Weather factors affecting the development of maize from sowing to flowering

1972 ◽  
Vol 78 (2) ◽  
pp. 325-331 ◽  
Author(s):  
M. N. Hough
Keyword(s):  
Soil Moisture ◽  
Correlation Analysis ◽  
Linear Correlation ◽  
Weather Conditions ◽  
Weather Data ◽  
Weather Factors ◽  
Mean Values ◽  
Rate Of Development ◽  
Linear Correlation Analysis ◽  
The Mean

SUMMARYThe phenological development from sowing to flowering of the eaxly maize hybrid INRA 200 is related to the weather conditions. Plot trial data from Wytham, near Oxford, England, and weather information from that and nearby sites formed the basic data.The mean rate of development per day from sowing to emergence is related by linear correlation analysis to the mean values of soil temperature at 5 cm depth and soil moisture deficit. A range of temperature thresholds for emergence development exist, which depend upon the soil moisture, and which differ from the true physiological threshold.Between omergence and flowering the mean rate of development per day is related by linear correlation analysis to mean air temperature, solar radiation and potential transpiration estimated from weather data. All correlations are significant, but the parameters which combine radiation and temperature are statistically better.

scholarly journals Weather conditions during the blossoming period and their effect on the yield of three cultivars of blackcurrant (Ribes nigrum L.)

2009 ◽  
Vol 21 (2) ◽  
pp. 35-42
Author(s):  
Jan Grabowski ◽  
Zdzisław Kawecki ◽  
Anna Bieniek ◽  
Zofia Tomaszewska
Keyword(s):  
Statistical Analysis ◽  
Correlation Analysis ◽  
Air Temperature ◽  
Ground Level ◽  
Weather Conditions ◽  
Ribes Nigrum ◽  
Weather Factors ◽  
Total Rainfall ◽  
Linear Correlation Analysis ◽  
Variable Weather

Abstract This study presents the influence of major weather factors during the blackcurrant blossoming period on the yields of the ‘Ojebyn’, ‘Titania’ and ‘Roodknop’ cultivars, cultivated in Warmia (Olsztyn). The study was conducted for five years (2003-2007). Among the factors analysed were the currant yield, the duration of the blossoming period, average daily air temperature, the number of days with frost at the height of 2 m and at the ground level, total rainfall during the blossoming period and the number of days with rainfall during the blossoming period. The statistical analysis of particular weather factors has shown that the number of days with frost contributed significantly to the yield of the fruit. A linear correlation analysis has shown that the yield of the three examined cultivars of blackcurrant depended on variable weather conditions during the blossoming period. The yield of the ‘Ojebyn’ cultivar was significantly correlated with the duration of the blossoming period, the number of days with frost and the number of days with rainfall. A significant correlation in the ‘Titania’ cultivar was found with the number of days with frost and with the total amount of rainfall during the blossoming period. The fruit yield from the ‘Roodknop’ cultivar in the five-year period under study depended only on one variable: the duration of the blossoming period. The highest yield was obtained from the ‘Titania’ cultivar.

scholarly journals Pollination success and full seed percentage in European larch (Larix decidua MILL.)

2019 ◽  
Vol 48 (No. 6) ◽  
pp. 271-280 ◽  
Author(s):  
B. Slobodník
Keyword(s):  
Correlation Analysis ◽  
Correlation Coefficients ◽  
Critical Value ◽  
Larix Decidua ◽  
European Larch ◽  
Seed Formation ◽  
Pollination Success ◽  
Empty Seed ◽  
Linear Correlation Analysis ◽  
The Mean

A relationship between the success of pollination and the percentage of full seeds of European larch (Larix decidua MILL.) was studied using several models of non-linear correlation analysis. Although the proportion of pollinated ovules was high in most cases (especially in the middle parts of open-pollinated conelets), the mean percentage of full seeds was extraordinarily low (after the controlled self-pollination even close to zero) and most of the calculated correlation coefficients are lower than the corresponding critical value. This fact gives an evidence for the strong effect of additional important factors causing the empty seed formation in Larix even after the successful pollination of ovules.

Multiple Linear Correlation Analysis of Daily Reference Evapotranspiration

2013 ◽  
Vol 27 (5) ◽  
pp. 1489-1500 ◽  
Author(s):  
Mallikarjuna Perugu ◽  
Aruna Jyothy Singam ◽  
Chandra Sekhar Reddy Kamasani
Keyword(s):  
Correlation Analysis ◽  
Linear Correlation ◽  
Reference Evapotranspiration ◽  
Linear Correlation Analysis

scholarly journals Non-linear functional brain co-activations in short-term memory distortion tasks

2021 ◽  
Author(s):  
Anna Ceglarek ◽  
Jeremi K. Ochab ◽  
Ignacio Cifre ◽  
Magdalena Fąfrowicz ◽  
Barbara Sikora-Wachowicz ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Cognitive Performance ◽  
Linear Correlation ◽  
Short Term Memory ◽  
Time Of Day ◽  
Short Term ◽  
Term Memory ◽  
Non Linear ◽  
Linear Correlation Analysis ◽  
Linear Correlations

AbstractRecent works shed light on the neural correlates of true and false recognition and the influence of time of day on cognitive performance. The current study aimed to investigate the modulation of the false memory formation by the time of day using a non-linear correlation analysis originally designed for fMRI resting-state data. Fifty-four young and healthy participants (32 females, mean age: 24.17 y.o., SD: 3.56 y.o.) performed in MR scanner the modified Deese-Roediger-McDermott paradigm in short-term memory during one session in the morning and another in the evening. Subjects’ responses were modeled with a general linear model, which includes as a predictor the non-linear correlations of regional BOLD activity with the stimuli, separately for encoding and retrieval phases. The results show the dependence of the non-linear correlations measures with the time of day and the type of the probe. In addition, the results indicate differences in the correlations measures with hippocampal regions between positive and lure probes. Besides confirming previous results on the influence of time-of-day on cognitive performance, the study demonstrates the effectiveness of the non-linear correlation analysis method for the characterization of fMRI task paradigms.

An Efficient Algorithm for Power Prediction in PV Generation System

2020 ◽  
Vol 9 (2) ◽  
pp. 207-216
Author(s):  
Qais Alsafasfeh
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Weather Conditions ◽  
Cuckoo Search ◽  
Wavelet Neural Network ◽  
Mean Values ◽  
Neural Network Algorithm ◽  
The Mean ◽  
Pv Generation ◽  
Error Indicators

Aiming at the existing photovoltaic power generation prediction methods, the modeling is complicated, the prediction accuracy is low, and it is difficult to meet the actual needs. Based on the improvement of the traditional wavelet neural network, a dual-mode cuckoo search wavelet neural network algorithm combined prediction method is proposed, which takes into account the extraction of chaotic features of surface solar radiation and photovoltaic output power. The proposed algorithm first reconstructs the chaotic phase space of the hidden information of each influencing factor in the data history of PV generation and according to the correlation analysis, the solar radiation is utilized as additional input. Next, the proposed algorithm overcomes the limitations of the cuckoo search algorithm such as the sensitivity to the initial value and searchability and convergence speed by dual-mode cuckoo search wavelet neural network algorithm. Lastly, a prediction model of the proposed algorithm is proposed and the prediction analysis is performed under different weather conditions. Simulation results show that the proposed algorithm shows better performance than the existing algorithms under different weather conditions. Under various weather conditions, the mean values of TIC, EMAE and ENRMSE error indicators of the proposed forecasting algorithm were reduced by 43.70%, 45.75%, and 45.41%, respectively. Compared with the Chaos-WNN prediction method, the prediction performance has been further improved under various weather conditions and the mean values of TIC, EMAE and ENRMSE error indicators have been reduced by 25.55%, 27.26%, and 36.83%, respectively. ©2020. CBIORE-IJRED. All rights reserved

scholarly journals An Efficient Intelligent Power Detection Method for Photovoltaic System

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Detection Method ◽  
Peak Load ◽  
Weather Conditions ◽  
Photovoltaic System ◽  
Weather Data ◽  
Weather Factors ◽  
Pv System ◽  
Intelligent Technique ◽  
Highly Nonlinear ◽  
Validation Set

Jordan has experienced a significant increase in both peak load and annual electricity demand within the last decade due to the growth of the economy and population. Photovoltaic (PV) system is one of the most popular renewable energy source in Jordan. PV system is highly nonlinear with unpredictable behavior since it is always subject to many external factors such as severe weather conditions, irradiance level, sheds, temperature, etc. This makes it difficult to maintain maximum power production around its operation ranges. In this paper, an intelligent technique is used to predict and identify the working ability of the PV system under different weather factors in Tafila Technical University (TTU) in Jordan. It helps in optimizing power productions for different operation points. The PV system in Tafila with size 1 MWp PV generated 5.4 GWh since 2017. It saves about € 1.5 million in three years. A real power data from the PV system and a weather data from world weather online site of TTU location are used in this study. Decision tree technique is employed to identify the relation between the output power and weather factors. The results show that the system accuracy is 82.01% during the training phase and 93.425 % on the validation set.

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