Cardinal temperatures and thermal time required foremergence of lenti (Lens culinaris Medik)

2017 ◽  
Author(s):  
Ali reza Safahani ◽  
Behnam Kamakar ◽  
Amir Nabizadeh
Keyword(s):  
Lens Culinaris ◽  
Seedling Emergence ◽  
Information Criterion ◽  
Optimization Method ◽  
Thermal Time ◽  
Coefficient Of Determination ◽  
Nonlinear Regression Models ◽  
Maximum Temperatures ◽  
Time Required ◽  
Cardinal Temperatures

The present study was performed to compare four nonlinear regression models (segmented, beta, beta modified, and dent-like) to describe the emergence rate–temperature relationships of six lentil (Lens culinaris Medik) cultivars at field experiment with a range of sowing dates, with the aim of identifying the cardinal temperatures and physiological days (i.e., number of days under optimum temperatures) required for seedling emergence. Models and statistical indices were calibrated using an iterative optimization method and their performance was compared by root mean square error (RMSD), coefficient of determination (R2) and corrected Akaike information criterion correction (AIC). The beta model was found to be the best model for predicting the response of lentil emergence to temperature, (R2= 0.99; RMSD= 0.005; AICc= -232.97). Based on the model outputs, the base, optimum, and maximum temperatures of seedling emergence were 4.5, 22.9, and 40 °C, respectively. The Six physiological days (equivalent to a thermal time of 94 °C days) were required from sowing to emergence

Quantitative Description of the Germination of Littleseed Canarygrass (Phalaris minor) in Response to Temperature

Weed Science ◽  
2014 ◽  
Vol 62 (2) ◽  
pp. 250-257 ◽  
Author(s):  
Abolfazl Derakhshan ◽  
Javid Gherekhloo ◽  
Ribas A. Vidal ◽  
Rafael De Prado
Keyword(s):  
Germination Rate ◽  
Quantitative Description ◽  
Absolute Error ◽  
Optimization Method ◽  
Coefficient Of Determination ◽  
Segmented Model ◽  
Nonlinear Regression Models ◽  
Time Required ◽  
Response To Temperature ◽  
Cardinal Temperatures

Littleseed canarygrass is a troublesome grass weed in wheat fields in Iran. Predicting weed emergence dynamics can help farmers more effectively control weeds. In this work, four nonlinear regression models (beta, three-piece segmented, two-piece segmented, and modified Malo's exponential sine) were compared to describe the cardinal temperatures for the germination of littleseed canarygrass. Two replicated experiments were performed with the same temperatures. An iterative optimization method was used to calibrate the models and different statistical indices (mean absolute error [MAE], coefficient of determination [R2], intercept and slope of the regression equation of predicted vs. observed hours to germination) were applied to compare their performance. The three-piece segmented model was the best model to predict the germination rate (R2= 0.99, MAE = 0.20 d, and coefficient of variation 1.01 to 4.06%). Based on the model outputs, the base, the lower optimum, the upper optimum, and the maximum temperatures for the germination of littleseed canarygrass were estimated to be 4.69, 22.60, 29.62, and 38.13 C, respectively. The thermal time required to reach 10, 50, and 90% germination was 31.98, 39.26 and 45.55 degree-days, respectively. The cardinal temperatures depended on the model used for their estimation. Overall, the three-piece segmented model was better suited than the other models to estimate the cardinal temperatures for the germination of littleseed canarygrass.

scholarly journals EVALUATION OF CARDINAL TEMPERATURES AND THERMAL TIME REQUIREMENT FOR GERMINATION OF SCROPHULARIA STRIATA AND TANACETUM POLYCEPHALUM (SCHULTZ BIP. SSP. HETEROPHYLLUM)

2018 ◽  
Vol 78 ◽  
pp. 83-97
Author(s):  
Bahram Karavani ◽  
Reza Tavakkol Afshari ◽  
Nasser Majnoon Hosseini ◽  
Seyed Amir Moosavi ◽  
Hamed Akbari
Keyword(s):  
Medicinal Plants ◽  
Sesquiterpene Lactones ◽  
Germination Rate ◽  
Optimization Method ◽  
Thermal Time ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
Scrophularia Striata ◽  
Maximum Temperatures ◽  
Cardinal Temperatures

Scrophularia striata and Tanacetum polycephalum are important medicinal plants in Iran which are rich inessential oils, bitter substances, and sesquiterpene lactones. The present study was conducted to compare fournon-linear regression models (segmented, beta, beta modified and Dent-like) to describe the germination ratetemperaturerelationships of Scrophularia striata and Tanacetum polycephalum over eight and seven constanttemperatures, respectively, to find cardinal temperatures and thermal time requirements to reach differentgermination percentiles. An iterative optimization method was used to calibrate the models and differentstatistical indices including RMSE, coefficient of determination (R2), and AICc were applied to compare theirperformance. The beta model was found to be the best model to predict germination rate of Scrophulariastriata at D10, D50 and D90 (R2 = 0.96, R2 = 0.97, R2 = 0.95; RMSE = 0.005, 0.001 and 0.001, respectively).According to this model outputs, the base, optimum, and the maximum temperatures for germination wereestimated as 1.21 ± 0.39, 25.91 ± 0.33 and 46.35 ± 4.12 °C, respectively. Also the segmented model wasfound to be the best model to predict germination rate of Tanacetum polycephalum at D10, D50 and D90 (R2= 0.98, R2 = 0.98, R2 = 0.98; RMSE = 0.067, 0.59 and 0.56, respectively). According to the model outputs, thebase, optimum, and the maximum temperatures for germination were estimated as 0.44±1.15, 26.95±0.75 and38.33±0.98 oC, respectively. It seems these two medicinal plants need moderate optimum temperature for seedgermination.

scholarly journals Quantifying cardinal temperatures and thermal time required for germination of Silybum marianum seed

2015 ◽  
Vol 3 (2) ◽  
pp. 145-151 ◽  
Author(s):  
Ghasem Parmoon ◽  
Seyed Amir Moosavi ◽  
Hamed Akbari ◽  
Ali Ebadi
Keyword(s):  
Thermal Time ◽  
Silybum Marianum ◽  
Time Required ◽  
Cardinal Temperatures

scholarly journals Cardinal Temperatures and Thermal Time for Seed Germination of Brunonia australis (Goodeniaceae) and Calandrinia sp. (Portulacaceae)

HortScience ◽  
2011 ◽  
Vol 46 (5) ◽  
pp. 753-758 ◽  
Author(s):  
Robyn L. Cave ◽  
Colin J. Birch ◽  
Graeme L. Hammer ◽  
John E. Erwin ◽  
Margaret E. Johnston
Keyword(s):  
Seed Germination ◽  
Cumulative Distribution Function ◽  
Thermal Time ◽  
Cumulative Distribution ◽  
Maximum Temperature ◽  
Base Temperature ◽  
Percentage Germination ◽  
Maximum Temperatures ◽  
Seed Propagation ◽  
Cardinal Temperatures

Seed germination of Brunonia australis Sm. ex R.Br. and Calandrinia sp. (Mt. Clere: not yet fully classified) was investigated using a thermogradient plate set at different constant temperatures to determine seed propagation requirements of these potential floriculture species. Germination responses were tested at 3, 7, 11, 15, 18, 22, 25, 29, 34, and 38 °C. Germination data were modeled using the cumulative distribution function of the inverse normal, which provides information on lag, rate, and maximum seed germination for each temperature regime. To determine cardinal temperatures, the reciprocal time to median germination (1/t50) and percentage germination per day were calculated and regressed against temperature. Base temperature estimates for B. australis were 4.9 and 5.5 °C and optimum temperatures were 21.4 and 21.9 °C, whereas maximum temperatures were 35.9 and 103.5 °C, with the latter being clearly overestimated using the 1/t50 index. Base temperatures for Calandrinia sp. were 5.8 and 7.9 °C, whereas optimum and maximum temperature estimates of 22.5 and 42.7 °C, respectively, were reported using the percentage germination per day index. Maximum seed germination of 0.8 to 0.9, expressed as the probability of a seed germinating, occurred at 11 to 25 °C for B. australis, whereas maximum germination for Calandrinia sp. was 0.5 to 0.7 at 18 to 25 °C. Thermal time, the accumulation of daily mean temperate above a base temperature, was calculated for different germination percentages. Estimates of thermal time (°Cd) for 50% seed germination were 54 and 90 °Cd for B. australis and Calandrinia sp., respectively.

scholarly journals Productive traits of rye cultivars grown under different sowing seasons

2019 ◽  
Vol 23 (12) ◽  
pp. 937-944
Author(s):  
Jéssica A. Kleinpaul ◽  
Alberto Cargnelutti Filho ◽  
Fernanda Carini ◽  
Rafael V. Pezzini ◽  
Gabriela G. Chaves ◽  
...  
Keyword(s):  
Aerial Part ◽  
Dry Matter ◽  
Nonlinear Models ◽  
Logistic Models ◽  
Information Criterion ◽  
Thermal Time ◽  
Coefficient Of Determination ◽  
Maximum Acceleration ◽  
Intrinsic Nonlinearity ◽  
Productive Traits

ABSTRACT This study aimed to adjust the Gompertz and Logistic nonlinear models for the fresh and dry matter of aerial part and indicate the model that best describes the growth of two rye cultivars in five sowing seasons, as well as to characterize the growth of the cultivars regarding the fresh and dry matter of aerial part. Ten uniformity trials were conducted with the rye crop in 2016. A weekly sampling and evaluation of 10 plants per trial was performed from the time the plants presented one expanded leaf. For each plant, the fresh and dry matter of aerial part were weighed. The Gompertz and Logistic models were adjusted to the accumulated thermal time based on the measures of each trait in each assessment. Also the parameters a, b, and c for each model were estimated and calculated the interval of confidence for each parameter, as well as the inflection points, maximum acceleration, maximum deceleration and asymptotic deceleration. The quality of the model adjustments was verified using the coefficient of determination, Akaike information criterion, and residual standard deviation. The intrinsic nonlinearity and nonlinearity of the parameter effect was quantified. Both models satisfactorily describe the behavior of the fresh and dry matter of aerial part. The Logistic model best describes the growth of rye cultivars. The growth of the cultivars BRS Progresso and Temprano is distinct between sowing seasons. Cultivar BRS Progresso requires a lower thermal time until reaching 50% of its growth when compared to the Temprano cultivar.

scholarly journals Genotypic variation in cardinal temperatures and thermal time for germination and seedling emergence of pigeonpea [Cajanus cajan (L.) Millsp.]

2021 ◽  
pp. 1-12
Author(s):  
Sabampillai Mahendraraj ◽  
Marisa Collins ◽  
Yash Chauhan ◽  
Vincent Mellor ◽  
Rao C.N. Rachaputi
Keyword(s):  
Cajanus Cajan ◽  
Seedling Emergence ◽  
Genotypic Variation ◽  
Thermal Time ◽  
Cardinal Temperatures

scholarly journals Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-55
Author(s):  
Rodgers Makwinja ◽  
Seyoum Mengistou ◽  
Emmanuel Kaunda ◽  
Tena Alemiew ◽  
Titus Bandulo Phiri ◽  
...  
Keyword(s):  
Time Series ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Annual Fish ◽  
Unit Effort ◽  
The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

scholarly journals The use of the nonlinear models in the growth of pears of ‘Shinseiki’ cultivar

2017 ◽  
Vol 48 (1) ◽  
Author(s):  
Thais Destefani Ribeiro ◽  
Taciana Villela Savian ◽  
Tales Jesus Fernandes ◽  
Joel Augusto Muniz
Keyword(s):  
Logistic Model ◽  
Least Squares Method ◽  
Nonlinear Models ◽  
Logistic Models ◽  
Information Criterion ◽  
Fruit Weight ◽  
Coefficient Of Determination ◽  
Pear Fruit ◽  
Asian Pear ◽  
Residual Correlations

ABSTRACT: The goal of this study was to elucidate the growth and development of the Asian pear fruit, on the grounds of length, diameter and fresh weight determined over time, using the non-linear Gompertz and Logistic models. The specifications of the models were assessed utilizing the R statistical software, via the least squares method and iterative Gauss-Newton process (DRAPER & SMITH, 2014). The residual standard deviation, adjusted coefficient of determination and the Akaike information criterion were used to compare the models. The residual correlations, observed in the data for length and diameter, were modeled using the second-order regression process to render the residuals independent. The logistic model was highly suitable in demonstrating the data, revealing the Asian pear fruit growth to be sigmoid in shape, showing remarkable development for three variables. It showed an average of up to 125 days for length and diameter and 140 days for fresh fruit weight, with values of 72mm length, 80mm diameter and 224g heavy fat.

scholarly journals Nonlinear modeling growth body weight of Mangalarga Marchador horses

2017 ◽  
Vol 47 (4) ◽  
Author(s):  
Felipe Amorim Caetano Souza ◽  
Tales Jesus Fernandes ◽  
Raquel Silva de Moura ◽  
Sarah Laguna Conceição Meirelles ◽  
Rafaela Aparecida Ribeiro ◽  
...  
Keyword(s):  
Body Weight ◽  
Linear Models ◽  
Nonlinear Modeling ◽  
Growth Curves ◽  
Live Weight ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Cross Sectional ◽  
Adult Body Weight ◽  
The Cross

ABSTRACT: The analysis of the growth and development of various species has been done using the growth curves of the specific animal based on non-linear models. The objective of the current study was to evaluate the fit of the Brody, Gompertz, Logistic and von Bertalanffy models to the cross-sectional data of the live weight of the MangalargaMarchador horses to identify the best model and make accurate predictions regarding the growth and maturity in the males and females of this breed. The study involved recording the weight of 214 horses, of which 94 were males and 120 were non-pregnant females, between 6 and 153 months of age. The parameters of the model were estimated by employing the method of least squares, using the iteratively regularized Gauss-Newton method and the R software package. Comparison of the models was done based on the following criteria: coefficient of determination (R²); Residual Standard Deviation (RSD); corrected Akaike Information Criterion (AICc). The estimated weight of the adult horses by the models ranged between 431kg and 439kg for males and between 416kg and 420kg for females. The growth curves were studied using the cross-sectional data collection method. For males the von Bertalanffymodel was found to be the most effective in expressing growth, while in females the Brody model was more suitable. The MangalargaMarchador females achieve adult body weight earlier than the males.

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