25 (P3) Wm−2. It was established that the maximum temperature was the most critical environmental factor to plant development at all radiation levels. Lengths of new lateral shoots after pinching and the number of flower buds/plant were significantly reduced when radiation was reduced and triapenthenol concentrations increased. The interaction between total radiation and triapenthenol concentration significantly affected the number of flower buds/plant. An increase in triapenthenol concentration and a reduction of total radiation caused increased wrinkling of the leaves.In the unshaded plot (P1), the 140 mg L−1 triapenthenol concentration produced high-quality plants that were shorter than the untreated ones with more flower buds/plant during the growth period. In the moderately shaded plot (P2), the best market-quality plants were those treated with 70 mg L−1 triapenthenol. Key words: Gardenia jasminoides, temperature, total solar radiation, triapenthenol, relative humidity

scholarly journals Weather relation of rice-grass pea crop sequence in Indian Sundarbans

2021 ◽  
Vol 22 (2) ◽  
pp. 148-157
Author(s):  
SUKAMAL SARKAR ◽  
ARGHA GHOSH ◽  
KOUSHIK BRAHMACHARI ◽  
KRISHNENDU RAY ◽  
MANOJ KUMAR NANDA ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Grain Yield ◽  
West Bengal ◽  
Prediction Models ◽  
Correlation Study ◽  
Maximum Temperature ◽  
Total Solar Radiation ◽  
Grass Pea ◽  
Flowering Stage ◽  
Sowing Dates

In order to develop weather-based yield prediction models for rice and grass pea in coastal saline zone of West Bengal, the experiments were conducted with rice (cv. CR 1017) and grass pea (cv. Bio L 212) in the rainy and winter seasons, respectively of 2016-17 and 2017-18. Rice was sown in nursery bed on six different dates starting from June 15 to July 19 at weekly interval in both rainy seasons in two different land situations viz. medium upland and medium lowland. Likewise, grass pea was sown on six different dates just before harvesting of rice. It was observed that both early sown rice and grass pea resulted in higher grain yield and took more time to mature under medium lowland situation irrespective of sowing dates. Correlation study revealed that air temperature during sowing to transplanting phase exhibited significant positive correlation with grain of rice in medium upland (Tmax = 0.76**, Tmin = 0.69*) and medium lowland (Tmax = 0.93**, Tmin = 0.81**) situations. On the other hand, maximum temperature and total solar radiation during 100% emergence to 100% flowering stage were negatively associated with the grain yield of grass pea in both medium upland (Tmax = -0.69*, Accumulated solar radiation = -0.73**) and medium lowland (Tmax = -0.74**, Acc. solar radiation = -0.77**) situations. Grain yield of rice and grass pea could be predicted with 94.4% and 87.4% predictability. Pre-harvest forecasting of grain yield was possible with 77.3% for rice and 83.8% for grass pea.

scholarly journals Detrended Multiple Cross-Correlation Coefficient applied to solar radiation, air temperature and relative humidity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrea de Almeida Brito ◽  
Heráclio Alves de Araújo ◽  
Gilney Figueira Zebende
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Solar Energy ◽  
Correlation Coefficient ◽  
Air Temperature ◽  
Cross Correlation ◽  
Global Solar Radiation ◽  
Meteorological Variables ◽  
Hourly Data ◽  
Cross Correlations

AbstractDue to the importance of generating energy sustainably, with the Sun being a large solar power plant for the Earth, we study the cross-correlations between the main meteorological variables (global solar radiation, air temperature, and relative air humidity) from a global cross-correlation perspective to efficiently capture solar energy. This is done initially between pairs of these variables, with the Detrended Cross-Correlation Coefficient, ρDCCA, and subsequently with the recently developed Multiple Detrended Cross-Correlation Coefficient, $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2. We use the hourly data from three meteorological stations of the Brazilian Institute of Meteorology located in the state of Bahia (Brazil). Initially, with the original data, we set up a color map for each variable to show the time dynamics. After, ρDCCA was calculated, thus obtaining a positive value between the global solar radiation and air temperature, and a negative value between the global solar radiation and air relative humidity, for all time scales. Finally, for the first time, was applied $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2 to analyze cross-correlations between three meteorological variables at the same time. On taking the global radiation as the dependent variable, and assuming that $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}={\bf{1}}$$DMCx2=1 (which varies from 0 to 1) is the ideal value for the capture of solar energy, our analysis finds some patterns (differences) involving these meteorological stations with a high intensity of annual solar radiation.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

scholarly journals MRI-based radiomic feature analysis of end-stage liver disease for severity stratification

2021 ◽  
Author(s):  
Jennifer Nitsch ◽  
Jordan Sack ◽  
Michael W. Halle ◽  
Jan H. Moltz ◽  
April Wall ◽  
...  
Keyword(s):  
Disease Severity ◽  
Prediction Models ◽  
Blood Parameters ◽  
Meld Score ◽  
Poor Correlation ◽  
Discrimination Ability ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Academic Center ◽  
End Stage

Abstract Purpose We aimed to develop a predictive model of disease severity for cirrhosis using MRI-derived radiomic features of the liver and spleen and compared it to the existing disease severity metrics of MELD score and clinical decompensation. The MELD score is compiled solely by blood parameters, and so far, it was not investigated if extracted image-based features have the potential to reflect severity to potentially complement the calculated score. Methods This was a retrospective study of eligible patients with cirrhosis ($$n=90$$ n = 90 ) who underwent a contrast-enhanced MR screening protocol for hepatocellular carcinoma (HCC) screening at a tertiary academic center from 2015 to 2018. Radiomic feature analyses were used to train four prediction models for assessing the patient’s condition at time of scan: MELD score, MELD score $$\ge $$ ≥ 9 (median score of the cohort), MELD score $$\ge $$ ≥ 15 (the inflection between the risk and benefit of transplant), and clinical decompensation. Liver and spleen segmentations were used for feature extraction, followed by cross-validated random forest classification. Results Radiomic features of the liver and spleen were most predictive of clinical decompensation (AUC 0.84), which the MELD score could predict with an AUC of 0.78. Using liver or spleen features alone had slightly lower discrimination ability (AUC of 0.82 for liver and AUC of 0.78 for spleen features only), although this was not statistically significant on our cohort. When radiomic prediction models were trained to predict continuous MELD scores, there was poor correlation. When stratifying risk by splitting our cohort at the median MELD 9 or at MELD 15, our models achieved AUCs of 0.78 or 0.66, respectively. Conclusions We demonstrated that MRI-based radiomic features of the liver and spleen have the potential to predict the severity of liver cirrhosis, using decompensation or MELD status as imperfect surrogate measures for disease severity.

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