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Showing 6 results for Linear Regression
Zeinab Alipoor, Sohrab Mahmodi,
Volume 2, Issue 2 (2-2016)
Volume 2, Issue 2 (2-2016)
Abstract
In order to determinate the cardinal temperatures and investigate the effect of temperature on seed germination and seedling growth of Securigera securidaca, a study was conducted in a completely randomized design with 8 temperature treatments (5, 10, 15, 20, 25, 30, 35 and 40oC) and four replications in research laboratory of faculty of agriculture at the Birjand University in 2013. A two-segmented linear regression model was fitted between germination rate and temperatures to determine cardinal temperatures for germination. Cardinal (minimum, optimum and maximum) temperatures of germination were determined as -1, 22.5 and 40.2oC respectively. Maximum rate and percentage of germination obtained in the range of 20-25oC. Maximum of radicle length and dry weight, maximum of caulicle length and dry weight and maximum of vigor seedling were obtained in 20oC,10-20oC and 5-30oC respectively. Maximum and minimum germination uniformity (GU) was obtained in 25 and 5oC.
Gholamhassan Ranjbar, Hossein Ghadiri,
Volume 3, Issue 2 (2-2017)
Volume 3, Issue 2 (2-2017)
Abstract
A controlled experiment was run to quantify emergence of Kochia indica under different temperature, salinity and seeding depth levels at Yazd National Salinity Research Center in 2013. Treatments were five day/night temperature regimes: 20/10, 25/15, 30/20, 35/25 and 40/30 °C, five salinity levels: 2, 6, 10, 14 and 18 dS m-1, and seeding depth on the surface (0 mm), 5, 10 and 15 mm. Final emergence percentage, emergence rate index and elapsed time (days) to reach 50% of the maximum emergence were measured. The results showed that the highest and lowest final emergence percentages were observed at 25/15°C and 40/30°C day/night, respectively. Final emergence percentages at salinity levels of 6, 10, 14 and 18 dS m-1were, respectively, 9, 22, 36 and 57% lower than 2 dS m-1. Final emergence percentages for 5, 10 and 15 mm seeding depths were, respectively, 30, 44 and 72% lower, as compared with the placement of seeds on the soil surface. Regression analysis showed that final emergence percentage linearly decreased with increase in salinity and seeding depth levels. However, elapsed time (days) to reach 50% of the maximum emergence (T50), increased as salinity and seeding depth increased, so that the highest T50 was obtained for 18 dS m-1 and seeding depth of 15 mm. Increase in salinity and seeding depth was associated with a significant decrease in emergence rate index. In addition, using a logistic regression equation, emergence rate of K. indica was quantified on each day after sowing for each temperature-salinity combination to predict the distribution range of the plant in these situations.
Farnaz Porali, Farshid Ghaderi-Far, Elias Soltani, Mohammad Hadi Palevani,
Volume 5, Issue 2 (3-2019)
Volume 5, Issue 2 (3-2019)
Abstract
Extended abstract
Introduction: Germination speed is one of the most important germination indices, used in most studies to compare the effects of different treatments on seed germination. Researchers use the reverse time up to 50% maximum germination (1/D50) to calculate the germination rate. One of the methods used for calculating the D50 is the utilization of nonlinear regression models such as Logestic, Gompertz, Richard, Weibull and Hill. In addition, for the purpose of calculating this parameter, simple empirical models such as the model presented by Farooq et al. and Ellis and Roberts are used. The question which arises is which of these methods has more precision predicting D50. The purpose of this study was to calculate D50, using different methods in seed germination of cotton.
Material and Methods: In this experiment, cottonseeds were placed at three temperatures of 15, 25 and 40°C with three replications, and germinated seeds were counted daily several times. To calculate D50, several nonlinear regression models including Gompertze, Logestic, Hill (the four-parameter), Richard and Weibull models were used. Moreover, for the purpose of calculating D50, the models presented by Farooq et al. and Ellis and Roberts were used.
Results: The results showed that all nonlinear regression models exhibited suitable fit to germination data. However, logestic, Hill and Weibull showed better predictability of D50, compared with other models. Besides, D50 calculated by the Farooq model was similar to that estimated by nonlinear regression models, whereas D50 estimated by the Ellis and Roberts model was higher than that estimated by other models.
Conclusions: The results of this study showed that both non-linear regression models and the model developed by Farooq could be used to calculate D50 of cottonseed. In general, the results of this study showed that nonlinear regression models could be used to calculate D50. In this research, Logestic, Hill, and Weibull showed good fit for cumulative seed germination data of cotton seeds versus time at different temperatures. These models have coefficients that have a biological concept that includes maximum germination percentage, time to 50% maximum germination and time to start germination. Moreover, when researchers only seek to measure D50 and are not familiar with the statistical software, they can use the empirical formula presented in this research.
Highlights:
Amin Haghighi, Yazdan Izady, Miad Haji Mahmoudi, Seyed Amir Moosavi,
Volume 7, Issue 2 (3-2021)
Volume 7, Issue 2 (3-2021)
Abstract
Extended Abstract
Introduction: Seed germination and seedling emergence depend on the genetics of plant species and are also influenced by environmental factors. Genetics and nutritional status of the maternal plant, maturity stage at a time of harvest, and environmental factors such as temperature, salinity, drought, and soil fertility influence seed germination. Seed vigor as the main parameter of seed quality decreases due to accelerated aging and storage. The objective of this study was to evaluate the response of accelerated aged Chia seed to different levels of salinity stress.
Material and Methods: Two-way factorial experiment with experimental factors, including five levels of seed accelerated aging durations (0, 24, 48, 72, 96 h) and six levels of salinity stress (0, 50, 100, 150, 200, and 250 mM) was arranged based on a complete randomized block design with three replications. The experiment was conducted at seed technology laboratory Khuzestan Agricultural Sciences and Natural Resources, University of Khuzestan, in 2019.
Results: Results of analysis of variance revealed that the effect of seed accelerating aging, salinity stress, and interaction effects of both factors on all measured germination traits were significant (p<0.01). The best pattern of seed germination was evaluated using three-parameter sigmoid models (logistic, Gompertz, and sigmoidal) and two polynomial models (quadratic and cubic), then the performance of all models was compared using (R2adj), root square of the mean (RMSE) and corrected Akaike index (AICc). Results showed that at accelerated aging duration, models' performance to describe Chia seed germination response varied at different levels of salinity stress. At no aging and 72h of accelerated aging treatments, the sigmoidal model exhibited the best fit on final seed germination, whereas for the other levels of accelerated aging, Gompertz exhibited the best fit. Based on the output of the sigmoidal model, for no aging and 72 hours of accelerated aging, 50% of seed germination was declined at 171.7 and 76.9 mM, respectively, and based on the results of the Gompertz model, after 24 and 48 h of accelerated aging, seed germination declined to 50% at 163.8 and 129.6 mM. Results obtained from fitting polynomial models on seed germination showed that the cubic model provides reasonable descriptions for studied traits such as seed vigor.
Conclusion: Chia seed germination was sensitive to salinity and accelerated aging treatments. At no aging condition, Chia seeds tolerate salinity stress up to 200 mM and were able to germinate. By increasing aging durations, seed germination declined dramatically at all salinity levels and after 96 hours of aging, there was no seed germination at 150 mM.
Highlights:
1- The best nonlinear model to study accelerated Chia seed response to salinity stress was selected using the model selection criterion.
2- Chia seed germination threshold to salinity stress was determined for not- aged and aged seeds.
Introduction: Seed germination and seedling emergence depend on the genetics of plant species and are also influenced by environmental factors. Genetics and nutritional status of the maternal plant, maturity stage at a time of harvest, and environmental factors such as temperature, salinity, drought, and soil fertility influence seed germination. Seed vigor as the main parameter of seed quality decreases due to accelerated aging and storage. The objective of this study was to evaluate the response of accelerated aged Chia seed to different levels of salinity stress.
Material and Methods: Two-way factorial experiment with experimental factors, including five levels of seed accelerated aging durations (0, 24, 48, 72, 96 h) and six levels of salinity stress (0, 50, 100, 150, 200, and 250 mM) was arranged based on a complete randomized block design with three replications. The experiment was conducted at seed technology laboratory Khuzestan Agricultural Sciences and Natural Resources, University of Khuzestan, in 2019.
Results: Results of analysis of variance revealed that the effect of seed accelerating aging, salinity stress, and interaction effects of both factors on all measured germination traits were significant (p<0.01). The best pattern of seed germination was evaluated using three-parameter sigmoid models (logistic, Gompertz, and sigmoidal) and two polynomial models (quadratic and cubic), then the performance of all models was compared using (R2adj), root square of the mean (RMSE) and corrected Akaike index (AICc). Results showed that at accelerated aging duration, models' performance to describe Chia seed germination response varied at different levels of salinity stress. At no aging and 72h of accelerated aging treatments, the sigmoidal model exhibited the best fit on final seed germination, whereas for the other levels of accelerated aging, Gompertz exhibited the best fit. Based on the output of the sigmoidal model, for no aging and 72 hours of accelerated aging, 50% of seed germination was declined at 171.7 and 76.9 mM, respectively, and based on the results of the Gompertz model, after 24 and 48 h of accelerated aging, seed germination declined to 50% at 163.8 and 129.6 mM. Results obtained from fitting polynomial models on seed germination showed that the cubic model provides reasonable descriptions for studied traits such as seed vigor.
Conclusion: Chia seed germination was sensitive to salinity and accelerated aging treatments. At no aging condition, Chia seeds tolerate salinity stress up to 200 mM and were able to germinate. By increasing aging durations, seed germination declined dramatically at all salinity levels and after 96 hours of aging, there was no seed germination at 150 mM.
Highlights:
1- The best nonlinear model to study accelerated Chia seed response to salinity stress was selected using the model selection criterion.
2- Chia seed germination threshold to salinity stress was determined for not- aged and aged seeds.
Majid Azimmohseni, Farshid Ghaderi-Far, Mahnaz Khalafi, Hamid Reza Sadeghipour, Marzieh Ghezel,
Volume 9, Issue 1 (9-2022)
Volume 9, Issue 1 (9-2022)
Abstract
Extended abstract
Introduction: Numerous studies are being carried out to reveal the effects of different treatments on the germination of seeds from various plants. The most commonly used method of analysis is the nonlinear regression which estimates germination parameters. Although the nonlinear regression has been performed based on different models, some serious problems in its structure and results motivated researchers to investigate alternative approaches with higher accuracy and precision. The main purpose of the present research is to introduce the alternative parametric time to event model and comparing its reliability to the nonlinear regression in experiments carried out under different conditions.
Materials and Methods: The results of four different experiments were used here including the effect of Potassium cyanide on walnut seed germination, the effect of salinity on wheat seed germination, the effect of water potential on corn seed germination and the effect of temperature on cotton seed germination. The nonlinear regression and time to event methods were applied based on the Gompertz model. The obtained standard errors from the two models were further assessed using the Monte-Carlo method.
Results: Both methods provided well-fitting models according to the MSE and R2 criteria. Although the germination parameters were approximately identical in both models, the standard error of parameters in nonlinear regression was significantly less than those of time to event method except for the experiments in which all tested seeds germinated within the time frame of study so that in the latter case the estimated standard errors in both models were identical. The Monte-Carlo method confirmed the results of the time to event model and reveals the underestimation of the nonlinear regression method in estimating the standard error of parameters.
Conclusions: Generally, the results of this research showed that the time to event model can be trustfully utilized in seed germination studies under different conditions and treatments. This model, not only provides precise estimates of the germination parameters but also provides the precise standard error of parameters that have important roles in making inferences for parameters. The drc package in R software enables researchers to fit the different time to event models.
Highlights:
- Using the time to the event model in estimation of seed germination parameters.
- Comparing the time to event and nonlinear regression methods in different seed germination experiments.
- Using the Monte-Carlo method for investigating the accuracy of results of the used methods.
Mahboubeh Shahbazi, Jafar Asghari, Behnam Kamkar, Edris Taghvaie Salimi,
Volume 10, Issue 2 (2-2024)
Volume 10, Issue 2 (2-2024)
Abstract
Extended abstract
Introduction: The germination process is one of the most critical stages of a plant's growth and determines the success of the emergence of a weed in an agroecosystem because it is the first stage in which the weed competes for a niche. Various environmental factors, including temperature and moisture, affect the germination of weed seeds. Modeling techniques are capable of predicting germination, seedling emergence, and establishment of weed species. The ability to predict weed germination in response to environmental conditions is very effective for the development of control programs. The experiment was conducted to determine the cardinal temperature and evaluate the best model for quantifying the response of the germination rate of Western ragweed weed seeds under different water stress conditions.
Materials and Methods: A factorial experiment was conducted in the form of a completely randomized design in three replications. The investigated factors include temperature with eight levels (5, 10, 15, 20, 25, 30, 35, and 40 C˚) and water potential with six levels (0, -0.3, -0.6, -0.9, -1.2, and -1.5 MPa) on the germination of Western ragweed. In order to quantify the response of Western ragweed germination rate to temperature, three non-linear Dent-like, Beta, and Segmented regression models were used.
Results: The results showed that the effect of temperature, water potential, and their interactions on maximum germination, germination rate, and time required to reach 10, 50, and 90 percent germination were significant. Also, the results showed that by increasing the temperature from 10 to 25 C˚, the percentage and rate of germination increased whereas by increasing water potential, the percentage and rate of germination decreased. In comparing the models, based on RMSE, R2, CV, and coefficients a and b parameters, the Beta model was the most suitable for estimating the temperatures of cardinal Western ragweed. The base, optimum, and ceiling temperatures using the Beta model were 3.88, 25, and 40 C˚, respectively.
Conclusions: The use of the Beta model to quantify the germination response of Western ragweed seeds to different levels of water potential at different temperatures had acceptable results. Therefore, by using the output of these models at different temperatures, it is possible to predict the germination rate at different potentials.
Highlights:
1- Germination cardinal temperatures and the effect of water potential on western ragweed weed were investigated.
2- Estimation of different models to quantify the response of germination rate to temperature and different water potentials.
Introduction: The germination process is one of the most critical stages of a plant's growth and determines the success of the emergence of a weed in an agroecosystem because it is the first stage in which the weed competes for a niche. Various environmental factors, including temperature and moisture, affect the germination of weed seeds. Modeling techniques are capable of predicting germination, seedling emergence, and establishment of weed species. The ability to predict weed germination in response to environmental conditions is very effective for the development of control programs. The experiment was conducted to determine the cardinal temperature and evaluate the best model for quantifying the response of the germination rate of Western ragweed weed seeds under different water stress conditions.
Materials and Methods: A factorial experiment was conducted in the form of a completely randomized design in three replications. The investigated factors include temperature with eight levels (5, 10, 15, 20, 25, 30, 35, and 40 C˚) and water potential with six levels (0, -0.3, -0.6, -0.9, -1.2, and -1.5 MPa) on the germination of Western ragweed. In order to quantify the response of Western ragweed germination rate to temperature, three non-linear Dent-like, Beta, and Segmented regression models were used.
Results: The results showed that the effect of temperature, water potential, and their interactions on maximum germination, germination rate, and time required to reach 10, 50, and 90 percent germination were significant. Also, the results showed that by increasing the temperature from 10 to 25 C˚, the percentage and rate of germination increased whereas by increasing water potential, the percentage and rate of germination decreased. In comparing the models, based on RMSE, R2, CV, and coefficients a and b parameters, the Beta model was the most suitable for estimating the temperatures of cardinal Western ragweed. The base, optimum, and ceiling temperatures using the Beta model were 3.88, 25, and 40 C˚, respectively.
Conclusions: The use of the Beta model to quantify the germination response of Western ragweed seeds to different levels of water potential at different temperatures had acceptable results. Therefore, by using the output of these models at different temperatures, it is possible to predict the germination rate at different potentials.
Highlights:
1- Germination cardinal temperatures and the effect of water potential on western ragweed weed were investigated.
2- Estimation of different models to quantify the response of germination rate to temperature and different water potentials.
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